Sensing, Computing, and Communication for Energy Harvesting IoTs: A Survey
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paper_title: Approaches to Transient Computing for Energy Harvesting Systems: A Quantitative Evaluation

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Systems operating from harvested sources typically integrate batteries or supercapacitors to smooth out rapid changes in harvester output. However, such energy storage devices require time for charging and increase the size, mass and cost of the system. A recent approach to address this is to power systems directly from the harvester output, termed transient computing. To solve the problem of having to restart computation from the start due to power-cycles, a number of techniques have been proposed to deal with transient power sources. In this paper, we quantitatively evaluate three state-of-the-art approaches on a Texas Instruments MSP430 microcontroller characterizing the application scenarios where each performs best. Finally, recommendations are provided to system designers for selecting the most suitable approach.
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paper_title: Intermittent Computing: Challenges and Opportunities

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The maturation of energy-harvesting technology and ultra-low-power computer systems has led to the advent of intermittently-powered, batteryless devices that operate entirely using energy extracted from their environment. Intermittently operating devices present a rich vein of programming languages research challenges and the purpose of this paper is to illustrate these challenges to the PL research community. To provide depth, this paper includes a survey of the hardware and software design space of intermittent computing platforms. On the foundation of these research challenges and the state of the art in intermittent hardware and software, this paper describes several future PL research directions, emphasizing a connection between intermittence, distributed computing, energy-aware programming and compilation, and approximate computing. We illustrate these connections with a discussion of our ongoing work on programming for intermittence, and on building and simulating intermittent distributed systems.
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paper_title: Energy Harvesting Sensor Nodes: Survey and Implications

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Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.
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paper_title: The Future of Sensing is Batteryless, Intermittent, and Awesome

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Sensing has been obsessed with delivering on the "smart dust" vision outlined decades ago, where trillions of tiny invisible computers support daily life, infrastructure, and humanity in general. Batteries are the single greatest threat to this vision of a sustainable Internet of Things. They are expensive, bulky, hazardous, and wear out after a few years (even rechargeables). Replacing and disposing of billions or trillions of dead batteries per year would be expensive and irresponsible. By leaving the batteries behind and surviving off energy harvested from the environment, tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades. The intermittent execution, constrained compute and energy resources, and unreliability of these devices creates new challenges for the sensing and embedded systems community. However, the rewards and potential impact across many fields are worth it, enabling currently impractical applications in health services and patient care, commercial and consumer applications, wildlife conservation, industrial and infrastructure management, even space exploration. This paper highlights major research questions and establishes new directions for the community to embrace and investigate.
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paper_title: Energy Harvesting Technologies

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Energy Harvesting Technologies provides a cohesive overview of the fundamentals and current developments in the field of energy harvesting. In a well-organized structure, this volume discusses basic principles for the design and fabrication of bulk and MEMS based vibration energy systems, theory and design rules required for fabrication of efficient electronics, in addition to recent findings in thermoelectric energy harvesting systems. Combining leading research from both academia and industry onto a single platform, Energy Harvesting Technologies serves as an important reference for researchers and engineers involved with power sources, sensor networks and smart materials.
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paper_title: A survey of energy harvesting sources for embedded systems

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Historically, batteries have been the source of energy for most mobile, embedded and remote system applications. Now, with ubiquitous computing requirements in the fields of embedded systems, wireless sensor networks and low- power electronics such as MEMS devices, an alternative source of energy is required. Also with the limited capacity of finite power sources and the need for supplying energy for a lifetime of a system, there is a requirement for self- powered devices. The process of extracting energy from the surrounding environment is termed as energy harvesting. Energy harvesting, which originated from the windmill and water wheel, is widely being considered as a low- maintenance solution for a wide variety of applications. There are various forms of energy that can be scavenged, like thermal, mechanical, solar, acoustic, wind, and wave. This paper serves as a survey for identifying the sources of energy harvesting based on various technical papers available in the public domain.
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paper_title: Energy-Harvesting Active Networked Tags (EnHANTs): Prototyping and Experimentation

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This article focuses on a new type of wireless devices in the domain between RFIDs and sensor networks—Energy-Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be small, flexible, and self-powered devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, toys, produce, and clothing). Therefore, they will provide the infrastructure for various tracking applications and can serve as one of the enablers for the Internet of Things. We present the design considerations for the EnHANT prototypes, developed over the past 4 years. The prototypes harvest indoor light energy using custom organic solar cells, communicate and form multihop networks using ultra-low-power Ultra-Wideband Impulse Radio (UWB-IR) transceivers, and dynamically adapt their communications and networking patterns to the energy harvesting and battery states. We describe a small-scale testbed that uniquely allows evaluating different algorithms with trace-based light energy inputs. Then, we experimentally evaluate the performance of different energy-harvesting adaptive policies with organic solar cells and UWB-IR transceivers. Finally, we discuss the lessons learned during the prototype and testbed design process.
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paper_title: Battery-free sensing platform for wearable devices: The synergy between two feet

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Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the people as long as being functioning. In this study, we propose a battery-free sensing platform for wearable devices in the form-factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds' walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.
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paper_title: Novel Batteryless Wireless Sensor for Traffic-Flow Measurement

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This paper presents a novel batteryless wireless sensor that can be embedded in the road and used for the measurement of traffic-flow rate. Compared with existing inductive loop-based traffic sensors, the new sensor is expected to provide increased reliability, easy installation, and low cost. The sensor requires no external power source and has zero idle power loss. Energy to power this sensor is harvested from the short-duration vibrations that result when an automobile passes over the sensor. Since much of the earlier work in literature on vibration energy harvesting has focused on continuous sources of vibration, this paper focuses on short-duration vibrations and on developing low-power control algorithms that can be implemented on the sensor using an analog circuit. To this effect, this paper develops and compares three control algorithms: 1) “fixed threshold switching,” 2) “maximum voltage switching,” and 3) “switched inductor.” The “switched inductor” algorithm is shown to be the most effective at maximizing harvested energy. Experimental results are presented and validate the fact that adequate energy can be harvested from each passing vehicle to enable successful wireless transmission of traffic data.
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paper_title: Batteries used to power implantable biomedical devices

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Battery systems have been developed that provide years of service for implantable medical devices. The primary systems utilize lithium metal anodes with cathode systems including iodine, manganese oxide, carbon monofluoride, silver vanadium oxide and hybrid cathodes. Secondary lithium ion batteries have also been developed for medical applications where the batteries are charged while remaining implanted. While the specific performance requirements of the devices vary, some general requirements are common. These include high safety, reliability and volumetric energy density, long service life, and state of discharge indication. Successful development and implementation of these battery types has helped enable implanted biomedical devices and their treatment of human disease.
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paper_title: A pacemaker powered by an implantable biofuel cell operating under conditions mimicking the human blood circulatory system--battery not included.

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Biocatalytic electrodes made of buckypaper were modified with PQQ-dependent glucose dehydrogenase on the anode and with laccase on the cathode and were assembled in a flow biofuel cell filled with serum solution mimicking the human blood circulatory system. The biofuel cell generated an open circuitry voltage, Voc, of ca. 470 mV and a short circuitry current, Isc, of ca. 5 mA (a current density of 0.83 mA cm(-2)). The power generated by the implantable biofuel cell was used to activate a pacemaker connected to the cell via a charge pump and a DC-DC converter interface circuit to adjust the voltage produced by the biofuel cell to the value required by the pacemaker. The voltage-current dependencies were analyzed for the biofuel cell connected to an Ohmic load and to the electronic loads composed of the interface circuit, or the power converter, and the pacemaker to study their operation. The correct pacemaker operation was confirmed using a medical device - an implantable loop recorder. Sustainable operation of the pacemaker was achieved with the system closely mimicking human physiological conditions using a single biofuel cell. This first demonstration of the pacemaker activated by the physiologically produced electrical energy shows promise for future electronic implantable medical devices powered by electricity harvested from the human body.
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paper_title: Design of an RFID-Based Battery-Free Programmable Sensing Platform

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This paper presents the wireless identification and sensing platform (WISP), which is a programmable battery-free sensing and computational platform designed to explore sensor-enhanced radio frequency identification (RFID) applications. WISP uses a 16-bit ultralow-power microcontroller to perform sensing and computation while exclusively operating from harvested RF energy. Sensors that have successfully been integrated into the WISP platform to date include temperature, ambient light, rectified voltage, and orientation. The microcontroller encodes measurements into an electronic product code (EPC) Class 1 Generation 1 compliant ID and dynamically computes the required 16-bit cyclical redundancy checking (CRC). Finally, WISP emulates the EPC protocol to communicate the ID to the RFID reader. To the authors' knowledge, WISP is the first fully programmable computing platform that can operate using power transmitted from a long-range (UHF) RFID reader and communicate arbitrary multibit data in a single response packet.
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paper_title: Energy harvesting active networked tags (EnHANTs) for ubiquitous object networking

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This article presents the design challenges posed by a new class of ultra-low-power devices referred to as Energy-Harvesting Active Networked Tags (EnHANTs). EnHANTs are small, flexible, and self-reliant (in terms of energy) devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, walls, doors, toys, keys, produce, and clothing). EnHANTs will enable the Internet of Things by providing the infrastructure for various novel tracking applications. Examples of these applications include locating misplaced items, continuous monitoring of objects, and determining locations of disaster survivors. Recent advances in ultra-low-power circuit design, ultra-wideband (UWB) wireless communications, and organic energy harvesting techniques will enable the realization of EnHANTs in the near future. The harvesting components and the ultra-low-power physical layer have special characteristics whose implications on the higher layers have yet to be studied (e.g., when using UWB communications, the energy required to receive a bit is significantly higher than the energy required to transmit a bit). In this article, we describe paradigm shifts associated with technologies that enable EnHANTs and demonstrate their implications on higher-layer protocols. Moreover, we describe some of the components we have designed for EnHANTs. Finally, we briefly discuss our indoor light measurements and their implications on the design of higher-layer protocols.
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paper_title: Wearable battery-less wireless sensor network with electromagnetic energy harvesting system

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Abstract This paper presents a battery-less wireless sensor network (WSN) equipped with electromagnetic (EM) energy harvesters and sensor nodes with adjustable time-interval based on stored the energy. A wearable EM energy harvesting system is developed and optimized to power-up a typical wireless sensor mote from body motion. This is realized through characterization of the body motion and design of a compact EM energy harvester according to vibration frequencies generated during human running and walking. The harvesting system provides self-initiating and regulated supply voltage through rectifying, charging buffer capacitance, and buck DC-DC converter. The proposed system provides maintenance-free operation and adjustment of the sensing-rate according to jogger activity. It is shown that energy-level-based adjustment of the sensing and transmitting time-interval can prolong the lifetime of the battery-less sensor node. It is experimentally demonstrated that the presented WSN measurement nodes provide reliable and long-term operation as attached to human body, proving the feasibility of the system as a wearable battery-less sensor network.
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paper_title: Powering indoor sensing with airflows: a trinity of energy harvesting, synchronous duty-cycling, and sensing

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Whereas a lot of efforts have been put on energy conservation in wireless sensor networks, the limited lifetime of these systems still hampers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) ceases to work. To enable long-lived indoor sensing, we report in this paper a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an "energy-free" sensing. First of all, given the pervasive operation of heating, ventilation and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny (only of hundreds of μW), an extremely low but synchronous duty-cycle has to be applied whereas the system gets no energy surplus to support existing synchronization schemes. So we design two complementary synchronization schemes that cost virtually no energy. Finally, we exploit the feature of our harvester to sense the airflow speed (which can be used to infer the indoor microclimate) in an energy-free manner. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.
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paper_title: Efficient light harvesting for accurate neural classification of human activities

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Energy autonomy extension of wearable devices is an ever increasing user need and it can be achieved by inexpensive energy harvesting from the broadly available solar and artificial light. However efficient conversion, relevant storage and utilization must be carefully implemented if the device supports power-hungry applications such as Artificial Intelligence for human activity classification based on Artificial Neural Networks. In this paper, a whole hardware and software system implementation is presented, which is able to achieve system autonomy extension and at the same time high classification accuracy. Quantitative and qualitative results are shown under real working conditions.
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paper_title: Low-cost and Device-free Activity Recognition System with Energy Harvesting PIR and Door Sensors

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Progress of IoT and ubiquitous computing technologies has strong anticipation to realize smart services in households such as efficient energy-saving appliance control and elderly monitoring. In order to put those applications into practice, high-accuracy and low-cost in-home living activity recognition is essential. Many researches have tackled living activity recognition so far, but the following problems remain: (i)privacy exposure due to utilization of cameras and microphones; (ii) high deployment and maintenance costs due to many sensors used; (iii) burden to force the user to carry the device and (iv) wire installation to supply power and communication between sensor node and server; (v) few recognizable activities; (vi) low recognition accuracy. In this paper, we propose an in-home living activity recognition method to solve all the problems. To solve the problems (i)--(iv), our method utilizes only energy harvesting PIR and door sensors with a home server for data collection and processing. The energy harvesting sensor has a solar cell to drive the sensor and wireless communication modules. To solve the problems (v) and (vi), we have tackled the following challenges: (a) determining appropriate features for training samples; and (b) determining the best machine learning algorithm to achieve high recognition accuracy; (c) complementing the dead zone of PIR sensor semipermanently. We have conducted experiments with the sensor by five subjects living in a home for 2-3 days each. As a result, the proposed method has achieved F-measure: 62.8% on average.
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paper_title: Tethys: Collecting Sensor Data without Infrastracture or Trust

