S. Sitharama Iyengar

Grid coverage for surveillance and target location in distributed sensor networks

We present novel grid coverage strategies for effective surveillance and target location in distributed sensor networks. We represent the sensor field as a grid (two or three-dimensional) of points (coordinates) and use the term target location to refer to the problem of locating a target at a grid point at any instant in time. We first present an integer linear programming (ILP) solution for minimizing the cost of sensors for complete coverage of the sensor field. We solve the ILP model using a representative public-domain solver and present a divide-and-conquer approach for solving large problem instances. We then use the framework of identifying codes to determine sensor placement for unique target location, We provide coding-theoretic bounds on the number of sensors and present methods for determining their placement in the sensor field. We also show that grid-based sensor placement for single targets provides asymptotically complete (unambiguous) location of multiple targets in the grid.

Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks

We propose a distributed solution for a canonical task in wireless sensor networks - the binary detection of interesting environmental events. We explicitly take into account the possibility of sensor measurement faults and develop a distributed Bayesian algorithm for detecting and correcting such faults. Theoretical analysis and simulation results show that 85-95 percent of faults can be corrected using this algorithm, even when as many as 10 percent of the nodes are faulty.

Multiresolution data integration using mobile agents in distributed sensor networks

We describe the use of the mobile agent paradigm to design an improved infrastructure for data integration in a distributed sensor network (DSN). We use the acronym MADSN to denote the proposed mobile-agent-based DSN. Instead of moving data to processing elements for data integration, as is typical of a client/server paradigm, MADSN moves the processing code to the data locations. This saves network bandwidth and provides an effective means for overcoming network latency, since large data transfers are avoided. Our major contributions are the use of mobile agent in DSN for distributed data integration and the evaluation of performance between DSN and MADSN approaches. We develop an enhanced multiresolution integration (MRI) algorithm where multiresolution analysis is applied at a local node before accumulating the overlap function by mobile agent. Compared to the MRI implementation in DSN, the enhanced integration algorithm saves up to 90% of the data transfer time. We develop objective functions to evaluate the performance between DSN and MADSN approaches. For a given set of network parameters, we analyze the conditions under which MADSN performs better than DSN and determine the condition under which MADSN reaches its optimum performance level.

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.

