Shashi Phoha

Sensor Network Operations

PREFACE. CONTRIBUTORS. I SENSOR NETWORK OPERATIONS OVERVIEW. 1 Overview of Mission-Oriented Sensor Networks. 1.1 Introduction. 1.2 Trends in Sensor Development. 1.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective. References. II SENSOR NETWORK DESIGN AND OPERATIONS. 2 Sensor Deployment, Self-Organization, and Localization. 2.1 Introduction. 2.2 SCARE: A Scalable Self-Configuration and Adaptive Reconfiguration Scheme for Dense Sensor Networks. 2.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks. 2.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation. 2.5 Sensing Coverage and Breach Paths in Surveillance Wireless Sensor Networks. References. 3 Purposeful Mobility and Navigation. 3.1 Introduction. 3.2 Controlled Mobility for Efficient Data Gathering in Sensor Networks with Passively Mobile Nodes. 3.3 Purposeful Mobility in Tactical Sensor Networks. 3.4 Formation and Alignment of Distributed Sensing Agents with Double-Integrator Dynamics and Actuator Saturation. 3.5 Modeling and Enhancing the Data Capacity of Wireless Sensor Networks. References. 4 Lower Layer Issues-MAC, Scheduling, and Transmission. 4.1 Introduction. 4.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks. 4.3 Comprehensive Performance Study of IEEE 802.15.4. 4.4 Providing Energy Efficiency for Wireless Sensor Networks Through Link Adaptation Techniques. References. 5 Network Routing. 5.1 Introduction. 5.2 Load-Balanced Query Protocols for Wireless Sensor Networks. 5.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in Sensor Networks. 5.4 LESS: Low-Energy Security Solution for Large-scale Sensor Networks Based on Tree-Ripple-Zone Routing Scheme. References. 6 Power Management. 6.1 Introduction. 6.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor Networks. 6.3 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis. 6.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless Sensor Nodes. 6.5 Battery Lifetime Estimation and Optimization for Underwater Sensor Networks. References. 7 Distributed Sensing and Data Gathering. 7.1 Introduction. 7.2 Secure Differential Data Aggregation for Wireless Sensor Networks. 7.3 Energy-Conserving Data Gathering Strategy Based on Trade-off Between Coverage and Data Reporting Latency in Wireless Sensor Networks. 7.4 Quality-Driven Information Processing and Aggregation in Distributed Sensor Networks. 7.5 Progressive Approach to Distributed Multiple-Target Detection in Sensor Networks. References. 8 Network Security. 8.1 Introduction. 8.2 Energy Cost of Embedded Security for Wireless Sensor Networks. 8.3 Increasing Authentication and Communication Confidentiality in Wireless Sensor Networks. 8.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc Networks. 8.5 Fast and Scalable Key Establishment in Sensor Networks. 8.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group Key Agreement Protocol for Sensor Networks. References. III SENSOR NETWORK APPLICATIONS. 9 Pursuer-Evader Tracking in Sensor Networks. 9.1 Introduction. 9.2 The Problem. 9.3 Evader-Centric Program. 9.4 Pursuer-Centric Program. 9.5 Hybrid Pursuer-Evader Program. 9.6 Efficient Version of Hybrid Program. 9.7 Implementation and Simulation Results. 9.8 Discussion and Related Work. References. 10 Embedded Soft Sensing for Anomaly Detection in Mobile Robotic Networks. 10.1 Introduction. 10.2 Mobile Robot Simulation Setup. 10.3 Software Anomalies in Mobile Robotic Networks. 10.4 Soft Sensor. 10.5 Software Anomaly Detection Architecture. 10.6 Anomaly Detection Mechanisms. 10.7 Test Bed for Software Anomaly Detection in Mobile Robot Application. 10.8 Results and Discussion. 10.9 Conclusions and Future Work. Appendix A. Appendix B. References. 11 Multisensor Network-Based Framework for Video Surveillance: Real-Time Superresolution Imaging. 11.1 Introduction. 11.2 Basic Model of Distributed Multisensor Surveillance System. 11.3 Superresolution Imaging. 11.4 Optical Flow Computation. 11.5 Superresolution Image Reconstruction. 11.6 Experimental Results. 11.7 Conclusion. References. 12 Using Information Theory to Design Context-Sensing Wearable Systems. 12.1 Introduction. 12.2 Related Work. 12.3 Theoretical Background. 12.4 Adaptations. 12.5 Design Considerations. 12.6 Case Study. 12.7 Results. 12.8 Conclusion. Appendix. References. 13 Multiple Bit Stream Image Transmission over Wireless Sensor Networks. 13.1 Introduction. 13.2 System Description. 13.3 Experimental Results. 13.4 Summary and Discussion. References. 14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking. 14.1 Introduction. 14.2 Sensor Network Operational Components. 14.3 Sensor Network Challenge Problem. 14.4 Integrated Target Surveillance Experiment. 14.5 Experimental Results and Evaluation. 14.6 Conclusion. References. 15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert. 15.1 Introduction. 15.2 Distributed Tracking. 15.3 Algorithms. 15.4 Experimental Methods. 15.5 Results and Discussion. 15.6 Conclusion. References. ACKNOWLEDGMENTS. INDEX. ABOUT THE EDITORS.

