A survey of testing and debugging testbeds for sensor networks
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paper_title: Static Verification of Wireless Sensor Networks with Formal Methods

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Wireless Sensor Networks (WSNs) are widely recognized as a solution to build monitoring systems, even in critical environments. WSNs, however, are subjected to faults due to several causes (i.e. rain, EMF radiations, vibrations, etc..) and tools and methodologies for the design of dependable WSN-based systems are needed. Formal methods partially meet such needs by assessing the degree of correctness of design models and identifying potential system bottlenecks. The aim of this paper is to define a methodology for the static verification of WSN based systems using a formal language (Event Calculus). In particular we show how the formal specification can be used to verify the design of a WSN in terms of its dependability properties. To this aim, we define a set of correctness specifications that apply to a generic WSN, coupled with specific structural specifications describing the target network topology to evaluate. Finally, after having presented an automatic tool, designed to support the designer, we adopt this methodology to a case study.
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paper_title: A Review of Automated Formal Verification of Ad Hoc Routing Protocols for Wireless Sensor Networks

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This paper surveys how formal verification can be used to prove the correctness of ad hoc routing protocols, which are fundamental infrastructure of wireless sensor networks. The existing techniques fall into two classes: verification on small-scale networks and verification on unbounded networks. The former one is always fully automatic and easy to use, thanks to the limited state space generated in verification. However, it cannot prove the correctness over all cases. The latter one can provide a complete proof based on abstractions of unbounded network. However, it usually needs user intervention and expertise in verification. The two kinds of technique are illustrated by verifications against some key properties such as stability, loop-freedom and deadlock-freedom. To conclude, they can be used to find faults and prove correctness, respectively. We believe that they can together aid the development of correct ad hoc routing protocols and their reliable implementations.
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paper_title: NISAT: a zero-side-effect testbed for wireless sensor networks

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The NISAT testbed consists of a center server and many test units. The test units probe the internal interconnected signals inside the motes with extra hardware sniffers. The server gathers, parses and analyzes all data from test units to obtain the information on the network behavior. By adopting the passive sniffing mechanism, NISAT has no side effect on the normal behavior of sensor networks, and it is transparent to the software running on motes. NISAT offers accurate and precise test data for the studies on sensor networks, especially for the high-precision performance measurements and black-box tests without source codes.
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paper_title: ATEMU: a fine-grained sensor network simulator

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In this paper we describe the design and implementation of ATEMU, a fine grained sensor network simulator. ATEMU is intended to bridge the gap between actual sensor network deployments and sensor network simulations. We adopt a hybrid strategy, where the operation of individual sensor nodes is emulated in an instruction by instruction manner, and their interactions with each other via wireless transmissions are simulated in a realistic manner. A unique feature of ATEMU is its ability to simulate a heterogeneous sensor network. Using ATEMU it is possible to not only accurately simulate the operation of different application on the MICA2 platform but also a complete sensor network where the sensor nodes themselves maybe based on different hardware platforms. In addition we also describe our implementation of XATDB, our front-end debugger/GUI for ATEMU. XATDB provides an excellent educational tool for people to start learning about the operation of sensor nodes and sensor networks, without requiring the purchase of actual sensor node hardware. The accuracy and emulation capabilities provided by ATEMU ensure that when and if actual hardware is used, the software will already have undergone rigorous testing and debugging on an accurate platform. This would provide the sensor network deployment community with a much more accurate estimate of the performance of various algorithms and protocols in realistic scenarios and platforms.
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paper_title: Emstar: A software environment for developing and deploying heterogeneous sensor-actuator networks

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Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8- or 16-bit “Motes” to less resource-constrained 32-bit embedded “Microservers.” Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications. In order to accomplish these objectives, Emstar is designed to run as a multiprocess system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time while achieving a high degree of robustness. We also show that a sample application was developed much more quickly on this platform than it would have been otherwise.
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paper_title: S2DB: a novel simulation-based debugger for sensor network applications

