A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring
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paper_title: System software techniques for low-power operation in wireless sensor networks

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The operation of wireless sensor networks is fundamentally constrained by available energy sources. The underlying hardware determines the power draw of each possible mode of operation. System software attempts maximize the use of the lowest possible modes of each of the subsystems. This tutorial paper describes the system software techniques used at several levels. At the application sensing level, this includes duty-cycling, sensor hierarchy, and aggregation. At the communication level, it includes low-power listening, communication scheduling, piggybacking, post-hoc synchronization, and power-aware routing. At the node OS level, it includes event driven execution with split-phase operation and cooperative power management interfaces. At the lowest level, it includes management of primary and secondary energy storage devices coupled with intelligent charge transfer scheduling. All of these aspects must be integrated in a systematic software framework.
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paper_title: The nesC language: A holistic approach to networked embedded systems

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We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.
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paper_title: Soil embedded networked systems for studying soil carbon dynamics: the A-MARSS project

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Introduction A-MARSS (Automated-Minirhizotron and Arrayed Rhizosphere Soil Sensors) is a NSF biocomplexity instrumentation grant to study mycorrhizae, soil ecology, and has application to soil carbon dynamics. The role of soil microorganisms is still a black box in global change research (Treseder and Allen 2000) because soil processes are difficult to quantify in space and time (Klironomos et al. 1999) (Figure 1). R. Vargas1, M.F. Allen1, W. Swenson1, and M. Hamilton2
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paper_title: A drift-tolerant model for data management in ocean sensor networks

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Traditional means of observing the ocean, like fixed mooring stations and radar systems, are difficult and expensive to deploy and provide coarse-grained and data measurements of currents and waves. In this paper, we explore the use of inexpensive wireless drifters as an alternative flexible infrastructure for fine-grained ocean monitoring. Surface drifters are designed specifically to move passively with the flow of water on the ocean surface and they are able to acquire sensor readings and GPS-generated positions at regular intervals. We view the fleet of drifters as a wireless ad-hoc sensor network with two types of nodes:i) a few powerful drifters with satellite connectivity, acting as mobile base-stations, and ii)a large number of low-power drifters with short-range acoustic or radio connectivity. Using real datasets from the Gulf of Maine (US) and the Liverpool Bay (UK), we study connectivity and uniformity properties of the ad-hoc mobile sensor network. We investigate the effect of deployment strategy, weather conditions as well as seasonal changes on the ability of drifters to relay readings to the end-users,and to provide sufficient sensing coverage of the monitored area. Our empirical study provides useful insights on how to design distributed routing and in-network processing algorithms tailored for ocean-monitoring sensor networks.
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paper_title: Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

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Movement patterns, like flockingand converging, leadingand following, are examples of high-level process knowledge derived from low-level trajectory data. Conventional techniques for the detection of movement patterns rely on centralized "omniscient" computing systems that have global access to the trajectories of mobile entities. However, in decentralized spatial information processing systems, exemplified by wireless sensor networks, individual processing units may only have access to localinformation about other individuals in their immediate spatial vicinity. Where the individuals in such decentralized systems are mobile, there is a need to be able to detect movement patterns using collaboration between individuals, each of which possess only partial knowledge of the global system state. This paper presents an algorithm for decentralized detection of the movement pattern flock, with applications to mobile wireless sensor networks. The algorithm's reliability is evaluated through testing on simulated trajectories emerging from unconstrained random movement and correlated random walk.
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paper_title: Delay-bounded routing in vehicular ad-hoc networks

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Ad hoc networks formed by traveling vehicles are envisaged to become a common platform that will support a wide variety of applications, ranging from road safety to advertising and entertainment. The multitude of vehicular applications calls for routing schemes that satisfy user-defined delay requirements while at the same time maintaining a low level of channel utilization to allow their coexistence. This paper focuses on the development of carry-and-forward schemes that attempt to deliver data from vehicles to fixed infrastructure nodes in an urban setting. The proposed algorithms leverage local or global knowledge of traffic statistics to carefully alternate between the Data Muling and Multihop Forwarding strategies, in order to minimize communication overhead while adhering to delay constraints imposed by the application. We provide an extensive evaluation of our schemes using realistic vehicular traces on a real city map.
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paper_title: Directed diffusion: a scalable and robust communication paradigm for sensor networks