Distributed Sensor Networks

An Overview, S.S. Iyengar, Ankit Tandon, and R.R. Brooks Microsensor Applications, David Shepherd A Taxonomy of Distributed Sensor Networks, Shivakumar Sastry and S.S. Iyengar Contrast with Traditional Systems, R.R. Brooks Digital Signal Processing Background, Yu Hen Hu Image-Processing Background Lynne Grewe and Ben Shahshahani Object Detection and Classification, Akbar M. Sayeed Parameter Estimation David Friedlander Target Tracking with Self-Organizing Distributed Sensors R.R. Brooks, C. Griffin, D.S. Friedlander, and J.D. Koch Collaborative Signal and Information Processing: An Information-Directed Approach Feng Zhao, Jie Liu, Juan Liu, Leonidas Guibas, and James Reich Environmental Effects, David C. Swanson Detecting and Counteracting Atmospheric Effects Lynne L. Grewe Signal Processing and Propagation for Aeroacoustic Sensor Networks, Richard J. Kozick, Brian M. Sadler, and D. Keith Wilson Distributed Multi-Target Detection in Sensor Networks Xiaoling Wang, Hairong Qi, and Steve Beck Foundations of Data Fusion for Automation S.S. Iyengar, S. Sastry, and N. Balakrishnan Measurement-Based Statistical Fusion Methods For Distributed Sensor Networks, Nageswara S.V. Rao Soft Computing Techniques, R.R. Brooks Estimation and Kalman Filters, David L. Hall Data Registration, R.R. Brooks, Jacob Lamb, and Lynne Grewe Signal Calibration, Estimation for Real-Time Monitoring and Control, Asok Ray and Shashi Phoha Semantic Information Extraction, David Friedlander Fusion in the Context of Information Theory Mohiuddin Ahmed and Gregory Pottie Multispectral Sensing, N.K. Bose Coverage-Oriented Sensor Deployment Yi Zou and Krishnendu Chakrabarty Deployment of Sensors: An Overview S.S. Iyengar, Ankit Tandon, Qishi Wu, Eungchun Cho, Nageswara S.V. Rao, and Vijay K. Vaishnavi Genetic Algorithm for Mobile Agent Routing in Distributed Sensor Networks, Qishi Wu, S.S. Iyengar, and Nageswara S.V. Rao Computer Network - Basic Principles, Suresh Rai Location-Centric Networking in Distributed Sensor Networks, Kuang-Ching Wang and Parameswaran Ramanathan Directed Diffusion, Fabio Silva, John Heidemann, Ramesh Govindan, and Deborah Estrin Data Security Perspectives, David W. Carman Quality of Service Metrics, N. Gautam Network Daemons for Distributed Sensor Networks Nageswara S.V. Rao and Qishi Wu Designing Energy-Aware Sensor Systems N. Vijaykrishnan, M.J. Irwin, M. Kandemir, L. Li, G. Chen, and B. Kang Operating System Power Management Vishnu Swaminathan and Krishnendu Chakrabarty An Energy-Aware Approach for Sensor Data Communication, H. Saputra, N. Vijaykrishnan, M. Kandemir, R. Brooks, and M.J. Irwin Compiler-Directed Communication Energy Optimizations for Microsensor Networks, I. Kadayif, M. Kandemir, A. Choudhary, M. Karakoy, N. Vijaykrishnan, and M.J. Irwin Sensor-Centric Routing in Wireless Sensor Networks Rajgopal Kannan and S.S. Iyengar Query Processing in Sensor Networks Samuel Madden and Johannes Gehrke Autonomous Software Reconfiguration, R.R. Brooks Mobile Code Support, R.R. Brooks and T. Keiser The Mobile-Agent Framework for Collaborative Processing in Sensor Networks, Hairong Qi, Yingyue Xu, and Teja Phani Kuruganti Distributed Services, Alvin S. Lim Adaptive Active Querying, Bhaskar Krishnamachari Need for Self-Configuration, R.R. Brooks Emergence, R.R. Brooks Biological Primitives, M. Pirretti Physics and Chemistry, Mengxia Zhu, Richard Brooks, Matthew Pirretti, and S.S. Iyengar Collective Intelligence for Power-Aware Routing in Mobile Ad Hoc Sensor Networks Vijay S. Iyer, S.S. Iyengar, and N. Balakrishnan Random Networks and Percolation Theory R.R. Brooks On the Behavior of Communication Links in a Multi-Hop Mobile Environment , Prince Samar and Stephen B. Wicker Example Distributed Sensor Network Control Hierarchy Mengxia Zhu, S.S. Iyengar, Jacob Lamb, R.R. Brooks, and Matthew Pirretti SenSoft: Development of a Collaborative Sensor Network Gail Mitchell, Jeff Mazurek, Ken Theriault, and Prakash Manghwani Statistical Approaches to Cleaning Sensor Data Eiman Elnahrawy and Badri Nath Plant Monitoring with Special Reference to Endangered Species, K.W. Bridges and Edo Biagioni Designing Distributed Sensor Applications for Wireless Mesh Networks, Robert Poor and Cliff Bowman Beamforming, J.C. Chen and K. Yao

Robot navigation in unknown terrains: Introductory survey of non-heuristic algorithms

A formal framework for navigating a robot in a geometric terrain by an unknown set of obstacles is considered. Here the terrain model is not a priori known, but the robot is equipped with a sensor system (vision or touch) employed for the purpose of navigation. The focus is restricted to the non-heuristic algorithms which can be theoretically shown to be correct within a given framework of models for the robot, terrain and sensor system. These formulations, although abstract and simplified compared to real-life scenarios, provide foundations for practical systems by highlighting the underlying critical issues. First, the authors consider the algorithms that are shown to navigate correctly without much consideration given to the performance parameters such as distance traversed, etc. Second, they consider non-heuristic algorithms that guarantee bounds on the distance traversed or the ratio of the distance traversed to the shortest path length (computed if the terrain model is known). Then they consider the navigation of robots with very limited computational capabilities such as finite automata, etc.