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Sensor network computing can be characterized as resource-constrained distributed computing using unreliable, low bandwidth communication. This combination of characteristics poses significant software development and maintenance challenges. Effective and efficient debugging tools for sensor network are thus critical. Existent development tools, such as TOSSIM, EmStar, ATEMU and Avrora, provide useful debugging support, but not with the fidelity, scale and functionality that we believe are sufficient to meet the needs of the next generation of applications.In this paper, we propose a debugger, called S2DB, based on a distributed full system sensor network simulator with high fidelity and scalable performance, DiSenS. By exploiting the potential of DiSenS as a scalable full system simulator, S2DB extends conventional debugging methods by adding novel device level, program source level, group level, and network level debugging abstractions. The performance evaluation shows that all these debugging features introduce overhead that is generally less than 10% into the simulator and thus making S2DB an efficient and effective debugging tool for sensor networks.
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paper_title: An open source tool for simulation and supervision of underwater intervention missions

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This paper presents UWSim: a new software tool for visualization and simulation of underwater robotic missions. The software visualizes an underwater virtual scenario that can be configured using standard modeling software. Controllable underwater vehicles, surface vessels and robotic manipulators, as well as simulated sensors, can be added to the scene and accessed externally through network interfaces. This allows to easily integrate the simulation and visualization tool with existing control architectures, thus allowing hardware-in-the-loop simulations (HIL). UWSim has been successfully used for simulating the logics of underwater intervention missions and for reproducing real missions from the captured logs. The software is offered as open source, thus filling a gap in the underwater robotics community, where commercial simulators oriented to ROV pilot training predominate.
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paper_title: Disens: scalable distributed sensor network simulation

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Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make trade-offs between fidelity and scalability and thus offer limited capabilities as design and analysis tools. In this paper, we introduce DiSenS (DIstributed SENsor network Simulation) -- a highly scalable distributed simulation system for sensor networks. DiSenS does not only faithfully emulates an extensive set of sensor hardware and supports extensible radio/power models, so that sensor network applications can be simulated transparently with high fidelity, but also employs distributed-memory parallel cluster system to attack the complex simulation problem. Combining an efficient distributed synchronization protocol and a sophisticated node partitioning algorithm (based on existent research), DiSenS achieves greater scalability than even many discrete event simulators. On a small to medium size cluster (16-64 nodes), DiSenS is able to simulate hundreds of motes in realtime speed and scale to thousands in sub-realtime speed. To our knowledge, DiSenS is the first full-system sensor network simulator with such scalability.
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paper_title: TOSSIM: accurate and scalable simulation of entire TinyOS applications

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Accurate and scalable simulation has historically been a key enabling factor for systems research. We present TOSSIM, a simulator for TinyOS wireless sensor networks. By exploiting the sensor network domain and TinyOS's design, TOSSIM can capture network behavior at a high fidelity while scaling to thousands of nodes. By using a probabilistic bit error model for the network, TOSSIM remains simple and efficient, but expressive enough to capture a wide range of network interactions. Using TOSSIM, we have discovered several bugs in TinyOS, ranging from network bit-level MAC interactions to queue overflows in an ad-hoc routing protocol. Through these and other evaluations, we show that detailed, scalable sensor network simulation is possible.
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paper_title: Marionette: using RPC for interactive development and debugging of wireless embedded networks

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A main challenge with developing applications for wireless embedded systems is the lack of visibility and control during execution of an application. In this paper, we present a tool suite called Marionette that provides the ability to call functions and to read or write variables on pre-compiled, embedded programs at run-time, without requiring the programmer to add any special code to the application. This rich interface facilitates interactive development and debugging at minimal cost to the node.
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paper_title: Clairvoyant: a comprehensive source-level debugger for wireless sensor networks

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Wireless sensor network (WSN) applications are notoriously difficult to develop and debug. This paper describes Clairvoyant which is a comprehensive source-level debugger for wireless, embedded networks. With Clairvoyant, a developer can wirelessly connect to a sensor network and execute standard debugging commands including break, step, watch, and backtrace, as well as new commands that are specially designed for debugging WSNs. Clairvoyant attempts to minimize its effect on the program being debugged in terms of network load, memory footprint, execution speed, clock consistency, and flash lifetime.
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paper_title: Design of an application-cooperative management system for wireless sensor networks

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This paper argues for the usefulness of an application-cooperative interactive management system for wireless sensor networks and presents SNMS, a sensor network management system. SNMS is designed to be simple and have minimal impact on memory and network traffic, while remaining open and flexible. The system is evaluated in light of issues derived from real deployment experiences.
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paper_title: MoteLab: a wireless sensor network testbed