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Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.
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paper_title: The nesC language: A holistic approach to networked embedded systems

paper_content:

We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.
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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: TAG: Tiny AGgregate Queries in Ad-Hoc Sensor Networks

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We present the Tiny AGgregation (TAG) approach to distributing declarative SQL-style aggregate queries with grouping over networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in-network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-ofnetwork methods, and discuss a variety of optimizations for improving the performance and fault-tolerance of the basic solution.
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paper_title: MauveDB: supporting model-based user views in database systems

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Real-world data --- especially when generated by distributed measurement infrastructures such as sensor networks --- tends to be incomplete, imprecise, and erroneous, making it impossible to present it to users or feed it directly into applications. The traditional approach to dealing with this problem is to first process the data using statistical or probabilistic models that can provide more robust interpretations of the data. Current database systems, however, do not provide adequate support for applying models to such data, especially when those models need to be frequently updated as new data arrives in the system. Hence, most scientists and engineers who depend on models for managing their data do not use database systems for archival or querying at all; at best, databases serve as a persistent raw data store.In this paper we define a new abstraction called model-based views and present the architecture of MauveDB, the system we are building to support such views. Just as traditional database views provide logical data independence, model-based views provide independence from the details of the underlying data generating mechanism and hide the irregularities of the data by using models to present a consistent view to the users. MauveDB supports a declarative language for defining model-based views, allows declarative querying over such views using SQL, and supports several different materialization strategies and techniques to efficiently maintain them in the face of frequent updates. We have implemented a prototype system that currently supports views based on regression and interpolation, using the Apache Derby open source DBMS, and we present results that show the utility and performance benefits that can be obtained by supporting several different types of model-based views in a database system.
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paper_title: Model-Driven Data Acquisition in Sensor Networks

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Declarative queries are proving to be an attractive paradigm for ineracting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this paper, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.
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paper_title: Detecting basic topological changes in sensor networks by local aggregation

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Wireless sensor networks (WSNs) can provide real-time information about geospatial environments, and so have the potential to play an important role in the monitoring of geographic phenomena. The research reported in this paper uses WSNs to provide salient information about spatially distributed dynamic fields, such as regional variations in temperature or concentration of a toxic gas. The focus is on topological changes to areas of high-activity that occur during the evolution of the field. Topological changes investigated include region merging and splitting, and hole formation or elimination. Such changes are formally characterized, and an algorithm is developed that detects such changes by means purely of in-network processing. The efficiency of this algorithm is investigated both theoretically and using simulation experiments.
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paper_title: Monitoring dynamic spatial fields using responsive geosensor networks

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Information about dynamic spatial fields, such as temperature, windspeed, or the concentration of gas pollutant in the air, is important for many environmental applications. At the same time, the development of geosensor networks (wirelessly communicating, sensor-enabled, small computing devices distributed throughout a geographic environment) present new opportunities for monitoring dynamic spatial fields in much greater detail than ever before. This paper develops a new model for querying information about dynamic spatial fields using geosensor networks. In order to manage the inherent complexity of dynamic geographic phenomena, our approach is to focus on the qualitative representation of spatial entities, like regions, boundaries, and holes, and of events, like splitting, merging, appearance, and disappearance. Based on combinatorial maps, we present a qualitative model as the underlying data management paradigm for geosensor networks. This model is capable of tracking salient changes in the network in an energy-efficient way. Further, our model enables reconfiguration of the geosensor network in response to changes in the environment. We present an algorithm capable of adapting sensor network granularity according to dynamic monitoring requirements. Regions of high variability can trigger increases in the geosensor network granularity, leading to more detailed information about the dynamic field. Conversely, regions of stability can trigger a coarsening of the sensor network, leading to efficiency increases in particular with respect to power consumption and longevity of the sensor nodes. Querying of this responsive geosensor network is also considered, and the paper concludes with a review of future research directions.
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paper_title: Towards Spatial Window Queries Over Continuous Phenomena in Sensor Networks