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Careful resource monitoring is necessary to understand usage patterns and set conservation goals in an institutional setting. Sensor systems provide data to measure consumption and evaluate the effectiveness of active interventions. However, deploying sensing systems can be difficult when infrastructure support is limited. This paper describes the process of designing Tethys, a wireless water flow sensor that collects data at per-fixture granularity without dependence on existing infrastructure and trusted gateways. Rather than rely on electrical infrastructure, Tethys implements energy harvesting to allow for long term deployment. To avoid dependence on existing network infrastructure, Tethys crowdsources the data collection process to residents' smartphones acting as gateways. These gateways are untrusted and unreliable, so Tethys implements end-to-end reliability and security between the sensing device and a cloud backend. We present initial findings from a deployment in undergraduate residential halls. Our results demonstrate that Tethys can capture meaningful patterns in shower use. For instance, visible water conservation signs are statistically correlated with shorter mean shower length (p < 0:05) and are a potential area for future studies.
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paper_title: Step Detection from Power Generation Pattern in Energy-Harvesting Wearable Devices

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Energy-harvesting wearable devices generate power by converting natural phenomena such as human motion into usable electricity. We conduct an experimental study to validate the feasibility of detecting steps from the power generation patterns of a wearable piezoelectric energy harvester (PEH). Four healthy adults took part in the study, which includes walking along straight and turning walkways as well as descending and ascending stairs. We find that power generation exhibits distinctive peaks for each step, making it possible to accurately detect steps using widely used peak detection algorithms. Using our PEH prototype, we successfully detected 550 steps out of 570, achieving a step detection accuracy of 96%.
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paper_title: Energy-harvesting thermoelectric sensing for unobtrusive water and appliance metering

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Fine-grained energy metering in homes and buildings provides a promising technique for addressing the unmaintainable energy consumption levels of worldwide buildings. Metering electricity, lighting, natural gas, HVAC, occupancy, and water on a per appliance or room basis can provide invaluable insight when trying to reduce a building's energy footprint. A myriad of sensor designs and systems collect data on particular building aspects, but are often hampered by installation difficulty or ongoing maintenance needs (like battery replacement). We address these common pitfalls for water and heat metering by developing a small, energy-harvesting sensor that meters using the same thermoelectric generator with which it powers itself. In short, the rate at which the harvester captures energy is proportional to the heat production of the monitored appliance or pipe and this relationship allows us to estimate energy use simply based on the sensor's ability to harvest. We prototype our sensor in a bracelet shaped form-factor that can attach to a shower head pipe, faucet, or appliance to provide local hot water or heat metering.
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paper_title: DoubleDip: leveraging thermoelectric harvesting for low power monitoring of sporadic water use

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We present DoubleDip, a low power monitoring system for enabling non-intrusive water flow detection. DoubleDip taps into minute thermal gradients in pipes for both replenishing energy reserves and performing low power wakeup. One of the remaining issues with wireless water monitoring in residences and offices is that current solutions require installing sensor nodes with access to electrical wiring or replacing batteries frequently. DoubleDip (DD) significantly extends the lifetime of vibration-based non-intrusive water flow sensors by harvesting thermal energy from hot pipes wherever accessible. DoubleDip requires less than an inch of exposed metal pipe to attach a coupler for gathering sufficient energy to power itself, in some cases, into perpetuity. We observe that water use in homes and offices is incredibly sporadic, making continuous monitoring both impractical and wasteful. Instead, DD puts a thermoelectric harvester into double duty. It uses thermal gradients not only for gathering energy but also for extremely low power (
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paper_title: CapSense: Capacitor-based Activity Sensing for Kinetic Energy Harvesting Powered Wearable Devices

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We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion data at high frequency, which promises significant power saving for the sensing device. We prototype a shoe-mounted KEH-powered wearable device and conduct experiments with 10 subjects for detecting 5 different activities. Our results show that compared to the existing time-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power, after considering wireless transmissions, by 75%. CapSense recognizes activities with up to 90%.
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paper_title: Intermittently-powered energy harvesting step counter for fitness tracking

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Over the past decade, there has been a rapid increase in the popularity of wearable and portable devices, such as step counters, to monitor fitness performance. However, these devices are battery-powered, meaning that their lifetimes are restricted by battery capacity. Ideally, wearable devices could be powered by energy harvested from human motion. Energy harvesting systems traditionally incorporate energy storage to cope with source variability. However, energy storage takes time to charge and increases the size and cost of systems. This paper proposes an intermittently-powered energy harvesting step counter for integrated wearable applications, which aims to remove the energy storage element. The proposed step counter sustains its operation by harvesting energy from footsteps using a ferroelectret insole, which also works as an event detection sensor, i.e. the system is powered by the parameter that is being sensed. Designing this required the characterization of the insole to evaluate the amount of energy provided, and analysis of the energy needed by the overall system. Finally, the system was implemented and experimentally validated. The proposed step counter has an error of less than 4% when walking, which is lower than the error in conventional smartphone applications.
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paper_title: Energy Harvesting Wearables Can Tell Which Train Route You Have Taken

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Location privacy is an important concern for the users of mobile devices and this private information can be identified using the embedded motion sensors in these devices. This paper highlights the privacy leakage resulting from unrestricted access to seemingly innocuous generated patterns of vibration energy harvesting (VEH) circuits in mobile devices. We investigate that by monitoring the VEH generated patterns, a malware application can infer which train route the passenger has taken, violating her privacy. We collected real-world motion data for 4 distinct train routes in the Sydney metropolitan area. Our results show that the passenger's train route can be inferred with an average accuracy of 97.2% for a journey of 7 stations, confirming the rich contextual information embedded in VEH data and to light the associated privacy risks in releasing this data.
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paper_title: Transportation mode detection using kinetic energy harvesting wearables

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Detecting the transportation mode of an individual's everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, accelerometer and GPS are the dominantly used signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we investigate the feasibility of using the output voltage from the kinetic energy harvesting device as the signal source to achieve transportation mode detection. The proposed idea is based on the intuition that the vibrations experienced by the passenger during motoring of different transportation modes are different. Thus, voltage generated by the energy harvesting devices should contain distinctive features to distinguish different transportation modes. Using the dataset collected from a real energy harvesting device, we present the initial demonstration of the proposed method. We can achieve 98.84% of accuracy in determining whether the user is traveling by pedestrian or motorized modes, and in a fine-grained classification of three different motorized modes (car, bus, and train), an overall accuracy over 85% is achieved.
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paper_title: Step Detection from Power Generation Pattern in Energy-Harvesting Wearable Devices

paper_content:

Energy-harvesting wearable devices generate power by converting natural phenomena such as human motion into usable electricity. We conduct an experimental study to validate the feasibility of detecting steps from the power generation patterns of a wearable piezoelectric energy harvester (PEH). Four healthy adults took part in the study, which includes walking along straight and turning walkways as well as descending and ascending stairs. We find that power generation exhibits distinctive peaks for each step, making it possible to accurately detect steps using widely used peak detection algorithms. Using our PEH prototype, we successfully detected 550 steps out of 570, achieving a step detection accuracy of 96%.
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paper_title: HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices

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Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.
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paper_title: The Future of Sensing is Batteryless, Intermittent, and Awesome

paper_content:

Sensing has been obsessed with delivering on the "smart dust" vision outlined decades ago, where trillions of tiny invisible computers support daily life, infrastructure, and humanity in general. Batteries are the single greatest threat to this vision of a sustainable Internet of Things. They are expensive, bulky, hazardous, and wear out after a few years (even rechargeables). Replacing and disposing of billions or trillions of dead batteries per year would be expensive and irresponsible. By leaving the batteries behind and surviving off energy harvested from the environment, tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades. The intermittent execution, constrained compute and energy resources, and unreliability of these devices creates new challenges for the sensing and embedded systems community. However, the rewards and potential impact across many fields are worth it, enabling currently impractical applications in health services and patient care, commercial and consumer applications, wildlife conservation, industrial and infrastructure management, even space exploration. This paper highlights major research questions and establishes new directions for the community to embrace and investigate.
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paper_title: Pedometers Without Batteries: An Energy Harvesting Shoe

paper_content:

The demand for thin, light wearables with long battery lifetimes motivate novel energy optimization techniques for a wide range of devices with applications ranging from telemedicine in clinical devices to activity recognition in consumer applications. In this paper, we present a simple activity monitoring system based on a piezoelectric energy-harvesting platform. The device requires no battery, no recharging, and no complex algorithms, instead leveraging the relationship between energy harvesting efficiency and user activity to create an effective wearable device with an indefinite lifetime. Experimental results confirm that the system can be used for coarse-grained activity monitoring with no external power source.
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paper_title: Feasibility and accuracy of hotword detection using vibration energy harvester

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Vibration energy harvesting (VEH) is a promising source of renewable energy that can be used to extend battery life of next generation mobile devices. In this paper, we study the feasibility and accuracy of VEH for detecting hotwords, such as “OK Google”, used by popular voice control applications to distinguish user commands from other conversations. The idea of using power signals of VEH to detect hotwords is based on the fact that human voice creates vibrations in the air, which could be potentially picked up by the VEH hardware inside a mobile device. Using off-the-shelf VEH product, we conduct a comprehensive experimental study involving 8 subjects. We analyse two possible usage scenarios for the VEH hardware. In the first scenario, the user is not required to talk directly to the device (indirect), but the VEH is expected to pick up the ambient vibrations caused by user-generated sound waves. In the second, the user is expected to direct his voice to the VEH (direct) and talk to it from a close distance. For both usage scenarios, we evaluate two types of hotword detection, speaker-independent and speaker-dependent. We find that VEH can detect hotwords more accurately in the direct scenario compared to the indirect. For the direct scenario, our results show that a simple Decision Tree classifier can detect hotwords from VEH signals with accuracies of 73% and 85%, respectively, for speaker-independent and speaker-dependent detections. Finally, we show that these accuracies are comparable to what could be achieved with an accelerometer sampled at 200 Hz.
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paper_title: A Self-Powered Insole for Human Motion Recognition

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Biomechanical energy harvesting is a feasible solution for powering wearable sensors by directly driving electronics or acting as wearable self-powered sensors. A wearable insole that not only can harvest energy from foot pressure during walking but also can serve as a self-powered human motion recognition sensor is reported. The insole is designed as a sandwich structure consisting of two wavy silica gel film separated by a flexible piezoelectric foil stave, which has higher performance compared with conventional piezoelectric harvesters with cantilever structure. The energy harvesting insole is capable of driving some common electronics by scavenging energy from human walking. Moreover, it can be used to recognize human motion as the waveforms it generates change when people are in different locomotion modes. It is demonstrated that different types of human motion such as walking and running are clearly classified by the insole without any external power source. This work not only expands the applications of piezoelectric energy harvesters for wearable power supplies and self-powered sensors, but also provides possible approaches for wearable self-powered human motion monitoring that is of great importance in many fields such as rehabilitation and sports science.
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paper_title: SEHS: Simultaneous Energy Harvesting and Sensing Using Piezoelectric Energy Harvester

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Piezoelectric energy harvesting (PEH), which converts ambient motion, stress, and vibrations into usable electricity, may help combat battery issues in a growing number of industrial and wearable Internet of things (IoTs). Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts. These developments suggest that the same PEH hardware could be potentially used for simultaneous energy harvesting and sensing (SEHS), offering a new design space for low cost and low power IoT devices. Unfortunately, realization of SEHS is challenging as the energy harvesting process distorts the sensing signal. To achieve high quality sensing from PEH, the state-of-the-art uses separate PEHs for sensing and energy harvesting, which increases system complexity, form factor, and cost. In this paper, we propose a novel SEHS architecture, which combines energy harvesting and sensing in the same piece of PEH, and minimizes distortion in the sensing signal by applying a special filtering algorithm. We prototype the SEHS concept in the form factor of a shoe, and evaluate its energy harvesting as well as sensing performance with 20 subjects using gait recognition as a case study. We demonstrate that the SEHS prototype harvests up to 127% more energy and detects human gait with 8% higher accuracy while consuming 35% less power compared to the state-of-the-art.
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paper_title: Estimating calorie expenditure from output voltage of piezoelectric energy harvester: an experimental feasibility study

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There is a growing interest in developing energy harvesting solutions for wearable devices so they can self-power themselves without relying on batteries. Piezoelectric energy harvesters (PEHs) can convert kinetic energy released from human activities into usable electrical energy for powering various electronic circuits inside the wearable device. Intuitively, the kinetic energy is produced because the user expends some calories during the physical activities. We therefore postulate that the voltage output of a PEH in a wearable device should contain information that can be used to estimate the amount of calorie expended. If this is true, then the PEH can be used as a new source for calorie estimation. Unlike conventional sensors, such as accelerometers, a PEH does not consume any power, which would make this new source very attractive. In this paper, using real PEH hardware and the data collected from ten real subjects, we conduct an experimental study to assess the suitability of PEH voltage in estimating calorie expenditure for two different activities, walking and running. We find that, for most subjects, the calorie estimations obtained from the output voltage of PEH is very close to those obtained from a 3-axial accelerometer.
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paper_title: Intermittently-powered energy harvesting step counter for fitness tracking

paper_content:

Over the past decade, there has been a rapid increase in the popularity of wearable and portable devices, such as step counters, to monitor fitness performance. However, these devices are battery-powered, meaning that their lifetimes are restricted by battery capacity. Ideally, wearable devices could be powered by energy harvested from human motion. Energy harvesting systems traditionally incorporate energy storage to cope with source variability. However, energy storage takes time to charge and increases the size and cost of systems. This paper proposes an intermittently-powered energy harvesting step counter for integrated wearable applications, which aims to remove the energy storage element. The proposed step counter sustains its operation by harvesting energy from footsteps using a ferroelectret insole, which also works as an event detection sensor, i.e. the system is powered by the parameter that is being sensed. Designing this required the characterization of the insole to evaluate the amount of energy provided, and analysis of the energy needed by the overall system. Finally, the system was implemented and experimentally validated. The proposed step counter has an error of less than 4% when walking, which is lower than the error in conventional smartphone applications.
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paper_title: HiddenCode: Hidden Acoustic Signal Capture with Vibration Energy Harvesting

paper_content:

The feasibility of using vibration energy harvesting (VEH) as an energy-efficient receiver for short-range acoustic data communication has been investigated recently. When data was encoded in acoustic signal within the energy harvesting frequency band and transmitted through a speaker, a VEH receiver was capable of decoding the data by processing the harvested energy signal. Although previous work created new opportunities for simultaneous energy harvesting and communication using the same hardware, the communication makes annoying sounds as the energy harvesting frequency band lies within the sensitive region of human auditory system. In this work, we present a novel modulation scheme to completely hide all communications within background music sound. The proposed modulation exploits sound masking theory to maximize signal to noise ratio of data communication without being audible to the music listener. We capitalize on the existence of repetitive sound patterns within popular music to realize synchronization between the transmitter and the receiver. We implement the proposed modulation within multiple hit songs and demonstrate its efficacy using a real VEH prototype made from off-the-shelf hardware. A user study involving 30 subjects confirms that the proposed modulation can completely hide VEH-based data communication from human perception while achieving up to 14 bps data rate, which is sufficient to transmit short codes or coupons of practical use.
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paper_title: Biometric Gait Authentication Using Accelerometer Sensor

paper_content:

This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.
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paper_title: Step Detection from Power Generation Pattern in Energy-Harvesting Wearable Devices

paper_content:

Energy-harvesting wearable devices generate power by converting natural phenomena such as human motion into usable electricity. We conduct an experimental study to validate the feasibility of detecting steps from the power generation patterns of a wearable piezoelectric energy harvester (PEH). Four healthy adults took part in the study, which includes walking along straight and turning walkways as well as descending and ascending stairs. We find that power generation exhibits distinctive peaks for each step, making it possible to accurately detect steps using widely used peak detection algorithms. Using our PEH prototype, we successfully detected 550 steps out of 570, achieving a step detection accuracy of 96%.
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paper_title: EcoVibe: On-Demand Sensing for Railway Bridge Structural Health Monitoring

paper_content:

Energy efficient sensing is one of the main objectives in the design of networked embedded monitoring systems. However, existing approaches such as duty cycling and ambient energy harvesting face challenges in railway bridge health monitoring applications due to the unpredictability of train passages and insufficient ambient energy around bridges. This paper presents eco-friendly vibration (ECO VIBE), an on-demand sensing system that automatically turns on itself when a train passes on the bridge and adaptively powers itself off after finishing all tasks. After that, it goes into an inactive state with near-zero power dissipation. ECO VIBE achieves these by: first, a novel, fully passive event detection circuit to continuously detect passing trains without consuming any energy. Second, combining train-induced vibration energy harvesting with a transistor-based load switch, a tiny amount of energy is sufficient to keep ECO VIBE active for a long time. Third, a passive adaptive off control circuit is introduced to quickly switch off ECO VIBE. Also this circuit does not consume any energy during inactivity periods. We present the prototype implementation of the proposed system using commercially available components and evaluate its performance in real-world scenarios. Our results show that ECO VIBE is effective in railway bridge health monitoring applications.
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paper_title: KEH-Gait: Using Kinetic Energy Harvesting for Gait-based User Authentication Systems

paper_content:

With the rapid development of sensor networks and embedded computing technologies, miniaturized wearable healthcare monitoring devices have become practically feasible. For many of these devices, accelerometer-based user authentication systems by gait analysis are becoming a hot research topic. However, a major bottleneck of such system is it requires continuous sampling of accelerometer, which reduces battery life of wearable sensors. In this paper, we present KEH-Gait , which advocates use of output voltage signal from kinetic energy harvester (KEH) as the source for gait recognition. KEH-Gait is motivated by the prospect of significant power saving by not having to sample the accelerometer at all. Indeed, our measurements show that, compared to conventional accelerometer-based gait detection, KEH-Gait can reduce energy consumption by 82.15 percent. The feasibility of KEH-Gait is based on the fact that human gait has distinctive movement patterns for different individuals, which is expected to leave distinctive patterns for KEH as well. We evaluate the performance of KEH-Gait using two different types of KEH hardware on a data set of 20 subjects. Our experiments demonstrate that, although KEH-Gait yields slightly lower accuracy than accelerometer-based gait detection when single step is used, the accuracy problem can be overcome by the proposed Probability-based Multi-Step Sparse Representation Classification (PMSSRC). Moreover, the security analysis shows that the EER of KEH-Gait against an active spoofing attacker is 11.2 and 14.1 percent using two different types of KEH hardware, respectively.
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paper_title: Pedometers Without Batteries: An Energy Harvesting Shoe

paper_content:

The demand for thin, light wearables with long battery lifetimes motivate novel energy optimization techniques for a wide range of devices with applications ranging from telemedicine in clinical devices to activity recognition in consumer applications. In this paper, we present a simple activity monitoring system based on a piezoelectric energy-harvesting platform. The device requires no battery, no recharging, and no complex algorithms, instead leveraging the relationship between energy harvesting efficiency and user activity to create an effective wearable device with an indefinite lifetime. Experimental results confirm that the system can be used for coarse-grained activity monitoring with no external power source.
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paper_title: Unobtrusive gait verification for mobile phones

paper_content:

Continuously and unobtrusively identifying the phone's owner using accelerometer sensing and gait analysis has a great potential to improve user experience on the go. However, a number of challenges, including gait modeling and training data acquisition, must be addressed before unobtrusive gait verification is practical. In this paper, we describe a gait verification system for mobile phone without any assumption of body placement or device orientation. Our system uses a combination of supervised and unsupervised learning techniques to verify the user continuously and automatically learn unseen gait pattern from the user over time. We demonstrate that it is capable of recognizing the user in natural settings. We also investigated an unobtrusive training method that makes it feasible to acquire training data without explicit user annotation.
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paper_title: Powering indoor sensing with airflows: a trinity of energy harvesting, synchronous duty-cycling, and sensing

paper_content:

Whereas a lot of efforts have been put on energy conservation in wireless sensor networks, the limited lifetime of these systems still hampers their practical deployments. This situation is further exacerbated indoors, as conventional energy harvesting (e.g., solar) ceases to work. To enable long-lived indoor sensing, we report in this paper a self-sustaining sensing system that draws energy from indoor environments, adapts its duty-cycle to the harvested energy, and pays back the environment by enhancing the awareness of the indoor microclimate through an "energy-free" sensing. First of all, given the pervasive operation of heating, ventilation and air conditioning (HVAC) systems indoors, our system harvests energy from airflow introduced by the HVAC systems to power each sensor node. Secondly, as the harvested power is tiny (only of hundreds of μW), an extremely low but synchronous duty-cycle has to be applied whereas the system gets no energy surplus to support existing synchronization schemes. So we design two complementary synchronization schemes that cost virtually no energy. Finally, we exploit the feature of our harvester to sense the airflow speed (which can be used to infer the indoor microclimate) in an energy-free manner. To our knowledge, this is the first indoor wireless sensing system that encapsulates energy harvesting, network operating, and sensing all together.
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paper_title: Feasibility and accuracy of hotword detection using vibration energy harvester

paper_content:

Vibration energy harvesting (VEH) is a promising source of renewable energy that can be used to extend battery life of next generation mobile devices. In this paper, we study the feasibility and accuracy of VEH for detecting hotwords, such as “OK Google”, used by popular voice control applications to distinguish user commands from other conversations. The idea of using power signals of VEH to detect hotwords is based on the fact that human voice creates vibrations in the air, which could be potentially picked up by the VEH hardware inside a mobile device. Using off-the-shelf VEH product, we conduct a comprehensive experimental study involving 8 subjects. We analyse two possible usage scenarios for the VEH hardware. In the first scenario, the user is not required to talk directly to the device (indirect), but the VEH is expected to pick up the ambient vibrations caused by user-generated sound waves. In the second, the user is expected to direct his voice to the VEH (direct) and talk to it from a close distance. For both usage scenarios, we evaluate two types of hotword detection, speaker-independent and speaker-dependent. We find that VEH can detect hotwords more accurately in the direct scenario compared to the indirect. For the direct scenario, our results show that a simple Decision Tree classifier can detect hotwords from VEH signals with accuracies of 73% and 85%, respectively, for speaker-independent and speaker-dependent detections. Finally, we show that these accuracies are comparable to what could be achieved with an accelerometer sampled at 200 Hz.
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paper_title: SEHS: Simultaneous Energy Harvesting and Sensing Using Piezoelectric Energy Harvester

paper_content:

Piezoelectric energy harvesting (PEH), which converts ambient motion, stress, and vibrations into usable electricity, may help combat battery issues in a growing number of industrial and wearable Internet of things (IoTs). Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts. These developments suggest that the same PEH hardware could be potentially used for simultaneous energy harvesting and sensing (SEHS), offering a new design space for low cost and low power IoT devices. Unfortunately, realization of SEHS is challenging as the energy harvesting process distorts the sensing signal. To achieve high quality sensing from PEH, the state-of-the-art uses separate PEHs for sensing and energy harvesting, which increases system complexity, form factor, and cost. In this paper, we propose a novel SEHS architecture, which combines energy harvesting and sensing in the same piece of PEH, and minimizes distortion in the sensing signal by applying a special filtering algorithm. We prototype the SEHS concept in the form factor of a shoe, and evaluate its energy harvesting as well as sensing performance with 20 subjects using gait recognition as a case study. We demonstrate that the SEHS prototype harvests up to 127% more energy and detects human gait with 8% higher accuracy while consuming 35% less power compared to the state-of-the-art.
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paper_title: VEH-COM: Demodulating vibration energy harvesting for short range communication

paper_content:

This paper investigates the possibility of using a vibration energy harvesting (VEH) device as a communication receiver. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, we aim to transmit small amounts of data at low rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming digital signal processing (DSP), which makes a VEH receiver significantly more power efficient than a conventional microphone-based decoder. To address the extremely narrow bandwidth of VEH, we design a simple ON-OFF keying modulation, but optimized for VEH hardware. Experiments with a real VEH device shows that, at a distance of 2 cm, a laptop speaker with the proposed modulation scheme can achieve 30 bps communication for a target bit error rate of less than 1%, which would enable many emerging short range applications, such as mobile payment. The communication range of a laptop can be extended to 80 cm for 5 bps, allowing a range of other audio-based device-to-device communications, such as a web advertisement on a laptop browser transferring tokens to a nearby smartphone. We also demonstrate that the proposed VEH-based sound decoding is resilient to background noise, thanks to its extremely narrow power harvesting bandwidth, which works as a natural noise filter.
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paper_title: Remote Detection of Chemical Reactions using Nanoscale Terahertz Communication Powered by Pyroelectric Energy Harvesting

paper_content:

A novel self-powered sensing and communication architecture for remote detection of chemical reactions is proposed. It is assumed that pyroelectric nanogenerators fitted with Graphene-based nano-antennas radiating in the Terahertz band (0.1-10THz) are embedded in the catalyst surface where different types of chemical reactions take place. Each reaction consumes or dissipates some heat, which causes temperature fluctuations on the catalyst surface. A pyroelectric nanogenerator harvests electrical energy from each temperature fluctuation and use the energy to transmit a THz pulse of proportional amplitude. Because different types of reactions dissipate different amounts of energy, we show that a remote receiver can detect the reaction type from the received pulse energy. The accuracy of reaction detection at the receiver, however, is compromised by the noise and attenuation of the THz channel, which makes it difficult to detect reactions from a longer distance. Using simulations, it is shown that dynamic frequency selection within the THz band based on the expected chemical composition of the reactor at any given time can help extending the distance of remote reaction detection.
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paper_title: Energy-harvesting thermoelectric sensing for unobtrusive water and appliance metering

paper_content:

Fine-grained energy metering in homes and buildings provides a promising technique for addressing the unmaintainable energy consumption levels of worldwide buildings. Metering electricity, lighting, natural gas, HVAC, occupancy, and water on a per appliance or room basis can provide invaluable insight when trying to reduce a building's energy footprint. A myriad of sensor designs and systems collect data on particular building aspects, but are often hampered by installation difficulty or ongoing maintenance needs (like battery replacement). We address these common pitfalls for water and heat metering by developing a small, energy-harvesting sensor that meters using the same thermoelectric generator with which it powers itself. In short, the rate at which the harvester captures energy is proportional to the heat production of the monitored appliance or pipe and this relationship allows us to estimate energy use simply based on the sensor's ability to harvest. We prototype our sensor in a bracelet shaped form-factor that can attach to a shower head pipe, faucet, or appliance to provide local hot water or heat metering.
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paper_title: SEMON: Sensorless Event Monitoring in Self-Powered Wireless Nanosensor Networks

paper_content:

A conventional wireless sensor network node consists of a number of components: microprocessor, memory, sensor, and radio. Advances in nanotechnology have enabled the miniaturization of these components, thus enabling wireless nanoscale sensor networks (WNSN). Due to their small size, WNSN nodes are expected to be powered by harvesting energy from the environment. Unfortunately, there is a mismatch in the energy that can be harvested and the energy required to power all the aforementioned components in a WNSN node. In this article, we propose a simplified sensor node architecture for event detection. We call our architecture Sensorless Event MONitoring in self-powered WNSNs (SEMON). A SEMON node consists of only an energy harvester and a radio with minimal processing capacity. We assume that each event to be monitored will generate a different amount of energy, and we can therefore use this amount of energy as the signature of an event. When an event occurs, a SEMON node harvests the energy released by the event and turns it into a radio pulse with an amplitude proportional to the harvested energy. A remote station is used to decode the amplitude of the pulse to recognize the event that has occurred. We propose two methods for the remote station to decode the events that have occurred. The first method is based on thresholds. The second method makes use of an event model that gives the probability that a sequence of events will occur. This enables us to formulate the decoding problem using Hidden Markov Models. We study the decoding performance of both methods. Finally, we provide a case study on using the SEMON architecture to monitor the chemical reactions inside a reactor.
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paper_title: Pedometers Without Batteries: An Energy Harvesting Shoe

paper_content:

The demand for thin, light wearables with long battery lifetimes motivate novel energy optimization techniques for a wide range of devices with applications ranging from telemedicine in clinical devices to activity recognition in consumer applications. In this paper, we present a simple activity monitoring system based on a piezoelectric energy-harvesting platform. The device requires no battery, no recharging, and no complex algorithms, instead leveraging the relationship between energy harvesting efficiency and user activity to create an effective wearable device with an indefinite lifetime. Experimental results confirm that the system can be used for coarse-grained activity monitoring with no external power source.
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paper_title: DoubleDip: leveraging thermoelectric harvesting for low power monitoring of sporadic water use

paper_content:

We present DoubleDip, a low power monitoring system for enabling non-intrusive water flow detection. DoubleDip taps into minute thermal gradients in pipes for both replenishing energy reserves and performing low power wakeup. One of the remaining issues with wireless water monitoring in residences and offices is that current solutions require installing sensor nodes with access to electrical wiring or replacing batteries frequently. DoubleDip (DD) significantly extends the lifetime of vibration-based non-intrusive water flow sensors by harvesting thermal energy from hot pipes wherever accessible. DoubleDip requires less than an inch of exposed metal pipe to attach a coupler for gathering sufficient energy to power itself, in some cases, into perpetuity. We observe that water use in homes and offices is incredibly sporadic, making continuous monitoring both impractical and wasteful. Instead, DD puts a thermoelectric harvester into double duty. It uses thermal gradients not only for gathering energy but also for extremely low power (
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paper_title: On the Design of a Solar-Panel Receiver for Optical Wireless Communications With Simultaneous Energy Harvesting

paper_content:

This paper proposes a novel design of an optical wireless communications (OWC) receiver using a solar panel as a photodetector. The proposed system is capable of simultaneous data transmission and energy harvesting. The solar panel can convert a modulated light signal into an electrical signal without any external power requirements. Furthermore, the direct current (DC) component of the modulated light can be harvested in the proposed receiver. The generated energy can potentially be used to power a user terminal or at least to prolong its operation time. The current work discusses the various parameters which need to be considered in the design of a system using a solar panel for simultaneous communication and energy harvesting. The presented theory is supported with an experimental implementation of orthogonal frequency division multiplexing (OFDM), thus, proving the validity of the analysis and demonstrating the feasibility of the proposed receiver. Using the propounded system, a communication link with a data rate of 11.84 Mbps is established for a received optical signal with a peak-to-peak amplitude of $\hbox{0.7}\times \hbox{10}^{-3}\ \hbox{W}/\hbox{cm}^{2}$ .
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paper_title: EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

paper_content:

A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users' lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.
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paper_title: SolarGest: Ubiquitous and Battery-free Gesture Recognition using Solar Cells

paper_content:

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.
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paper_title: Towards LuxTrace: Using Solar Cells to Measure Distance Indoors

paper_content:

Navigation for and tracking of humans within a building usually implies significant infrastructure investment and devices are usually too high in weight and volume to be integrated into garments. ::: ::: We propose a system that relies on existing infrastructure (so requires little infrastructure investment) and is based on a sensor that is low cost, low weight, low volume and can be manufactured to have similar characteristics to everyday clothing (flexible, range of colours). ::: ::: This proposed solution is based on solar modules. This paper investigates their theoretical and practical characteristics in a simplified scenario. Two models based on theory and on experimental results (empirical model) are developed and validated. ::: ::: First distance estimations indicate that an empirical model for a particular scenario achieves an accuracy of 18cm with a confidence of 83%.
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paper_title: Battery-free Visible Light Sensing

paper_content:

We present the design of the first Visible Light Sensing (VLS) system that consumes only tens of μWs of power to sense and communicate. Unlike most existing VLS systems, we require no modification to the existing light infrastructure since we use unmodulated light as a sensing medium. We achieve this by designing a novel mechanism that uses solar cells to achieve a sub-μW power consumption for sensing. Further, we devise an ultra-low power transmission mechanism that backscatters sensor readings and avoids the processing and computational overhead of existing sensor systems. Our initial results show the ability to detect and transmit hand gestures or presence of people up to distances of 330m at a peak power of μWs. Further, we demonstrate that our system can operate in diverse light conditions (100 lx to 80 klx) where existing VLS designs fail due to saturation of the transimpedance amplifier (TIA).
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paper_title: Gesture Recognition with Transparent Solar Cells: A Feasibility Study

paper_content:

Transparent solar cell is an emerging solar energy harvesting technology that allows us to see through these cells. This revolutionary discovery is creating unique opportunities to turn any mobile device screen into solar energy harvester. In this paper, we consider the possibility of using such energy harvesting screens as a sensor to detect hand gestures. As different gestures impact the incident light on the screen in a different way, they are expected to create unique energy generation patterns for the transparent solar cell. Our goal is to recognize gestures by detecting these solar energy patterns. A key uncertainty we face with transparent solar cell is that, to provide transparency, they cannot harvest from the visible spectra, which may lead to weaker energy patterns for the gestures. To study gesture recognition feasibility of transparent solar cell, we develop a 1cmx1cm organic see-through solar cell which provides high level of content visibility when placed on mobile phone screen. We then use the output current of the organic cell as the source signal for gesture pattern recognition using machine learning. Experimental results demonstrate that we can detect five hand gestures with average accuracies of 95%. We also compare gesture recognition accuracies of our prototype organic cell with those obtained from a conventional ceramic opaque solar cell, which reveals that organic solar cell can recognize some of these gestures almost as good as the opaque cells.
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paper_title: SunSpot: Exposing the Location of Anonymous Solar-powered Homes

paper_content:

Homeowners are increasingly deploying grid-tied solar systems due to the rapid decline in solar module prices. The energy produced by these solar-powered homes is monitored by utilities and third parties using networked energy meters, which record and transmit energy data at fine-grained intervals. Such energy data is considered anonymous if it is not associated with identifying account information, e.g., a name and address. Thus, energy data from these "anonymous" homes is often not handled securely: it is routinely transmitted over the Internet in plaintext, stored unencrypted in the cloud, shared with third-party energy analytics companies, and even made publicly available over the Internet. Extensive prior work has shown that energy consumption data is vulnerable to multiple attacks, which analyze it to reveal a range of sensitive private information about occupant activities. However, these attacks are useless without knowledge of a home's location. Our key insight is that solar energy data is not anonymous: since every location on Earth has a unique solar signature, it embeds detailed location information. To explore the severity and extent of this privacy threat, we design SunSpot to localize "anonymous" solar-powered homes using their solar energy data. We evaluate SunSpot on publicly-available energy data from 14 homes with rooftop solar. We find that SunSpot is able to localize a solar-powered home to a small region of interest that is near the smallest possible area given the energy data resolution, e.g., within a ~500m and ~28km radius for per-second and per-minute resolution, respectively. SunSpot then identifies solar-powered homes within this region using crowd-sourced image processing of satellite data before applying additional filters to identify a specific home.
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paper_title: LuxTrace: indoor positioning using building illumination

paper_content:

Tracking location is challenging due to the numerous constraints of practical systems including, but not limited to global cost, device volume and weight, scalability and accuracy; these constraints are typically more severe for systems that should be wearable and used indoors. We investigate the use of wearable solar cells to track changing light conditions (a concept that we named LuxTrace) as a source of user displacement and activity data. We evaluate constraints of this approach and present results from an experimental validation of displacement and activity estimation. The results indicate that a distance estimation accuracy of 21 cm (80% quantile) can be achieved. A simple method to combine LuxTrace with complementary absolute location estimation methods is also presented. We apply carpet-like distributed RFID tags to demonstrate online learning of new lighting environments.
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paper_title: Challenge: RFID Hacking for Fun and Profit

paper_content:

Passive radio frequency identification (RFID) tags are ubiquitous today due to their low cost (a few cents), relatively long communication range ($\sim$7-11~m), ease of deployment, lack of battery, and small form factor. Hence, they are an attractive foundation for environmental sensing. Although RFID-based sensors have been studied in the research literature and are also available commercially, manufacturing them has been a technically-challenging task that is typically undertaken only by experienced researchers. In this paper, we show how even hobbyists can transform commodity RFID tags into sensors by physically altering (`hacking') them using COTS sensors, a pair of scissors, and clear adhesive tape. Importantly, this requires no change to commercial RFID readers. We also propose a new legacy-compatible tag reading protocol called Differential Minimum Response Threshold (DMRT) that is robust to the changes in an RF environment. To validate our vision, we develop RFID-based sensors for illuminance, temperature, touch, and gestures. We believe that our approach has the potential to open up the field of batteryless backscatter-based RFID sensing to the research community, making it an exciting area for future work.
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paper_title: Static analysis and compiler design for idempotent processing

paper_content:

Recovery functionality has many applications in computing systems, from speculation recovery in modern microprocessors to fault recovery in high-reliability systems. Modern systems commonly recover using checkpoints. However, checkpoints introduce overheads, add complexity, and often save more state than necessary. This paper develops a novel compiler technique to recover program state without the overheads of explicit checkpoints. The technique breaks programs into idempotent regions---regions that can be freely re-executed---which allows recovery without checkpointed state. Leveraging the property of idempotence, recovery can be obtained by simple re-execution. We develop static analysis techniques to construct these regions and demonstrate low overheads and large region sizes for an LLVM-based implementation. Across a set of diverse benchmark suites, we construct idempotent regions close in size to those that could be obtained with perfect runtime information. Although the resulting code runs more slowly, typical performance overheads are in the range of just 2-12%. The paradigm of executing entire programs as a series of idempotent regions we call idempotent processing, and it has many applications in computer systems. As a concrete example, we demonstrate it applied to the problem of compiler-automated hardware fault recovery. In comparison to two other state-of-the-art techniques, redundant execution and checkpoint-logging, our idempotent processing technique outperforms both by over 15%.
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paper_title: Alpaca: intermittent execution without checkpoints

paper_content:

The emergence of energy harvesting devices creates the potential for batteryless sensing and computing devices. Such devices operate only intermittently, as energy is available, presenting a number of challenges for software developers. Programmers face a complex design space requiring reasoning about energy, memory consistency, and forward progress. This paper introduces Alpaca, a low-overhead programming model for intermittent computing on energy-harvesting devices. Alpaca programs are composed of a sequence of user-defined tasks. The Alpaca runtime preserves execution progress at the granularity of a task. The key insight in Alpaca is the privatization of data shared between tasks. Updates of shared values in a task are privatized and only committed to main memory on successful execution of the task, ensuring that data remain consistent despite power failures. Alpaca provides a familiar programming interface and a highly efficient runtime model. We also present an alternate version of Alpaca, Alpaca-undo, that uses undo-logging and rollback instead of privatization and commit. We implemented a prototype of both versions of Alpaca as an extension to C with an LLVM compiler pass. We evaluated Alpaca, and directly compared to three systems from prior work. Alpacaconsistently improves performance compared to the previous systems, by up to 23.8x, while also improving memory footprint in many cases, by up to 17.6x.
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paper_title: Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices

paper_content:

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.
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paper_title: Clank: Architectural Support for Intermittent Computation

paper_content:

The processors that drive embedded systems are getting smaller; meanwhile, the batteries used to provide power to those systems have stagnated. If we are to realize the dream of ubiquitous computing promised by the Internet of Things, processors must shed large, heavy, expensive, and high maintenance batteries and, instead, harvest energy from their environment. One challenge with this transition is that harvested energy is insufficient for continuous operation. Unfortunately, existing programs fail miserably when executed intermittently. This paper presents Clank: lightweight architectural support for correct and efficient execution of long-running applications on harvested energy---without programmer intervention or extreme hardware modifications. Clank is a set of hardware buffers and memory-access monitors that dynamically maintain idempotency. Essentially, Clank dynamically decomposes program execution into a stream of restartable sub-executions connected via lightweight checkpoints. To validate Clank's ability to correctly stretch program execution across frequent, random power cycles, and to explore the associated hardware and software overheads, we implement Clank in Verilog, formally verify it, and then add it to an ARM Cortex M0+ processor which we use to run a set of 23 embedded systems benchmarks. Experiments show run-time overheads as low as 2.5%, with run-time overheads of 6% for a version of Clank that adds 1.7% hardware. Clank minimizes checkpoints so much that re-execution time becomes the dominate contributor to run-time overhead.
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paper_title: A simpler, safer programming and execution model for intermittent systems

paper_content:

Energy harvesting enables novel devices and applications without batteries, but intermittent operation under energy harvesting poses new challenges to memory consistency that threaten to leave applications in failed states not reachable in continuous execution. This paper presents analytical models that aid in reasoning about intermittence. Using these, we develop DINO (Death Is Not an Option), a programming and execution model that simplifies programming for intermittent systems and ensures volatile and nonvolatile data consistency despite near-constant interruptions. DINO is the first system to address these consistency problems in the context of intermittent execution. We evaluate DINO on three energy-harvesting hardware platforms running different applications. The applications fail and exhibit error without DINO, but run correctly with DINO’s modest 1.8–2.7× run-time overhead. DINO also dramatically simplifies programming, reducing the set of possible failure- related control transfers by 5–9×.
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paper_title: QUICKRECALL: A Low Overhead HW/SW Approach for Enabling Computations across Power Cycles in Transiently Powered Computers

paper_content:

Transiently Powered Computers (TPCs) are a new class of batteryless embedded systems that depend solely on energy harvested from external sources for performing computations. Enabling long-running computations on TPCs is a major challenge due to the highly intermittent nature of the power supply (often bursts of <; 100ms), resulting in frequent system reboots. Prior work seeks to address this issue by frequently checkpointing system state in flash memory, preserving it across power cycles. However, this involves a substantial overhead due to the high erase/write times of flash memory. This paper proposes the use of FRAM, an emerging non-volatile memory technology that combines the benefits of SRAM and flash, to seamlessly enable long-running computations in TPCs. We propose a lightweight, in-situ checkpointing technique for TPCs using FRAM that decreases the time taken for saving and restoring a checkpoint to only 12.6μs, which is over two orders of magnitude lower than the corresponding overhead using flash. We have implemented and evaluated our technique, QUICKRECALL, using the TI MSP430FR5739 FRAM-enabled microcontroller. Experimental results show that our highly-efficient checkpointing translates to a significant speedup (1.4x - 4.5x) in program execution time.
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paper_title: Chain: tasks and channels for reliable intermittent programs

paper_content:

Energy harvesting computers enable general-purpose computing using energy collected from their environment. Energy-autonomy of such devices has great potential, but their intermittent power supply poses a challenge. Intermittent program execution compromises progress and leaves state inconsistent. This work describes Chain: a new model for programming intermittent devices. A Chain program is a set of programmer-defined tasks that compute and exchange data through channels. Chain guarantees forward progress at task granularity. A task is restartable and never sees inconsistent state, because its input and output channels are separated. Our system supports language features for expressing advanced data exchange patterns and for encapsulating reusable functionality. Chain fundamentally differs from state-of-the-art checkpointing approaches and does not incur the associated overhead. We implement Chain as C language extensions and a runtime library. We used Chain to implement four applications: machine learning, encryption, compression, and sensing. In experiments, Chain ensured consistency where prior approaches failed and improved throughput by 2-7x over the leading state-of-the-art system.
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paper_title: Idempotent code generation: Implementation, analysis, and evaluation

paper_content:

Leveraging idempotence for efficient recovery is of emerging interest in compiler design. In particular, identifying semantically idempotent code and then compiling such code to preserve the semantic idempotence property enables recovery with substantially lower overheads than competing software techniques. However, the efficacy of this technique depends on application-, architecture-, and compiler-specific factors that are not well understood. In this paper, we develop algorithms for the code generation of idempotent code regions and evaluate these algorithms considering how they are impacted by these factors. Without optimizing for these factors, we find that typical performance overheads fall in the range of roughly 10-15%. However, manipulating application idempotent region size typically improves the run-time performance of compiled code by 2-10%, differences in the architecture instruction set affect performance by up to 15%, and knowing in the compiler whether control flow side-effects can or cannot occur can impact performance by up to 10%. Overall, we find that, with small idempotent region and careful architecture- and application-specific tuning, it is possible to bring compiler performance overheads consistently down into the single-digit percentage range. The absolute best performance occurs when constructing the largest possible idempotent regions; to this end, however, better compiler support is needed. In the interest of spurring development in this area, we open-source our LLVM compiler implementation and make it available as a research tool.
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paper_title: Mementos: system support for long-running computation on RFID-scale devices

paper_content:

Transiently powered computing devices such as RFID tags, kinetic energy harvesters, and smart cards typically rely on programs that complete a task under tight time constraints before energy starvation leads to complete loss of volatile memory. Mementos is a software system that transforms general-purpose programs into interruptible computations that are protected from frequent power losses by automatic, energy-aware state checkpointing. Mementos comprises a collection of optimization passes for the LLVM compiler infrastructure and a linkable library that exercises hardware support for energy measurement while managing state checkpoints stored in nonvolatile memory. We evaluate Mementos against diverse test cases in a trace-driven simulator of transiently powered RFID-scale devices. Although Mementos's energy checks increase run time when energy is plentiful, they allow Mementos to safely suspend execution when energy dwindles, effectively spreading computation across zero or more power failures. This paper's contributions are: a study of the runtime environment for programs on RFID-scale devices; an energy-aware state checkpointing system for these devices that is implemented for the MSP430 family of microcontrollers; and a trace-driven simulator of transiently powered RFID-scale devices.
---
paper_title: Timely Execution on Intermittently Powered Batteryless Sensors

paper_content:

Tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades by leaving batteries behind and surviving off energy harvested from the environment--- avoiding the cost of replacing and disposing of billions or trillions of dead batteries. However, creating programs for these sensors is difficult. Energy harvesting is inconsistent, energy storage is scarce, and batteryless sensors can lose power at any point in time--- causing volatile memory, execution progress, and time to reset. In response to these disruptions, developers must write unwieldy programs attempting to protect against failures, instead of focusing on sensing goals, defining tasks, and generating useful data in a timely manner. To address these shortcomings, we have designed Mayfly, a language and runtime for timely execution of sensing tasks on tiny, intermittently-powered, energy harvesting sensing devices. Mayfly is a coordination language and runtime built on top of Embedded-C that combines intermittent execution fragments to form coherent sensing schedules---maintaining forward progress, data consistency, data freshness, and data utility across multiple power failures. Mayfly makes the passing of time explicit, binding data to the time it was gathered, and keeping track of data and time through power failures. We evaluated Mayfly against state-of-the art systems, conducted a user study, and implemented multiple real world applications across application domains in inventory tracking, and wearables.
---
paper_title: HarvOS: efficient code instrumentation for transiently-powered embedded sensing

paper_content:

We present code instrumentation strategies to allow transiently-powered embedded sensing devices efficiently checkpoint the system’s state before energy is exhausted. Our solution, called HarvOS, operates at compile-time with limited developer intervention based on the control-flow graph of a program, while adapting to varying levels of remaining energy and possible program executions at run-time. In addition, the underlying design rationale allows the system to spare the energy-intensive probing of the energy buffer whenever possible. Compared to existing approaches, our evaluation indicates that HarvOS allows transiently-powered devices to complete a given workload with 68% fewer checkpoints, on average. Moreover, our performance in the number of required checkpoints rests only 19% far from that of an “oracle” that represents an ideal solution, yet unfeasible in practice, that knows exactly the last point in time when to checkpoint.
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paper_title: Termination checking and task decomposition for task-based intermittent programs

paper_content:

Emerging energy-harvesting computer systems extract energy from their environment to compute, sense, and communicate with no battery or tethered power supply. Building software for energy-harvesting devices is a challenge, because they operate only intermittently as energy is available. Programs frequently reboot due to power loss, which can corrupt program state and prevent forward progress. Task-based programming models allow intermittent execution of long-running applications, but require the programmer to decompose code into tasks that will eventually complete between two power failures. Task decomposition is challenging and no tools exist to aid in task decomposition. We propose CleanCut, a tool that can check for and report non-terminating tasks in existing code, as well as automatically decompose code into efficient, terminating tasks. CleanCut is based on a statistical model for energy of paths through the program. We applied a prototype of CleanCut to four applications, including pattern-recognition, encryption, compression, and data filtering. Our experiments demonstrated the risk of non-termination in existing code and showed that CleanCut finds efficient task decompositions that execute 2.45x faster on average than manually placed boundaries.
---
paper_title: Persistent Clocks for Batteryless Sensing Devices

paper_content:

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.
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paper_title: Intermittent computation without hardware support or programmer intervention

paper_content:

As computation scales downward in area, the limitations imposed by the batteries required to power that computation become more pronounced. Thus, many future devices will forgo batteries and harvest energy from their environment. Harvested energy, with its frequent power cycles, is at odds with current models of long-running computation. ::: ::: To enable the correct execution of long-running applications on harvested energy--without requiring special-purpose hardware or programmer intervention--we propose Ratchet. Ratchet is a compiler that adds lightweight checkpoints to unmodified programs that allow existing programs to execute across power cycles correctly. Ratchet leverages the idea of idempotency, decomposing programs into a continuous stream of re-executable sections connected by lightweight checkpoints, stored in non-volatile memory. We implement Ratchet on top of LLVM, targeted at embedded systems with high-performance non-volatile main memory. Using eight embedded systems benchmarks, we show that Ratchet correctly stretches program execution across frequent, random power cycles. Experimental results show that Ratchet enables a range of existing programs to run on intermittent power, with total run-time overhead averaging below 60%--comparable to approaches that require hardware support or programmer intervention.
---
paper_title: Hibernus: Sustaining Computation During Intermittent Supply for Energy-Harvesting Systems

paper_content:

A key challenge to the future of energy-harvesting systems is the discontinuous power supply that is often generated. We propose a new approach, Hibernus, which enables computation to be sustained during intermittent supply. The approach has a low energy and time overhead which is achieved by reactively hibernating: saving system state only once, when power is about to be lost, and then sleeping until the supply recovers. We validate the approach experimentally on a processor with FRAM nonvolatile memory, allowing it to reactively hibernate using only energy stored in its decoupling capacitance. When compared to a recently proposed technique, the approach reduces processor time and energy overheads by 76%–100% and 49%–79% respectively.
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paper_title: Clank: Architectural Support for Intermittent Computation

paper_content:

The processors that drive embedded systems are getting smaller; meanwhile, the batteries used to provide power to those systems have stagnated. If we are to realize the dream of ubiquitous computing promised by the Internet of Things, processors must shed large, heavy, expensive, and high maintenance batteries and, instead, harvest energy from their environment. One challenge with this transition is that harvested energy is insufficient for continuous operation. Unfortunately, existing programs fail miserably when executed intermittently. This paper presents Clank: lightweight architectural support for correct and efficient execution of long-running applications on harvested energy---without programmer intervention or extreme hardware modifications. Clank is a set of hardware buffers and memory-access monitors that dynamically maintain idempotency. Essentially, Clank dynamically decomposes program execution into a stream of restartable sub-executions connected via lightweight checkpoints. To validate Clank's ability to correctly stretch program execution across frequent, random power cycles, and to explore the associated hardware and software overheads, we implement Clank in Verilog, formally verify it, and then add it to an ARM Cortex M0+ processor which we use to run a set of 23 embedded systems benchmarks. Experiments show run-time overheads as low as 2.5%, with run-time overheads of 6% for a version of Clank that adds 1.7% hardware. Clank minimizes checkpoints so much that re-execution time becomes the dominate contributor to run-time overhead.
---
paper_title: Mementos: system support for long-running computation on RFID-scale devices

paper_content:

Transiently powered computing devices such as RFID tags, kinetic energy harvesters, and smart cards typically rely on programs that complete a task under tight time constraints before energy starvation leads to complete loss of volatile memory. Mementos is a software system that transforms general-purpose programs into interruptible computations that are protected from frequent power losses by automatic, energy-aware state checkpointing. Mementos comprises a collection of optimization passes for the LLVM compiler infrastructure and a linkable library that exercises hardware support for energy measurement while managing state checkpoints stored in nonvolatile memory. We evaluate Mementos against diverse test cases in a trace-driven simulator of transiently powered RFID-scale devices. Although Mementos's energy checks increase run time when energy is plentiful, they allow Mementos to safely suspend execution when energy dwindles, effectively spreading computation across zero or more power failures. This paper's contributions are: a study of the runtime environment for programs on RFID-scale devices; an energy-aware state checkpointing system for these devices that is implemented for the MSP430 family of microcontrollers; and a trace-driven simulator of transiently powered RFID-scale devices.
---
paper_title: HarvOS: efficient code instrumentation for transiently-powered embedded sensing

paper_content:

We present code instrumentation strategies to allow transiently-powered embedded sensing devices efficiently checkpoint the system’s state before energy is exhausted. Our solution, called HarvOS, operates at compile-time with limited developer intervention based on the control-flow graph of a program, while adapting to varying levels of remaining energy and possible program executions at run-time. In addition, the underlying design rationale allows the system to spare the energy-intensive probing of the energy buffer whenever possible. Compared to existing approaches, our evaluation indicates that HarvOS allows transiently-powered devices to complete a given workload with 68% fewer checkpoints, on average. Moreover, our performance in the number of required checkpoints rests only 19% far from that of an “oracle” that represents an ideal solution, yet unfeasible in practice, that knows exactly the last point in time when to checkpoint.
---
paper_title: Intermittent computation without hardware support or programmer intervention

paper_content:

As computation scales downward in area, the limitations imposed by the batteries required to power that computation become more pronounced. Thus, many future devices will forgo batteries and harvest energy from their environment. Harvested energy, with its frequent power cycles, is at odds with current models of long-running computation. ::: ::: To enable the correct execution of long-running applications on harvested energy--without requiring special-purpose hardware or programmer intervention--we propose Ratchet. Ratchet is a compiler that adds lightweight checkpoints to unmodified programs that allow existing programs to execute across power cycles correctly. Ratchet leverages the idea of idempotency, decomposing programs into a continuous stream of re-executable sections connected by lightweight checkpoints, stored in non-volatile memory. We implement Ratchet on top of LLVM, targeted at embedded systems with high-performance non-volatile main memory. Using eight embedded systems benchmarks, we show that Ratchet correctly stretches program execution across frequent, random power cycles. Experimental results show that Ratchet enables a range of existing programs to run on intermittent power, with total run-time overhead averaging below 60%--comparable to approaches that require hardware support or programmer intervention.
---
paper_title: Timely Execution on Intermittently Powered Batteryless Sensors

paper_content:

Tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades by leaving batteries behind and surviving off energy harvested from the environment--- avoiding the cost of replacing and disposing of billions or trillions of dead batteries. However, creating programs for these sensors is difficult. Energy harvesting is inconsistent, energy storage is scarce, and batteryless sensors can lose power at any point in time--- causing volatile memory, execution progress, and time to reset. In response to these disruptions, developers must write unwieldy programs attempting to protect against failures, instead of focusing on sensing goals, defining tasks, and generating useful data in a timely manner. To address these shortcomings, we have designed Mayfly, a language and runtime for timely execution of sensing tasks on tiny, intermittently-powered, energy harvesting sensing devices. Mayfly is a coordination language and runtime built on top of Embedded-C that combines intermittent execution fragments to form coherent sensing schedules---maintaining forward progress, data consistency, data freshness, and data utility across multiple power failures. Mayfly makes the passing of time explicit, binding data to the time it was gathered, and keeping track of data and time through power failures. We evaluated Mayfly against state-of-the art systems, conducted a user study, and implemented multiple real world applications across application domains in inventory tracking, and wearables.
---
paper_title: TARDIS: Time and Remanence Decay in SRAM to Implement Secure Protocols on Embedded Devices without Clocks

paper_content:

Lack of a locally trustworthy clock makes security protocols challenging to implement on batteryless embedded devices such as contact smartcards, contactless smartcards, and RFID tags. A device that knows how much time has elapsed between queries from an untrusted reader could better protect against attacks that depend on the existence of a rate-unlimited encryption oracle. ::: ::: The TARDIS (Time and Remanence Decay in SRAM) helps locally maintain a sense of time elapsed without power and without special-purpose hardware. The TARDIS software computes the expiration state of a timer by analyzing the decay of existing on-chip SRAM. The TARDIS enables coarse-grained, hourglass-like timers such that cryptographic software can more deliberately decide how to throttle its response rate. Our experiments demonstrate that the TARDIS can measure time ranging from seconds to several hours depending on hardware parameters. Key challenges to implementing a practical TARDIS include compensating for temperature and handling variation across hardware. ::: ::: Our contributions are (1) the algorithmic building blocks for computing elapsed time from SRAM decay; (2) characterizing TARDIS behavior under different temperatures, capacitors, SRAM sizes, and chips; and (3) three proof-of-concept implementations that use the TARDIS to enable privacy-preserving RFID tags, to deter double swiping of contactless credit cards, and to increase the difficulty of brute-force attacks against e-passports.
---
paper_title: Persistent Clocks for Batteryless Sensing Devices

paper_content:

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.
---
paper_title: Advances in Energy Harvesting Communications: Past, Present, and Future Challenges

paper_content:

Recent emphasis on green communications has generated great interest in the investigations of energy harvesting communications and networking. Energy harvesting from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many attractive benefits to the environment and deployment. However, unlike the conventional stable energy, the intermittent and random nature of the renewable energy makes it challenging in the realization of energy harvesting transmission schemes. Extensive research studies have been carried out in recent years to address this inherent challenge from several aspects: energy sources and models, energy harvesting and usage protocols, energy scheduling and optimization, implementation of energy harvesting in cooperative, cognitive radio, multiuser and cellular networks, etc. However, there has not been a comprehensive survey to lay out the complete picture of recent advances and future directions. To fill such a gap, in this paper, we present an overview of the past and recent developments in these areas and highlight a number of possible future research avenues.
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paper_title: Designing intelligent energy harvesting communication systems

paper_content:

From being a scientific curiosity only a few years ago, energy harvesting (EH) is well on its way to becoming a game-changing technology in the field of autonomous wireless networked systems. The promise of long-term, uninterrupted and self-sustainable operation in a diverse array of applications has captured the interest of academia and industry alike. Yet the road to the ultimate network of perpetual communicating devices is plagued with potholes: ambient energy is intermittent and scarce, energy storage capacity is limited, and devices are constrained in size and complexity. In dealing with these challenges, this article will cover recent developments in the design of intelligent energy management policies for EH wireless devices and discuss pressing research questions in this rapidly growing field.
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paper_title: A survey of energy harvesting communications: models and offline optimal policies

paper_content:

As people pay more attention to environmental protection and energy conservation issues, energy consumption in communications have become a hot research field. In wireless communications networks such as wireless sensor networks, traditional battery-operated devices or nodes have a short lifetime and die after the batteries are depleted, and replacing the batteries may be very costly and sometimes will be impossible. Therefore, energy harvesting (EH) communications have become a good means to solve this problem. EH communications mean the nodes can continue working by harvesting ambient energy. EH communications are different from the traditional battery-operated communications, so we need new models and optimal transmission policies to maximize the throughput. In this article we review different methods of harvesting the ambient energy in EH communications and the models of EH communications. We focus on offline optimal policies, then compare different policies and classify them into certain types. Finally, we propose several open research challenges and directions for future work.
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paper_title: Reinforcement Learning Based Multi-Access Control with Energy Harvesting

paper_content:

In this paper, we study an uplink wireless system including Ν energy harvesting (EH) user equipments (UEs) and one base station (BS) with limited access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. To handle such an access control problem, we propose a scheduling algorithm that maximizes the expected sum discounted uplink transmission rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Different from the traditional access control solutions that usually assume strong model knowledges, our goal is to achieve a more stable and balanced transmission over a long time horizon in a data-driven fashion. Finally, experiment results show that the proposed RL algorithm can achieve better performances compared with existing benchmarks.
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paper_title: Reinforcement learning in MIMO wireless networks with energy harvesting

paper_content:

Energy harvesting wireless nodes provide much longer lifetime and higher energy efficiency for wireless networks compared to battery operated systems. In this paper, we study a MIMO wireless communication link in which the nodes are equipped with energy harvesters and rechargeable batteries that are continuously charging from a renewable energy source. Since the harvested energy arrival and thus the future remaining energy of the nodes is not deterministic in practice, we propose a learning approach in order to find the most efficient transmission policy for data communication that maximizes throughput. The problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities. A Q-Learning approach is proposed to solve the MDP model and find the optimal transmission policy.
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paper_title: Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting

paper_content:

Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy harvesting provide satisfactory quality of experiences for computation intensive applications. We propose a reinforcement learning (RL)-based privacy-aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy, and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption, and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. A post-decision state learning method uses the known channel state model to further improve the offloading performance. We provide the performance bound of this scheme regarding the privacy level, the energy consumption, and the computation latency for three typical healthcare IoT offloading scenarios. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme.
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paper_title: Action-Bounding for Reinforcement Learning in Energy Harvesting Communication Systems

paper_content:

In this paper, we consider a power allocation problem for energy harvesting communication systems, where a transmitter wants to send the desired messages to the receiver with the harvested energy in its rechargeable battery. We propose a new power allocation strategy based on deep reinforcement learning technique to maximize the expected total transmitted data for a given random energy arrival and random channel process. The key idea of our scheme is to lead the transmitter, rather than learning the undesirable power allocation policies, by an action-bounding technique using only causal knowledge of the energy and channel processes. This technique helps traditional reinforcement learning algorithms to work more accurately in the systems, and increases the performance of the learning algorithms. Moreover, we show that the proposed scheme achieves better performance with respect to the expected total transmitted data compared to existing power allocation strategies.
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paper_title: Learning to survive: Achieving energy neutrality in wireless sensor networks using reinforcement learning

paper_content:

Energy harvesting is a promising approach to enable autonomous long-life wireless sensor networks. As typical energy sources present time-varying behavior, each node embeds an energy manager, which dynamically adapts the power consumption of the node to maximize the quality of service, while preventing power failure. In this work, RLMan, a novel energy management scheme based on reinforcement learning theory, is proposed. RLMan dynamically adapts its policy to time-varying environment by continuously exploring, while exploiting the current knowledge to improve the quality of service. The proposed energy management scheme has a very low memory footprint, and requires very few computational power, which makes it suitable for online execution on sensor nodes. Moreover, it only necessitates the state of charge of the energy storage device as an input, and therefore is practical to implement. RLMan was compared to three state-of-the-art energy management schemes, using simulations and energy traces from real measurements. Results show that using RLMan can enable almost 70 % gains regarding the average throughput.
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paper_title: A Fuzzy Q-learning Based Power Management for Energy Harvest Wireless Sensor Node

paper_content:

In this study, a fuzzy Q-learning is used for the power management of energy harvest wireless sensor node(EHWSN). The proposed method establishes a fuzzy inference system (FIS) for the power management of energy harvest wireless sensor node and employs Q-learning in evaluating and updating the rule base. By interacting with the environment of the energy harvest wireless sensor node, the learning agent of the fuzzy Q-Learning is able to obtain the strategy in updating the fuzzy rule base and exercising the decided sensing duty cycle of the wireless sensor node such that energy neutrality of the EHWSN can be achieved. Experiment results shows that the proposed fuzzy Q-learning based power management can obtain better battery charge status in comparing other existing methods and achieve sustainable operation for EHWSN.
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paper_title: A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learning

paper_content:

Traditional wireless sensor networks (WSNs) face the problem of a limited-energy source, typically batteries, resulting in the need for careful and effective utilization of the energy source. However, inevitable energy depletion will eventually disturb the operation of a WSN. Energy harvesting (EH) technology is acquiring particular interest, because it has the potential to provide a continuous energy supply in battery-powered WSNs. Solar energy is the most effective environmental energy for EH-WSNs because of its high energy intensity, which comes from a non-controllable source. Therefore, the prediction of future energy availability is a critical issue, as the amount of the harvestable energy may vary over time. In this paper, a novel solar energy prediction algorithm with Q-learning, called Q-learning-based solar energy prediction (QL-SEP), is proposed. Q-learning is an effective way of predicting future actions based on past observations. The distinctive feature of QL-SEP is that not only past days’ observations but also the current weather conditions are considered for prediction. The performance of QL-SEP is simulated in this paper using real-world measurements obtained from a solar panel in comparison with the state-of-art approaches.
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paper_title: Sensing, Probing, and Transmitting Policy for Energy Harvesting Cognitive Radio With Two-Stage After-State Reinforcement Learning

paper_content:

This paper considers joint optimization of spectrum sensing, channel probing, and transmission power control for a single-channel secondary transmitter that operates with harvested energy from ambient sources. At each time slot, to maximize the expected secondary throughput, the transmitter needs to decide whether or not to perform the operations of spectrum sensing, channel probing, and transmission, according to energy status and channel fading status. First, we model this stochastic optimization problem as a two-stage continuous-state Markov decision process, with a sensing-and-probing stage and a transmit-power-control stage. We simplify this problem by a more useful after-state value function formulation. We then propose a reinforcement learning algorithm to learn the after-state value function from data samples when the statistical distributions of harvested energy and channel fading are unknown. Numerical results demonstrate learning characteristics and performance of the proposed algorithm.
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paper_title: Reinforcement learning for energy harvesting point-to-point communications

paper_content:

Energy harvesting point-to-point communications are considered. The transmitter harvests energy from the environment and stores it in a finite battery. It is assumed that the transmitter has always data to transmit and the harvested energy is used exclusively for data transmission. As in practical scenarios prior knowledge about the energy harvesting process might not be available, we assume that at each time instant only information about the current state of the transmitter is available, i.e., harvested energy, battery level and channel coefficient. We model the scenario as a Markov decision process and we implement reinforcement learning at the transmitter to find a power allocation policy that aims at maximizing the throughput. To overcome the limitations of traditional reinforcement learning algorithms, we apply the concept of function approximation and we propose a set of binary functions to approximate the expected throughput given the state of the transmitter. Numerical results show that the performance of the proposed approach, which requires only causal knowledge of the energy harvesting process and channel coefficients, has only a small degradation compared to the optimum case which requires perfect non-causal knowledge. Additionally, the proposed approach outperforms naive policies that assume only causal knowledge at the transmitter.
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paper_title: Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing

paper_content:

Mobile edge computing (also known as fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as ${Q}$ -learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.
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paper_title: Adaptive Power Management in Solar Energy Harvesting Sensor Node Using Reinforcement Learning

paper_content:

In this paper, we present an adaptive power manager for solar energy harvesting sensor nodes. We use a simplified model consisting of a solar panel, an ideal battery and a general sensor node with variable duty cycle. Our power manager uses Reinforcement Learning (RL), specifically SARSA(λ) learning, to train itself from historical data. Once trained, we show that our power manager is capable of adapting to changes in weather, climate, device parameters and battery degradation while ensuring near-optimal performance without depleting or overcharging its battery. Our approach uses a simple but novel general reward function and leverages the use of weather forecast data to enhance performance. We show that our method achieves near perfect energy neutral operation (ENO) with less than 6% root mean square deviation from ENO as compared to more than 23% deviation that occur when using other approaches.
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paper_title: User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

paper_content:

Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.
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paper_title: Switch-On/Off Policies for Energy Harvesting Small Cells through Distributed Q-Learning

paper_content:

The massive deployment of small cells (SCs) represents one of the most promising solutions adopted by 5G cellular networks to meet the foreseen huge traffic demand. The high number of network elements entails a significant increase in the energy consumption. The usage of renewable energies for powering the small cells can help reduce the environmental impact of mobile networks in terms of energy consumption and also save on electric bills. In this paper, we consider a two-tier cellular network architecture where SCs can offload macro base stations and solely rely on energy harvesting and storage. In order to deal with the erratic nature of the energy arrival process, we exploit an ON/OFF switching algorithm, based on reinforcement learning, that autonomously learns energy income and traffic demand patterns. The algorithm is based on distributed multi-agent Q-learning for jointly optimizing the system performance and the self-sustainability of the SCs. We analyze the algorithm by assessing its convergence time, characterizing the obtained ON/OFF policies, and evaluating an offline trained variant. Simulation results demonstrate that our solution is able to increase the energy efficiency of the system with respect to simpler approaches. Moreover, the proposed method provides an harvested energy surplus, which can be used by mobile operators to offer ancillary services to the smart electricity grid.
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paper_title: Reinforcement Learning for Scheduling Wireless Powered Sensor Communications

paper_content:

In a wireless powered sensor network, a base station transfers power to sensors by using wireless power transfer (WPT). Inadequately scheduling WPT and data transmission causes fast battery drainage and data queue overflow of some sensors who could have potentially gained high data reception. In this paper, scheduling WPT and data transmission is formulated as a Markov decision process (MDP) by jointly considering sensors' energy consumption and data queue. In practical scenarios, the prior knowledge about battery level and data queue length in MDP is not available at the base station. We study reinforcement learning at the sensors to find a transmission scheduling strategy, minimizing data packet loss. An optimal scheduling strategy with full-state information is also investigated, assuming that the complete battery level and data queue information are well known by the base station. This presents the lower bound of the data packet loss in wireless powered sensor networks. Numerical results demonstrate that the proposed reinforcement learning scheduling algorithm significantly reduces network packet loss rate by 60%, and increases network goodput by 67%, compared to existing non-MDP greedy approaches. Moreover, comparing the optimal solutions, the performance loss due to the lack of sensors' full-state information is less than 4.6%.
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paper_title: Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems

paper_content:

Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of Things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep ${Q}$ -network enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop an RL-based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.
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paper_title: Online Learning Schemes for Power Allocation in Energy Harvesting Communications

paper_content:

We consider the problem of power allocation over a time-varying channel with unknown distribution in energy harvesting communication systems. In this problem, the transmitter has to choose the transmit power based on the amount of stored energy in its battery with the goal of maximizing the average rate obtained over time. We model this problem as a Markov decision process (MDP) with the transmitter as the agent, the battery status as the state, the transmit power as the action and the rate obtained as the reward. The average reward maximization problem over the MDP can be solved by a linear program (LP) that uses the transition probabilities for the state-action pairs and their reward values to choose a power allocation policy. Since the rewards associated the state-action pairs are unknown, we propose two online learning algorithms: UCLP and Epoch-UCLP that learn these rewards and adapt their policies along the way. The UCLP algorithm solves the LP at each step to decide its current policy using the upper confidence bounds on the rewards, while the Epoch-UCLP algorithm divides the time into epochs, solves the LP only at the beginning of the epochs and follows the obtained policy in that epoch. We prove that the reward losses or regrets incurred by both these algorithms are upper bounded by constants. Epoch-UCLP incurs a higher regret compared to UCLP, but reduces the computational requirements substantially. We also show that the presented algorithms work for online learning in cost minimization problems like the packet scheduling with power-delay tradeoff with minor changes.
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paper_title: Reinforcement Learning for Energy Harvesting Decode-and-Forward Two-Hop Communications

paper_content:

Energy harvesting (EH) two-hop communications are considered. The transmitter and the relay harvest energy from the environment and use it exclusively for transmitting data. A data arrival process is assumed at the transmitter. At the relay, a finite data buffer is used to store the received data. We consider a realistic scenario in which the EH nodes have only local causal knowledge, i.e., at any time instant, each EH node only knows the current value of its EH process, channel state, and data arrival process. Our goal is to find a power allocation policy to maximize the throughput at the receiver. We show that because the EH nodes have local causal knowledge, the two-hop communication problem can be separated into two point-to-point problems. Consequently, independent power allocation problems are solved at each EH node. To find the power allocation policy, reinforcement learning with linear function approximation is applied. Moreover, to perform function approximation two feature functions which consider the data arrival process are introduced. Numerical results show that the proposed approach has only a small degradation as compared to the offline optimum case. Furthermore, we show that with the use of the proposed feature functions a better performance is achieved compared to standard approximation techniques.
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paper_title: Adaptive Rate and Energy Harvesting Interval Control Based on Reinforcement Learning for SWIPT

paper_content:

In this letter, we propose a new adaptive rate and energy harvesting interval control scheme to maximize the throughout subject to the average energy constraint in the multiple-input single-output simultaneous wireless information and power transfer system. We consider the realistic scenario of time-varying fading channel. In order to maximize the throughput and simultaneously to maintain the average energy required at the receiver, we first formulate a problem of jointly optimizing the rate and energy harvesting interval based on a Markov decision process (MDP) by using a regularization parameter. However, this MDP problem is difficult to directly solve because the channel transition probabilities (i.e., the model or the environment) are challenging to estimate in the practical systems. Thus, we propose an adaptive rate and energy harvesting interval control algorithm based on the model-free reinforcement learning technique. Numerical results demonstrate that the proposed scheme significantly outperforms the conventional scheme.
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paper_title: Reinforcement Learning Exploration Algorithms for Energy Harvesting Communications Systems

paper_content:

Prolonging the lifetime, and maximizing the throughput are important factors in designing an efficient communications system, especially for energy harvesting-based systems. In this work, the problem of maximizing the throughput of point-to- point energy harvesting communications system, while prolonging its lifetime is investigated. This work considers more real communications system, where this system does not have a priori knowledge about the environment. This system consists of a transmitter and receiver. The transmitter is equipped with an infinite buffer to store data, and energy harvesting capability to harvest renewable energy and store it in a finite battery. The problem of finding an efficient power allocation policy is formulated as a reinforcement learning problem. Two different exploration algorithms are used, which are the convergence- based and the epsilon-greedy algorithms. The first algorithm uses the action-value function convergence error and the exploration time threshold to balance between exploration and exploitation. On the other hand, the second algorithm tries to achieve balancing through the exploration probability (i.e. epsilon). Simulation results show that the convergence-based algorithm outperforms the epsilon-greedy algorithm. Then, the effects of the parameters of each algorithm are investigated.
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paper_title: Optimal Status Update for Age of Information Minimization With an Energy Harvesting Source

paper_content:

In this paper, we consider a scenario where an energy harvesting sensor continuously monitors a system and sends time-stamped status updates to a destination. The destination keeps track of the system status through the received updates. We use the metric Age of Information (AoI), the time that has elapsed since the last received update was generated, to measure the “freshness” of the status information available at the destination. Our objective is to design optimal online status update policies to minimize the long-term average AoI, subject to the energy causality constraint at the sensor. We consider three scenarios, i.e., the battery size is infinite, finite, and one unit only, respectively. For the infinite battery scenario, we adopt a best-effort uniform status update policy and show that it minimizes the long-term average AoI. For the finite battery scenario, we adopt an energy-aware adaptive status update policy, and prove that it is asymptotically optimal when the battery size goes to infinity. For the last scenario where the battery size is one, we first show that within a broadly defined class of online policies, the optimal policy should have a renewal structure. We then focus on a renewal interval, and prove that the optimal policy should have a threshold structure, i.e., if the AoI in the system is below a threshold when an energy arrival enters an empty battery, the sensor should store the energy first and then update when the AoI reaches the threshold; otherwise, it updates the status immediately. Simulation results corroborate the theoretical bounds.
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paper_title: A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks

paper_content:

In this paper, a reinforcement learning-based sleep scheduling for coverage (RLSSC) algorithm is proposed for sustainable time-slotted operation in rechargeable sensor networks. RLSSC is a two-stage sleep scheduling algorithm. It includes the precedence operator-based group formation algorithm and the Q learning-based active node selection algorithm. First, a precedence operator is designed in the group formation algorithm to ensure the desired area coverage. All the nodes are formed into groups. Then, Q learning algorithm is expanded into a multi-sensor cooperation Q learning group model. The learning and action selection strategy are designed in a group to direct nodes in collaborative learning of working modes selection while adapting to the dynamic environment. Through the role changes of active nodes, the algorithm accomplishes the entire team learning of sleep scheduling while scheduling others into sleep modes. Experiments of RLSSC on a solar-powered wireless sensor network for area surveillance tasks are presented. Compared with LEACH and a random algorithm, the results show that RLSSC can effectively adjust the working modes of nodes in a group by perceiving the environment. In addition, it achieves the energy consumption balance between nodes so as to prolong the network lifetime while maintaining the desired coverage.
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paper_title: Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

paper_content:

Internet of Things (IoT) devices can apply mobile-edge computing (MEC) and energy harvesting (EH) to provide the satisfactory quality of experiences for computation intensive applications and prolong the battery lifetime. In this article, we investigate the computation offloading for IoT devices with energy harvesting in wireless networks with multiple MEC devices such as base stations and access points, each with different computation resource and radio communication capability. We propose a reinforcement learning based computation offloading framework for an IoT device to choose the MEC device and determine the offloading rate according to the current battery level, the previous radio bandwidth to each MEC device and the predicted amount of the harvested energy. A"hotbooting"Q-learning based computation offloading scheme is proposed for an IoT device to achieve the optimal offloading performance without being aware of the MEC model, the energy consumption and computation latency model. We also propose a fast deep Q-network (DQN) based offloading scheme, which combines the deep learning and hotbooting techniques to accelerate the learning speed of Q-learning. We show that the proposed schemes can achieve the optimal offloading policy after sufficiently long learning time and provide their performance bounds under two typical MEC scenarios. Simulations are performed for IoT devices that use wireless power transfer to capture the ambient radio-frequency signals to charge the IoT batteries. Simulation results show that the fast DQN-based offloading scheme reduces the energy consumption, decreases the computation delay and the task drop ratio, and increases the utility of the IoT device in dynamic MEC, compared with the benchmark Q-learning based offloading.
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paper_title: eNEUTRAL IoNT: Energy-Neutral Event Monitoring for Internet of Nano Things

paper_content:

Advancements in nanotechnology promise new capabilities for the Internet of Things (IoT) to monitor extremely fine-grained events with sensors as small as a hundred nanometers. Researchers predict that such tiny sensors can be connected to the Internet using graphene-based nano-antenna radiating in the terahertz band, giving rise to the so-called Internet of Nano-Things (IoNT). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. Since in many application domains, different types of events discharge different amounts of energy to the environment, we propose an energy-neutral event monitoring framework, called eNEUTRAL IoNT, that allows the sensors to transmit event information using only the amount of energy harvested from the events. We design and analyze two implementation options for this framework. The first option uses a single pulse containing the entire energy harvested from the event but manipulates its time duration to create a unique pulse amplitude for a given combination of event type and its location. In the second option, the harvested event energy is divided into two pulses so that the energy of the first pulse uniquely defines a location and the second pulse uses the remaining energy to identify event types. To minimize classification error at the receiver, we optimize pulse durations in the single-pulse option and pulse energies in the dual-pulse option. Feasibility of eNEUTRAL IoNT is demonstrated using extensive numerical experiments involving terahertz channels. We find that the dual-pulse approach significantly outperforms the single-pulse approach achieving 99% accuracy for detecting both location and event type in 10-node network monitoring two different event types for a radius of 28 mm.
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paper_title: Direction of Arrival and Center Frequency Estimation for Impulse Radio Millimeter Wave Communications

paper_content:

The 30-300GHz millimeter wave (mmWave) band is currently being pursued to combat the rising capacity demands in 5G, WiFi, and IoT networks. Due to the high frequency, impulse radio (IR) in this band is better suited for positioning than other existing low-frequency bands. Besides precision positioning, the exceptionally wide bandwidth also enables concurrent use of multiple center frequencies in the same application, which opens up additional avenues of information encoding in IR mmWave networks. In this paper, we propose a new mmWave IR framework that can simultaneously detect direction of arrival (DOA) as well as the center frequency of the transmitted pulse. Based on the emerging graphene-based transceivers, we evaluate the performance of the proposed framework in the higher frequency region of mmWave band (100-300GHz). Numerical experiments demonstrate that the proposed framework can detect the DOA of a 0.1 $\mu$Watt mmWave pulse within 1 degree of precision at 20 meters, and classify three different center frequencies with 100% accuracy from a distance of 10 meters. These performances could be further improved by trading off the pulse rate of the system.
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paper_title: Towards packet-less ultrasonic sensor networks for energy-harvesting structures

paper_content:

This paper proposes and evaluates an energy-aware pulse switching architecture for a through-substrate ultrasonic sensor network. The network is run from vibration energy harvested on an airplane stabilizer structure, whose health is monitored using the through-substrate network. Pulse- switching-based protocols use single pulses instead of multi-bit packets for information delivery with ultra-high energy-efficiency. Pulse switching using ultrasound is particularly well suited for event reporting through metal / composite substrates used in structures such as bridges, aircraft wings, etc. This can eliminate the need for out-of-substrate radio or wired links. This paper presents a large-scale simulation model in which structural vibration modeling using finite element methods, energy harvesting modeling from such vibrations, and energy-aware pulse networking models are integrated for end-to-end architecture level performance evaluation. Simulation results are used for demonstrating the sensitivity of network performance to key system parameters, namely, structural vibration intensity, energy harvesting efficiency of the used piezoelectric material, and the energy storage capacity at the pulse switching sensor nodes. In addition to event reporting delay, the impacts of pulse loss have been thoroughly characterized using the integrated simulator.
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paper_title: Classifying the Order of Higher Derivative Gaussian Pulses in Terahertz Wireless Communications

paper_content:

The terahertz band is considered the last frontier for wireless communications and expected to play a significant role in beyond 5G networks. Besides supporting extremely high data rates for existing devices, the terahertz band is also expected to connect future nanoscale devices using graphene-based nano-antenna, which happens to radiate in 0.1-10 THz band. In this band, higher order derivatives of Gaussian pulses can provide energy-efficient communication for nanodevices. In this paper, we propose a metric, called root mean square (RMS) frequency spread, to detect the derivative order of the pulse at the receiving base station using uniform linear array antennas. Simulation experiments demonstrate that RMS frequency spread can be used to detect the derivative order of Gaussian pulses with 99% accuracy for distances up to 50 cm. This finding opens up a new design space for nanoscale terahertz communication, which can encode information in time derivative order of the transmitted pulse.
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paper_title: Energy Efficient Event Localization and Classification for Nano IoT

paper_content:

Advancements in nanotechnology promises new capabilities for Internet of Things (IoT) to monitor extremely fine-grained events by deploying sensors as small as a few hundred nanometers. Researchers predict that such tiny sensors can transmit wireless data using graphene-based nano-antenna radiating in the terahertz band (0.1-10 THz). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. In this paper, we propose an energy efficient event monitoring framework for nano IoT that enables nanosensors to update a remote base station about the location and type of the detected event using only a single short pulse. Nanosensors encode different events using different center frequencies with non overlapping half power bandwidth over the entire terahertz band. Using uniform linear array (ULA) antenna, the base station localizes the events by estimating the direction of arrival of the pulse and classifies them from the center frequency estimated by spectral centroid of the received signal. Simulation results confirm that, from a distance of 1 meter, a 6th derivative Gaussian pulse consuming only 1 atto Joule can achieve localization and classification accuracies of 1.58 degree and 98.8%, respectively.
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paper_title: On the Nature of Energy-Feasible Wireless Nanosensor Networks

paper_content:

Electromagnetic nanocommunications, understood as the communication between electronic nanoscale devices through electromagnetic waves in the terahertz band, has attracted increasing attention in recent years. In this regard, several solutions have already been proposed. However, many of them do not sufficiently capture the significance of the limitations in nanodevice energy-gathering and storing capacity. In this paper, we address key factors affecting the energy consumption of nanodevices, highlighting the effect of the communication scheme employed. Then, we also examine how nanodevices are powered, focusing on the main parameters governing the powering nanosystem. Different mathematical expressions are derived to analyze the impact of these parameters on its performance. Based on these expressions, the functionality of a nanogenerator is evaluated to gain insight into the conditions under which a wireless nanosensor network (WNSN) is viable from the energetic point of view. The results reveal that a micrometer-sized piezoelectric system in high-lossy environments (exceeding 100 dB/mm) becomes inoperative for transmission distances over 1.5 mm by its inability to harvest and store the amount of energy required to overcome the path loss.
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paper_title: Bistatic backscatter radio for power-limited sensor networks

paper_content:

For applications that require large numbers of wireless sensors spread in a field, backscatter radio can be utilized to minimize the monetary and energy cost of each sensor. Commercial backscatter systems such as those in radio frequency identification (RFID), utilize modulation designed for the bandwidth limited regime, and require medium access control (MAC) protocols for multiple access. High tag/sensor bitrate and monostatic reader architectures result in communication range reduction. In sharp contrast, sensing applications typically require the opposite: extended communication ranges that could be achieved with bitrate reduction and bistatic reader architectures. This work presents non-coherent frequency shift keying (FSK) for bistatic backscatter radio; FSK is appropriate for the power limited regime and also allows many RF tags/sensors to convey information to a central reader simultaneously with simple frequency division multiplexing (FDM). However, classic non-coherent FSK receivers are not directly applicable in bistatic backscatter radio. This work a) carefully derives the complete signal model for bistatic backscatter radio, b) describes the details of backscatter modulation with emphasis on FSK and its corresponding receiver, c) proposes techniques to overcome the difficulties introduced by the utilization of bistatic architectures, such as the carrier frequency offset (CFO), and d) presents bit error rate (BER) performance for the proposed receiver and carrier recovery techniques.
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paper_title: Ambient Backscatter Communications: A Contemporary Survey

paper_content:

Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.
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paper_title: Backscatter Communication Using Ultrawide Bandwidth Signals for RFID Applications

paper_content:

RFID technology for use in real-time object identification is being rapidly adopted in several fields such as logistic, automotive, surveillance, automation systems, etc. [1]. A radiofrequency identification (RFID) system consists of readers and tags applied to objects. The reader interrogates the tags via a wireless link to obtain the data stored on them. The cheapest RFID tags with the largest commercial potential are passive or semi-passive, and the energy necessary for tag–reader communication is harvested from the reader’s signal. Passive RFID tags are usually based on backscatter modulation, where the antenna reflection properties are changed according to information data [2].
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paper_title: EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

paper_content:

A wearable based long-term lifelogging system is desirable for the purpose of reviewing and improving users' lifestyle habits. Energy harvesting (EH) is a promising means for realizing sustainable lifelogging. However, present EH technologies suffer from instability of the generated electricity caused by changes of environment, e.g., the output of a solar cell varies based on its material, light intensity, and light wavelength. In this paper, we leverage this instability of EH technologies for other purposes, in addition to its use as an energy source. Specifically, we propose to determine the variation of generated electricity as a sensor for recognizing "places" where the user visits, which is important information in the lifelogging system. First, we investigate the amount of generated electricity of selected energy harvesting elements in various environments. Second, we design a system called EHAAS (Energy Harvesters As A Sensor) where energy harvesting elements are used as a sensor. With EHAAS, we propose a place recognition method based on machine-learning and implement a prototype wearable system. Our prototype evaluation confirms that EHAAS achieves a place recognition accuracy of 88.5% F-value for nine different indoor and outdoor places. This result is better than the results of existing sensors (3-axis accelerometer and brightness). We also clarify that only two types of solar cells are required for recognizing a place with 86.2% accuracy.
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paper_title: Three-axis piezoelectric vibration energy harvester

paper_content:

This paper reports for the first time a piezoelectric inertial harvester for scavenging vibrational energy in all three dimensions. The harvester is formed of optimized bulk-PZT/Si crab legs such that the first three resonance vibrational modes are in-plane and out-of-plane translational modes with closely-spaced frequencies (387–398 Hz). Partitioned electrodes on the PZT arms collect mechanical energy in the transverse (d 31 ) piezoelectric mode, and have phase difference in their voltage outputs according to the axis of the applied vibration. The harvester is realized using a planar micro-fabrication method, with internal and packaged device volumes of 2.5 and 6.5 cm3, respectively. The device has generated 5–53 µW from 50 mg acceleration applied in in-plane and out-of-plane directions at the respective resonance frequencies, and has a half-power bandwidth of 8–16 Hz. The normalized power density (0.3–3.2 mW/cm3/g2) compares favorably with respect to previously reported multi-axis inertial energy harvesters.
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paper_title: A Vibration-Based MEMS Piezoelectric Energy Harvester and Power Conditioning Circuit

paper_content:

This paper presents a micro-electro-mechanical system (MEMS) piezoelectric power generator array for vibration energy harvesting. A complete design flow of the vibration-based energy harvester using the finite element method (FEM) is proposed. The modal analysis is selected to calculate the resonant frequency of the harvester, and harmonic analysis is performed to investigate the influence of the geometric parameters on the output voltage. Based on simulation results, a MEMS Pb(Zr,Ti)O3 (PZT) cantilever array with an integrated large Si proof mass is designed and fabricated to improve output voltage and power. Test results show that the fabricated generator, with five cantilever beams (with unit dimensions of about 3 × 2.4 × 0.05 mm3) and an individual integrated Si mass dimension of about 8 × 12.4 × 0.5 mm3, produces a output power of 66.75 μW, or a power density of 5.19 μW∙mm−3∙g−2 with an optimal resistive load of 220 kΩ from 5 m/s2 vibration acceleration at its resonant frequency of 234.5 Hz. In view of high internal impedance characteristic of the PZT generator, an efficient autonomous power conditioning circuit, with the function of impedance matching, energy storage and voltage regulation, is then presented, finding that the efficiency of the energy storage is greatly improved and up to 64.95%. The proposed self-supplied energy generator with power conditioning circuit could provide a very promising complete power supply solution for wireless sensor node loads.
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paper_title: Piezoelectric and electromagnetic hybrid energy harvester for powering wireless sensor nodes in smart grid

paper_content:

A novel hybrid energy harvester integrated with piezoelectric and electromagnetic mechanism is designed for powering wireless sensor nodes in smart grid. In the proposed energy harvester device, four permanent magnets are attached on a piezoelectric cantilever array, and a Printed circuit board (PCB) with induction coils is mounted surrounding the magnets. The magnets couple to magnetic field of Alternating current (AC) carrying conductor and excite the piezoelectric cantilever array. Vibration energy is converted to electric energy via both piezoelectric and electromagnetic transduction mechanisms. A prototype in volume of 50×50×12 mm3 was fabricated and tested. From current-carrying conductor of 2.5A at 50 Hz, the proposed energy harvester combined with piezoelectric components and electromagnetic elements can generate up to 295.3 μW and 46.6 μW, respectively. With the total output power of 341.9 μW, the power density is much higher than the one generated only by conventional piezoelectric energy harvester under the same condition.
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paper_title: Electronic frog eye: Counting crowd using WiFi

paper_content:

Crowd counting, which count or accurately estimate the number of human beings within a region, is critical in many applications, such as guided tour and crowd control. A crowd counting solution should be scalable and be minimally intrusive (i.e., device-free) to users. Image-based solutions are device-free, but cannot work well in a dim or dark environment. Non-image based solutions usually require every human being carrying device, and are inaccurate and unreliable in practice. In this paper, we present FCC, a device-Free Crowd Counting approach based on Channel State Information (CSI). Our design is motivated by our observation that CSI is highly sensitive to environment variation, like a frog eye. We theoretically discuss the relationship between the number of moving people and the variation of wireless channel state. A major challenge in our design of FCC is to find a stable monotonic function to characterize the relationship between the crowd number and various features of CSI. To this end, we propose a metric, the Percentage of nonzero Elements (PEM), in the dilated CSI Matrix. The monotonic relationship can be explicitly formulated by the Grey Verhulst Model, which is used for crowd counting without a labor-intensive site survey. We implement FCC using off-theshelf IEEE 802.11n devices and evaluate its performance via extensive experiments in typical real-world scenarios. Our results demonstrate that FCC outperforms the state-of-art approaches with much better accuracy, scalability and reliability.
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paper_title: E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures

paper_content:

Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.
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paper_title: Gait recognition using wifi signals

paper_content:

In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information (CSI) on the WiFi receiver. To profile human movement using CSI, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectrograms that best characterize the walking pattern, we perform autocorrelation on the torso reflection to remove imperfection in spectrograms. We evaluated WifiU on a dataset with 2,800 gait instances collected from 50 human subjects walking in a room with an area of 50 square meters. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 79.28%, 89.52%, and 93.05%, respectively.
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paper_title: WiFall: Device-Free Fall Detection by Wireless Networks

paper_content:

Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.
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paper_title: Understanding and Modeling of WiFi Signal Based Human Activity Recognition

paper_content:

Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.
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paper_title: Ambient Backscatter Communications: A Contemporary Survey

paper_content:

Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.
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paper_title: H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors

paper_content:

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has a pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient.
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Title: Sensing, Computing, and Communication for Energy Harvesting IoTs: A Survey

Section 1: Introduction
Description 1: Introduce the background, the importance of energy harvesting for IoTs, and the organization of the paper.

Section 2: Commercial and Standards Developments in EH-IoTs
Description 2: Discuss recent commercial successes, research prototypes, and standardization activities in energy harvesting IoTs.

Section 3: Context Sensing from EH Patterns
Description 3: Survey methods for sensing various user and environmental contexts using energy harvesting signals instead of conventional sensors.

Section 4: Computing Optimizations for Batteryless EH-IoTs
Description 4: Review the challenges and techniques related to computing optimizations, including checkpointing and timing consistency, for batteryless energy harvesting IoTs.

Section 5: Communications Optimizations for EH-IoTs
Description 5: Examine the power allocation optimization strategies and recent advancements, particularly the application of reinforcement learning and packet-less transmission techniques, for communication in EH-IoTs.

Section 6: Future Research Directions
Description 6: Discuss potential future research areas and unresolved challenges in the field of energy harvesting IoTs.

Section 7: Conclusion
Description 7: Summarize the key findings of the survey, the lessons learned, and the implications for future research in energy harvesting IoTs.