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As wireless sensor networks have emerged as a exciting new area of research in computer science, many of the logistical challenges facing those who wish to develop, deploy, and debug applications on realistic large-scale sensor networks have gone unmet. Manually reprogramming nodes, deploying them into the physical environment, and instrumenting them for data gathering is tedious and time-consuming. To address this need we have developed MoteLab, a Web-based sensor network testbed. MoteLab consists of a set of permanently-deployed sensor network nodes connected to a central server which handles re programming and data logging while providing a Web interface for creating and scheduling jobs on the testbed. MoteLab accelerates application deployment by streamlining access to a large, fixed network of real sensor network devices; it accelerates debugging and development by automating data logging, allowing the performance of sensor network software to be evaluated offline Additionally, by providing a Web interface MoteLab allows both local and remote users access to the testbed, and its scheduling and quota system ensure fair sharing. We have developed and deployed MoteLab at Harvard and found ft invaluable for both research and teaching. The MoteLab source is freely available, easy to install, and already in use at several other research institutions. We expect that widespread use of MoteLab will accelerate and improve wireless sensor network research.
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paper_title: Kansei: a testbed for sensing at scale

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The Kansei testbed at the Ohio State University is designed to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation, (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources, (iii) High fidelity sensor data generation and real-time data and event injection, (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines.
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paper_title: Passive diagnosis for wireless sensor networks

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Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks (WSNs). Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sensor nodes, introducing network overhead to the resource constrained and usually traffic-sensitive sensor network. We report our initial attempt at providing a lightweight network diagnosis mechanism for sensor networks. We further propose PAD, a probabilistic diagnosis approach for inferring the root causes of abnormal phenomena. PAD employs a packet marking scheme for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a probabilistic inference model that encodes internal dependencies among different network elements for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD prototype in our sea monitoring sensor network test-bed. We also examine the efficiency and scalability of this design through extensive trace-driven simulations.
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paper_title: PMSW: a passive monitoring system in wireless sensor networks

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Network passive monitoring is an important method in debugging and analyzing wireless sensor networks. The passive monitoring method for wireless sensor networks (WSN) has the following problems: monitoring traces are incomplete and contain only limited information because some packets may be misheard; how to infer implicit information about network protocol behavior and how to detect network events from incomplete traces are challenging issues in passive monitoring system. To resolve the above issues, we propose the use of time tuning in trace merging and a finite state machine for inferring missing packets, implicit information and network event detection. (1) In order to enhance the integrity of traces, we propose clock-adjusting strategies including clock fine-tuning and clock self-adjustment to solve clock drifting in trace merging. (2) For inference of missing packets and implicit information, we devise and implement a finite state machine to infer missing packets and packet receiving state for multi-hop network protocols. (3) For network event detection, we propose an XML-based description of event rule and network event visualization methods. These methods are implemented in PMSW (passive monitoring system for WSN). We verify the accuracy of merging and inference modules with monitoring traces in the Collect Tree Protocol sensor network. Experiments show that the finite state machine can infer 40-60% of missing packets and more than 90% of the receiving state of data packets, when the trace has more than 50% of the data packets captured in the network.
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paper_title: Design of an application-cooperative management system for wireless sensor networks

paper_content:

This paper argues for the usefulness of an application-cooperative interactive management system for wireless sensor networks and presents SNMS, a sensor network management system. SNMS is designed to be simple and have minimal impact on memory and network traffic, while remaining open and flexible. The system is evaluated in light of issues derived from real deployment experiences.
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paper_title: SNTS: sensor network troubleshooting suite

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Sensor network troubleshooting is a notoriously difficult task, further exacerbated by resource constraints, unreliable components, unpredictable natural phenomena, and experimental programming paradigms. This paper presents SNTS (Sensor Network Troubleshooting Suite), a tool that performs automated failure diagnosis in sensor networks. SNTS can be used to monitor network conditions using simple visualization techniques as well as to troubleshoot deployed distributed sensor systems using data mining approaches. It is composed of (i) a data collection front-end that records events internal to the network and (ii) a data processing back-end for subsequent analysis. We use data mining techniques to automate failure diagnosis on the back-end. The assumption is that the occurrence of execution conditions that cause failures (e.g., traversal of an execution path that contains a "bug" or occurrence of a sequence of events that a protocol was not designed to handle) will have a measurable correlation (by causality) with the resulting failure itself. Hence, by mining for network conditions that correlate with failure states the root causes of failure are revealed with high probability. To evaluate the effectiveness of the tool, we have used it to troubleshoot a tracking system called EnviroTrack [4], which, although performs well most of the time, occasionally fails to track targets correctly. Results show that SNTS can identify the major causes of the problem and give developers useful hints on improving the performance of the tracking system.
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paper_title: Sympathy for the sensor network debugger