Distributed sensor networks—a review of recent research

Abstract Advances in sensor technology and computer networks have enabled distributed sensor networks (DSNs) to evolve from small clusters of large sensors to large swarms of micro-sensors, from fixed sensor nodes to mobile nodes, from wired communications to wireless communications, from static network topology to dynamically changing topology. However, these technological advances have also brought new challenges to processing large amount of data in a bandwidth-limited, power-constraint, unstable and dynamic environment. This paper reviews recent developments in DSNs from four aspects: network structure, data processing paradigm, sensor fusion algorithm with emphasis on fault-tolerant algorithm design, and optimal sensor deployment strategy.

Computing reliability and message delay for Cooperative wireless distributed sensor networks subject to random failures

One of the most compelling technological advances of this decade has been the advent of deploying wireless networks of heterogeneous smart sensor nodes for complex information gathering tasks. A wireless distributed sensor network (DSN) is a self-organizing , ad-hoc network of a large number of cooperative intelligent sensor nodes. Due to the limited power of sensor nodes, energy-efficient DSN are essentially multi-hop networks. The self-organizing capabilities, and the cooperative operation of DSN allow for forming reliable clusters of sensors deployed near, or at, the sites of target phenomena. Reliable monitoring of a phenomenon (or event detection) depends on the collective data provided by the target cluster of sensors, and not on any individual node. The failure of one or more nodes may not cause the operational data sources to be disconnected from the data sinks (command nodes or end user stations). However, it may increase the number of hops a data message has to go through before reaching its destination (and subsequently increase the message delay). In this paper, we focus on two related problems: computing a measure for the reliability of DSN, and computing a measure for the expected & the maximum message delay between data sources (sensors) & data sinks in an operational DSN. Given an estimation of the failure probabilities of the sensors, as well as the intermediate nodes (nodes used to relay messages between data sources, and data sinks), we use a probabilistic graph to model DSN. We define the DSN reliability as the probability that there exists an operating communication path between the sink node, and at least one operational sensor in a target cluster. We show that both problems are #P-hard for arbitrary networks. We then present two algorithms for computing the reliability, and the expected message delay for arbitrary networks. We also consider two special cases where efficient (polynomial time) algorithms are developed. Finally, we present some numerical results that demonstrate some of the applications of our algorithms.

Random asynchronous wakeup protocol for sensor networks

This paper presents a random asynchronous wakeup (RAW), a power saving technique for sensor networks that reduces energy consumption without significantly affecting the latency or connectivity of the network. RAW builds on the observation that when a region of a shared-channel wireless network has a sufficient density of nodes, only a small number of them need be active at any time to forward the traffic for active connections. RAW is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to be active. Each node is awake for a randomly chosen fixed interval per time frame. High node density results in existence of several paths between two given nodes whose path length and delay characteristics are similar to the shortest path. Thus, a packet can be forwarded to any of several nodes in order to be delivered to the destination without affecting much the path length and delay experienced by the packet as compared to forwarding the packet through the shortest path. The improvement in system lifetime, due to RAW, increases as the ratio of idle-to-sleep energy consumption increases, and as the density of the network increases. Through analytical and experimental evaluations, we show that RAW improves communication latency and system lifetime compared to current schemes.

Optimized broadcast protocol for sensor networks

Sensor networks usually operate under very severe energy restrictions. Therefore, sensor communications should consume the minimum possible amount of energy. White broadcasting is a very energy-expensive protocol, it is also widely used as a building block for a variety of other network layer protocols. Therefore, reducing the energy consumption by optimizing broadcasting is a major improvement in sensor networking. In this paper, we propose an optimized broadcast protocol for sensor networks (BPS). The major novelty of BPS is its adaptive-geometric approach that enables considerable reduction of retransmissions by maximizing each hop length. BPS adapts itself and gets the best out of existing radio conditions. In BPS, nodes do not need any neighborhood information, which leads to low communication and memory overhead. We analyze the worst-case scenario for BPS and show that the number of transmissions in such a scenario is a constant multiple of those required in the ideal case. Our simulation results show that BPS is very scalable with respect to network density. BPS is also resilient to transmission errors.