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Recent research on sensor networks has focused on the efficient processing of declarative SQL queries over sensor nodes. Users are often interested in querying an underlying continuous phenomenon such as a toxic plume, whereas only discrete readings of sensor nodes are available. Therefore, additional information estimation methods are necessary to process the sensor readings to generate the required query results. Most estimation methods are computationally intensive, even when computed in a traditional centralized setting. Furthermore, energy and communication constraints of sensor networks challenge the efficient application of established estimation methods in sensor networks. In this paper, we present an approach using Gaussian kernel estimation to process spatial window queries over continuous phenomena in sensor networks. The key contribution of our approach is the use of a small number of Hermite coefficients to approximate the Gaussian kernel function for subclustered sensor nodes. As a result, our algorithm reduces the size of messages transmitted in the network by logarithmic order, thus saving resources while still providing high-quality query results.
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paper_title: Utilizing Voronoi Cells of Location Data Streams for Accurate Computation of Aggregate Functions in Sensor Networks

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Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model. Aggregation queries on the data sampled at each sensor node are the main means to extract the abstract characteristics of the surrounding environment. However, the non-uniform distribution of the sensor nodes in the environment leads to inaccurate results generated by the aggregation queries. In this paper, we introduce “spatial aggregations” that take into consideration the spatial location of each measurement generated by the sensor nodes. We propose the use of spatial interpolation methods derived from the fields of spatial statistics and computational geometry to answer spatial aggregations. In particular, we study Spatial Moving Average (SMA), Voronoi Diagram and Triangulated Irregular Network (TIN). Investigating these methods for answering spatial average queries, we show that the average value on the data samples weighted by the area of the Voronoi cell of the corresponding sensor node, provides the best precision. Consequently, we introduce an algorithm to compute and maintain the accurate Voronoi cell at each sensor node while the location of the others arrive on data stream. We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network. To demonstrate the performance of in-network implementation of our aggregation operators, we have developed prototypes of two different approaches to distributed spatial aggregate processing.
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paper_title: TAG: Tiny AGgregate Queries in Ad-Hoc Sensor Networks

paper_content:

We present the Tiny AGgregation (TAG) approach to distributing declarative SQL-style aggregate queries with grouping over networks of low-power, wireless sensors. We discuss various generic properties of aggregates, and show how those properties affect the performance of our in-network approach. We include a performance study demonstrating the advantages of our approach over traditional centralized, out-ofnetwork methods, and discuss a variety of optimizations for improving the performance and fault-tolerance of the basic solution.
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Title: A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring
Section 1: Introduction
Description 1: Provide an overview of the recent technology trends affecting geosciences, the impact of real-time data availability on environmental monitoring, and the scope of the paper.

Section 2: Technology Developments
Description 2: Survey the current advancements in geosensor networks, including computing platforms and MEMS technology.

Section 3: Computing Platforms
Description 3: Detail the development and specifications of small-scale computing platforms used in geosensor networks, such as the Mica Motes, TMote Sky, and future trends like "Smart Dust."

Section 4: Sensors and Microsensors
Description 4: Discuss the development and uses of sensors and microsensors in geosensor networks, including specific examples like temperature and humidity sensors, and new bio-chemical microsensors.

Section 5: Geosensor Networks Applications
Description 5: Provide examples of geosensor network applications across different domains, classified by their observation characteristics.

Section 6: Terrestrial Ecology Observing Systems
Description 6: Discuss specific applications in terrestrial ecology, such as agricultural sensing systems and soil CO2 sensing systems.

Section 7: Geological Observation Systems
Description 7: Highlight geological observation applications, specifically focusing on volcano monitoring deployments.

Section 8: Aquatic Observing Systems
Description 8: Describe mobile geosensor networks used in aquatic observation systems, including stationary and mobile sensor networks.

Section 9: Research Challenges
Description 9: Examine the new research challenges posed by small-scale geosensor networks, including programming complexity, energy-efficient algorithms, real-time data management, and sensor data integration.

Section 10: Application Programming Interfaces for Geosensor Networks
Description 10: Discuss the need for user-friendly APIs to simplify tasking geosensor networks and detail current solutions like TinyDB.

Section 11: Decentralized and Collaborative Spatial Computation
Description 11: Explore the shift towards in-network computation and collaborative event detection to reduce energy consumption and optimize data processing.

Section 12: Real-Time Sensor Data Streams
Description 12: Discuss the management and analysis of real-time sensor data streams and the requirements for data stream processing technology.

Section 13: Sensor Web
Description 13: Describe the concept of the 'Sensor Web' for sharing and integrating sensor data streams, and the need for standardized interfaces and open-source software.

Section 14: Conclusions and Outlook
Description 14: Summarize the current state and future potential of geosensor networks in geosciences, along with expected technological advancements and research directions.