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Being embedded in the physical world, sensor networks present a wide range of bugs and misbehavior qualitatively different from those in most distributed systems. Unfortunately, due to resource constraints, programmers must investigate these bugs with only limited visibility into the application. This paper presents the design and evaluation of Sympathy, a tool for detecting and debugging failures in sensor networks. Sympathy has selected metrics that enable efficient failure detection, and includes an algorithm that root-causes failures and localizes their sources in order to reduce overall failure notifications and point the user to a small number of probable causes. We describe Sympathy and evaluate its performance through fault injection and by debugging an active application, ESS, in simulation and deployment. We show that for a broad class of data gathering applications, it is possible to detect and diagnose failures by collecting and analyzing a minimal set of metrics at a centralized sink. We have found that there is a tradeoff between notification latency and detection accuracy; that additional metrics traffic does not always improve notification latency; and that Sympathy's process of failure localization reduces.
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paper_title: Achieving Repeatability of Asynchronous Events in Wireless Sensor Networks with EnviroLog

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Abstract : Sensing events from dynamic environments are normally asynchronous and non-repeatable. This lack of repeatability makes it particularly difficult to statistically evaluate the performance of sensor network applications. Hence, it is essential to have the capability to capture and replay sensing events, providing a basis not only for system evaluation, but also for realistic protocol comparison and parameter tuning. To achieve that, we design and implement EnviroLog, a distributed service that improves repeatability of experimental testing of sensor networks via asynchronous event recording and replay. To use EnviroLog, an application programmer needs only to specify two types of simple annotations to the source code. Automatically, the preprocessor embeds EnviroLog into any desired level of an event-driven architecture. It records all events generated by lower layers and can replay them later to upper layers on demand. We validate the accuracy and performance of recording and replay through a set of microbenchmarks, using the latest XSM platforms. We further demonstrate the strength of EnviroLog in system tuning and performance evaluation for sensor network applications in an outdoor environment with 37 XSMs.
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paper_title: Passive diagnosis for wireless sensor networks

paper_content:

Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks (WSNs). Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sensor nodes, introducing network overhead to the resource constrained and usually traffic-sensitive sensor network. We report our initial attempt at providing a lightweight network diagnosis mechanism for sensor networks. We further propose PAD, a probabilistic diagnosis approach for inferring the root causes of abnormal phenomena. PAD employs a packet marking scheme for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a probabilistic inference model that encodes internal dependencies among different network elements for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD prototype in our sea monitoring sensor network test-bed. We also examine the efficiency and scalability of this design through extensive trace-driven simulations.
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paper_title: Learning of model parameters for fault diagnosis in wireless networks

paper_content:

Self-management is essential for Beyond 3G (B3G) systems, where the existence of multiple access technologies (GSM, GPRS, UMTS, WLAN, etc.) will complicate network operation. Diagnosis, that is, fault identification, is the most difficult task in automatic fault management. This paper presents a probabilistic system for auto-diagnosis in the radio access part of wireless networks, which comprises a model and a method. The parameters of the model are thresholds for the discretization of Key Performance Indicators (KPIs) and probabilities. In this paper, some techniques are proposed for the automatic learning of those model parameters. In order to support the theoretical concepts, experimental results are examined, based on data from a live network. It has been proven that calculating parameters from network statistics, instead of being defined by diagnosis experts, highly increases the performance of the diagnosis system. In addition, the proposed techniques enhance the results obtained with continuous diagnosis models previously exposed in the literature.
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paper_title: Continuous versus Discrete Model in Autodiagnosis Systems for Wireless Networks