Self-organizing sensor networks for integrated target surveillance

Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy-efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course

Surveillance coverage of sensor networks under a random mobility strategy

We consider the problem of surveillance of a region undertaken by a group of mobile sensors. Random mobility strategies are discussed in terms of coverage efficiency, communication and reliability in hostile environments. Under a Brownian motion random mobility strategy for the sensor grid, the distribution of the time-until-detection of slowly moving (point) targets is studied. Both two and three dimensional environments are considered. We obtain explicit formulas in three dimensions and bounds in two.

Tracking multiple targets with self-organizing distributed ground sensors

This paper describes a fully distributed approach to target tracking that we have implemented and tested in a military setting. The approach uses local sharing of robust statistics that summarize local events. Local collaboration extracts detection information such as time, velocity, position, heading and target type from the summary statistics. Groups of nodes used for local collaboration are determined dynamically at run time. Local collaboration information is compared with a list of tracks in the immediate vicinity. A variation on the nearest-neighbor algorithm associates detections to tracks. This paper extends our previous work by analyzing the ability of our distributed tracker to track multiple targets in a simulated environment. Results from simulations and field tests of the approach are provided.

Semantic Information Fusion for Coordinated Signal Processing in Mobile Sensor Networks

Distributed cognition of dynamic processes is commonly observed in mobile groups of animates like schools of fish, hunting lions, or in human teams for sports or military maneuvers. This paper presents methods for dynamic distributed cognition using an ad hoc mobile network of microsensors to detect, identify and track targets in noisy environments. We develop off-line algorithms for aggregating the most appropriate knowledge abstractions into semantic information, which is then used for on-line fusion of relevant attributes observed by local clusters in the sensor network. Local analysis of time series of sensor data yields aggregated semantic information, which is exchanged across nodes for higher level distributed cognition. This eliminates the need for exchanging high volumes of signal data and, thus reduces bandwidth and energy requirements for battery powered microsensors.

Signed real measure of regular languages for discrete-event automata

This paper presents the concept and formulation of a signed real measure of regular languages for analysis of discrete-event supervisory control systems. The measure is constructed based upon the principles of language theory and real analysis for quantitative evaluation and comparison of the controlled behaviour for discrete-event automata. The marked (i.e. accepted) states of finite-state automata are classified in different categories such that the event strings terminating at good and bad marked states have positive and negative measures, respectively. In this setting, a controlled language attempts to disable as many bad strings as possible and as few good strings as possible. Different supervisors may achieve this goal in different ways and generate a partially ordered set of controlled languages. The language measure creates a total ordering on the performance of the controlled languages, which provides a precise quantitative comparison of the controlled plant behaviour under different supervisors. Total variation of the language measure serves as a metric for the space of sublanguages of the regular language.