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In the near future, several radio access technologies will coexist in Beyond 3G mobile networks (B3G), and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e., those that self-configure, self-protect, self-heal, and self-optimize) are the solution to tackle the high complexity inherent to these networks. In this context, this paper proposes a system for autodiagnosis in the Radio Access Network (RAN) of wireless systems. The malfunction of the RAN may be due not only to a hardware fault but also (and more difficult to identify) to a bad configuration. The proposed system is based on the analysis of Key Performance Indicators (KPIs) in order to isolate the cause of the network malfunction. In this paper, two alternative probabilistic systems are compared, which differ on how KPIs are modeled (continuous or discrete variables). Experimental results are examined in order to support the theoretical concepts, based on data from a live network. The drawbacks and benefits of both systems are studied, and some conclusions on the scenarios under which each model should be used are presented.
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paper_title: Sensor fault diagnosis and location for small and medium-scale wireless sensor networks

paper_content:

One of the prerequisites for the normal operation of a small and medium-scale wireless sensor network is the ability to diagnose sensor node failures. Considering the characteristics of small scale, low-density node distribution, stable network topology and the fact that each node of the small and medium-scale wireless sensor network can be monitored, a method for detecting and identifying sensor node failure based on Principal Component Analysis (PCA) and wavelet decomposition is proposed in order to improve network stability and reliability. The principal component analysis model is established utilizing historical data and the data collected by sensor nodes are decomposed into principal space and residual sub-space. A faulty sensor can be detected in real time by analyzing the Square Prediction Error (SPE) index and the faulty sensor node identified by extracting high-frequency coefficients of wavelet decomposition. Experimental data indicate that this method of sensor node diagnostic analysis functions in real time operates with low communication overhead and is well adapted for use in small and medium-scale wireless sensor networks.
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paper_title: Antler: A multi-tiered approach to automated wireless network management

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Management of a large scale wireless network, be it an infrastructured WLAN or a metro-scale mesh network, presents several challenges. Troubleshooting problems related to wireless access in these networks requires a comprehensive set of metrics and network monitoring data. Current solutions gather large amounts of data and require significant bandwidth and processing to offload and analyze this management traffic. As a result, these solutions are typically not scalable or real-time. To this end, we propose a multi-tiered approach to wireless network monitoring that dynamically controls the granularity of data collection based on observed events in the network. Our approach can achieve significant bandwidth savings and enable real-time automated management of a wireless network. Our initial analysis using traces from a large WLAN shows a significant reduction in the amount of data collected to diagnose problems in a WLAN.
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paper_title: Sentomist: Unveiling Transient Sensor Network Bugs via Symptom Mining

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Wireless Sensor Network (WSN) applications are typically event-driven. While the source codes of these applications may look simple, they are executed with a complicated concurrency model, which frequently introduces software bugs, in particular, transient bugs. Such buggy logics may only be triggered by some occasionally interleaved events that bear implicit dependency, but can lead to fatal system failures. Unfortunately, these deeply-hidden bugs or even their symptoms can hardly be identified by state-of-the-art debugging tools, and manual identification from massive running traces can be prohibitively expensive. In this paper, we present Sentomist (Sensor application anatomist), a novel tool for identifying potential transient bugs in WSN applications. The Sentomist design is based on a key observation that transient bugs make the behaviors of a WSN system deviate from the normal, and thus outliers (i.e., abnormal behaviors) are good indicators of potential bugs. Sentomist introduces the notion of event-handling interval to systematically anatomize the long-term execution history of an event-driven WSN system into groups of intervals. It then applies a customized outlier detection algorithm to quickly identify and rank abnormal intervals. This dramatically reduces the human efforts of inspection (otherwise, we have to manually check tremendous data samples, typically with brute force inspection) and thus greatly speeds up debugging. We have implemented Sentomist based on the concurrency model of TinyOS. We apply Sentomist to test a series of representative real-life WSN applications that contain transient bugs. These bugs, though caused by complicated interactions that can hardly be predicted during the programming stage, are successfully confined by Sentomist.
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paper_title: Dustminer: troubleshooting interactive complexity bugs in sensor networks

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This paper presents a tool for uncovering bugs due to interactive complexity in networked sensing applications. Such bugs are not localized to one component that is faulty, but rather result from complex and unexpected interactions between multiple often individually non-faulty components. Moreover, the manifestations of these bugs are often not repeatable, making them particularly hard to find, as the particular sequence of events that invokes the bug may not be easy to reconstruct. Because of the distributed nature of failure scenarios, our tool looks for sequences of events that may be responsible for faulty behavior, as opposed to localized bugs such as a bad pointer in a module. An extensible framework is developed where a front-end collects runtime data logs of the system being debugged and an offline back-end uses frequent discriminative pattern mining to uncover likely causes of failure. We provide a case study of debugging a recent multichannel MAC protocol that was found to exhibit corner cases of poor performance (worse than single channel MAC). The tool helped uncover event sequences that lead to a highly degraded mode of operation. Fixing the problem significantly improved the performance of the protocol.We also provide a detailed analysis of tool overhead in terms of memory requirements and impact on the running application.
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paper_title: Agnostic diagnosis: Discovering silent failures in wireless sensor networks

paper_content:

In wireless sensor networks (WSNs), diagnosis is a crucial and challenging task due to the distributed nature and stringent resources. Most previous approaches are supervised, relying on a-priori knowledge of network faults. On the other hand, our experience with GreenOrbs, a long-term large-scale WSN system, reveals the need of diagnosis in an agnostic manner. Specifically, in addition to predefined faults (i.e., with known types and symptoms), silent failures that are unknown beforehand, account for a large fraction of network performance degradation. Currently, there is no effective solution for silent failures because they are often diverse and highly system-related. In this paper, we propose Agnostic Diagnosis (AD), an online lightweight failure detection approach. AD is motivated by the fact that the system metrics (e.g., radio-on time, number of packets transmitted) of GreenOrbs sensors usually exhibit certain correlation patterns. Violations of such patterns indicate potential silent failures. We accordingly design a correlation graph, which systematically characterizes internal correlations inside a node. Silent failures are discovered by tracking the changes and anomalies of correlation graphs. We implement AD on a working WSN consisting of 330 nodes. Our experimental results demonstrate the advantages of AD to discover silent failures, effectively expanding the capacity and scope of WSN diagnosis.
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paper_title: FIND: faulty node detection for wireless sensor networks

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Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functionnal faults, this paper proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.
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Title: A survey of testing and debugging testbeds for sensor networks

Section 1: INTRODUCTION
Description 1: This section introduces the significance, challenges, and current state of testing and debugging processes in wireless sensor networks, along with a brief overview of the methods used for these purposes.

Section 2: ACQUISITION FOR TESTING AND DEBUGGING INFORMATION
Description 2: This section discusses the types of information needed for testing and debugging, including the methods and tools used to acquire this information from wireless sensor networks.

Section 3: Simulation Debugging
Description 3: This section covers the approach of using simulation debugging systems, including examples like TOSSIM, DiSenS, S2DB, EmStar, and UWSim, along with their respective advantages and limitations.

Section 4: Source Code based Online Debugging
Description 4: This section explains the method of source code based online debugging, highlighting tools like Marionette and Clairvoyant, and discussing the pros and cons of this approach.

Section 5: Active Information Acquisition
Description 5: This section describes active information acquisition methods, featuring systems such as Motelab, Kansei, and SNMS, and outlines their functionalities and drawbacks.

Section 6: Passive Non-intrusive Information Acquisition
Description 6: This section explores passive non-intrusive information acquisition techniques, including systems like Pimoto, SNTS, PMSW, and Z-Monitor, focusing on how they gather data without disrupting the network.

Section 7: FAULT DIAGNOSIS AND DEBUGGING IN SENSOR NETWORKS
Description 7: This section discusses various methods for fault diagnosis and debugging, including recording and playback, artificial intelligence methods, data mining methods, and other innovative techniques.

Section 8: Recording and Playback Method
Description 8: This section delves into the recording and playback method for fault diagnosis, providing examples like Sympathy, Motelab, Kansei, EmStar, and Envirolog, and discussing their capabilities and limitations.

Section 9: Artificial Intelligence Method
Description 9: This section explains the artificial intelligence approaches used in fault diagnosis, such as machine learning, decision trees, and Bayesian networks, with examples like Sympathy, Antler, and others.

Section 10: Data Mining Method
Description 10: This section covers the application of data mining techniques in fault diagnosis, examining systems like Dustminer and Sentomist, and highlighting their effectiveness and constraints.

Section 11: Other Methods
Description 11: This section reviews additional methods for system inspection and fault detection, focusing on data correlation techniques and examples like the FIND system.

Section 12: CONCLUSION
Description 12: This section summarizes the reviewed testing and debugging methods and testbeds, discusses the trends in information acquisition and processing, and proposes future directions for testing and debugging in wireless sensor networks.