Tracking targets with self-organizing distributed ground sensors

This paper describes a fully distributed approach to target tracking that we have implemented and tested in a military setting. The approach is built upon local sharing of robust statistics that summarize local events. Local collaboration extracts detection information such as time, velocity, position, heading and target type from the summary statistics. The groups of nodes used for local collaboration are determined dynamically at run time. Local collaboration information is compared with a list of tracks in the immediate vicinity. Associating detections to tracks is currently done using a variation of the nearest-neighbor metric. This paper extends our previous work by using mobile code daemons to support multiple hypothesis tracking methods. This is done in a resourceconstrained environment by using the network to swap sohare modules dynamically. Results from field tests of the approach are provided. This includes a dependability analysis of the distributed approach versus centralized systems

A middleware-driven architecture for information dissemination in distributed sensor networks

We present a hybrid, multilayered, agent-oriented architecture for distributed sensor networks. We propose an agent-based middleware that bridges the gap between the programmable application layer consisting of software agents and the physical layer consisting of extremely small, low power devices that combine programmable computing with sensing, tracking and wireless communication capabilities. The middleware supports mechanisms for communication, mobility, cooperative data mining, querying, tasking, self-organization and administration of the constituent sensor nodes to build higher-level, interoperable applications with software agents. We discuss two major middleware components, namely, CCL, a command and control language, and STACS, a programmable controller synthesizer. We explain how the controller can be used to configure the behavior of a node and CCL to command and control the nodes in a sensor network. We provide frameworks for communication and mobility. We suggest an agent model that integrates the software agents of the application with the hardware agents of the physical environment. We describe an integrated target tracking experiment, to demonstrate an implementation of this architecture. Finally, we conclude with an insight to our future challenges.

Hybrid life-extending control of mechanical systems: experimental validation of the concept

The goal of life-extending control is to achieve high performance of complex dynamical systems (e.g., aircraft, spacecraft, and energy-conversion systems) without overstraining the mechanical structures and the potential benefit is an increase in the service life of critical components with no significant loss of performance. This paper presents a two-tier architecture and a design methodology of hybrid (i.e., combined discrete-event and continuously varying) life-extending control for structural durability and high performance of mechanical systems. A feedback controller at the lower tier is designed with due consideration to robust performance and damage mitigation. A variable-structure stochastic automaton is employed at the lower tier for status evaluation of structural damage while the overall system performance is maintained by the supervisory level discrete-event controller at the upper tier. Experimental results on a laboratory test apparatus are presented for validation of the proposed concept of hybrid life-extending control.

Sensor network based localization and target tracking through hybridization in the operational domains of beamforming and dynamic space-time clustering

The severe power, time and processing constraints on ad hoc wireless sensor networks for area surveillance require in-situ adaptations to conserve resources and optimize performance. In particular, it may be necessary to make dynamic tradeoffs between centralized processing algorithms, like beamforming, and knowledge based distributed processing algorithms like dynamic space-time clustering (DSTC) that rely on local processing of raw sensor data. Beamforming methods can achieve high levels of accuracy in estimating direction of arrival with a sound wave even when the source is in the far field. Hence accurate localization can be achieved with a relatively sparse sensor network. However, beamforming has severe limitations when the number of nodes increases. It requires orders of magnitude higher energy for transporting the entire time series over the network. DSTC methods, on the other hand, work well when the number of nodes is large because clusters can be formed within a smaller space-time window. This work examines the operational domains of the two centralized and distributed algorithms by analyzing sources of error, dependence on sensor density, sensor geometries, energy usage, control logic for data processing and the effects of network topology on the two algorithms. Based on this analysis, we develop hybrid algorithms that take advantage of the operational characteristics of each one in designing a high performance sensor network.