The Sensing Capacity of Sensor Networks

This paper demonstrates fundamental limits of sensor networks for detection problems where the number of hypotheses is exponentially large. Such problems characterize many important applications including detection and classification of targets in a geographical area using a network of sensors, and detecting complex substances with a chemical sensor array. We refer to such applications as largescale detection problems. Using the insight that these problems share fundamental similarities with the problem of communicating over a noisy channel, we define a quantity called the sensing capacity and lower bound it for a number of sensor network models. The sensing capacity expression differs significantly from the channel capacity due to the fact that a fixed sensor configuration encodes all states of the environment. As a result, codewords are dependent and non-identically distributed. The sensing capacity provides a bound on the minimal number of sensors required to detect the state of an environment to within a desired accuracy. The results differ significantly from classical detection theory, and provide an ntriguing connection between sensor networks and communications. In addition, we discuss the insight that sensing capacity provides for the problem of sensor selection.Comment: Submitted to IEEE Transactions on Information Theory, November 200

Throughput optimization for data collection in wireless sensor networks

Wireless sensor networks are widely used in many application domains in recent years. Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this dissertation, we aim to study the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Various communication scenarios are considered in our analysis, such as with a single sink or multiple sinks, randomly-deployed or arbitrarily- deployed sensors, and different communication models. For both randomly-deployed and arbitrarily-deployed sensor networks, an efficient collection algorithm has been proposed under protocol interference model and physical interference model respec- tively. We can prove that its performance is within a constant factor of the optimal for both single sink and regularly-deployed multiple sinks cases. We also study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss per- formance implications. In addition, we further discuss the problem of data collection capacity under Gaussian channel model

Linear Finite-Field Deterministic Networks With Many Sources and One Destination

We find the capacity region of linear finite-field deterministic networks with many sources and one destination. Nodes in the network are subject to interference and broadcast constraints, specified by the linear finite-field deterministic model. Each node can inject its own information as well as relay other nodes' information. We show that the capacity region coincides with the cut-set region. Also, for a specific case of correlated sources we provide necessary and sufficient conditions for the sources transmissibility. Given the "deterministic model" approximation for the corresponding Gaussian network model, our results may be relevant to wireless sensor networks where the sensing nodes multiplex the relayed data from the other nodes with their own data, and where the goal is to decode all data at a single "collector" node.Comment: 5 pages, 3 figures, submitted to ISIT 201

Location Management in Wireless Sensor Networks with Mobility

Wireless sensor networks comprise motes which are nothing but small sensor devices. The challenging problems for motes are battery power, storage capacity, and less calculation power of the mote. In this paper developed structure for Real-Time Tracking, Sensing and Management System using IITH motes is proposed. Also the algorithm developed for location management of wireless sensor networks with the aspect of mobility is proposed. This developed framework and algorithm can be utilized in emergency events and safety threats and provides warning signals to handle the emergency

No-Sense: Sense with Dormant Sensors

Wireless sensor networks (WSNs) have enabled continuous monitoring of an area of interest (body, room, region, etc.) while eliminating expensive wired infrastructure. Typically in such applications, wireless sensor nodes report the sensed values to a sink node, where the information is required for the end-user. WSNs also provide the flexibility to the end-user for choosing several parameters for the monitoring application. For example, placement of sensors, frequency of sensing and transmission of those sensed data. Over the years, the advancement in embedded technology has led to increased processing power and memory capacity of these battery powered devices. However, batteries can only supply limited energy, thus limiting the lifetime of the network. In order to prolong the lifetime of the deployment, various efforts have been made to improve the battery technologies and also reduce the energy consumption of the sensor node at various layers in the networking stack. Of all the operations in the network stack, wireless data transmission and reception have found to consume most of the energy. Hence many proposals found in the literature target reducing them through intelligent schemes like power control, reducing retransmissions, etc. In this article we propose a new framework called Virtual Sensing Framework (VSF), which aims to sufficiently satisfy application requirements while conserving energy at the sensor nodes.Comment: Accepted for publication in IEEE Twentieth National Conference on Communications (NCC-2014

An Architecture for On-Demand Wireless Sensor Networks

abstract: Majority of the Sensor networks consist of low-cost autonomously powered devices, and are used to collect data in physical world. Today's sensor network deployments are mostly application specific & owned by a particular entity. Because of this application specific nature & the ownership boundaries, this modus operandi hinders large scale sensing & overall network operational capacity. The main goal of this research work is to create a mechanism to dynamically form personal area networks based on mote class devices spanning ownership boundaries. When coupled with an overlay based control system, this architecture can be conveniently used by a remote client to dynamically create sensor networks (personal area network based) even when the client does not own a network. The nodes here are "borrowed" from existing host networks & the application related to the newly formed network will co-exist with the native applications thanks to concurrency. The result allows users to embed a single collection tree onto spatially distant networks as if they were within communication range. This implementation consists of core operating system & various other external components that support injection maintenance & dissolution sensor network applications at client's request. A large object data dissemination protocol was designed for reliable application injection. The ability of this system to remotely reconfigure a network is useful given the high failure rate of real-world sensor network deployments. Collaborative sensing, various physical phenomenon monitoring also be considered as applications of this architecture.Dissertation/ThesisM.S. Computer Science 201

Sensing Capacity for Markov Random Fields

This paper computes the sensing capacity of a sensor network, with sensors of limited range, sensing a two-dimensional Markov random field, by modeling the sensing operation as an encoder. Sensor observations are dependent across sensors, and the sensor network output across different states of the environment is neither identically nor independently distributed. Using a random coding argument, based on the theory of types, we prove a lower bound on the sensing capacity of the network, which characterizes the ability of the sensor network to distinguish among environments with Markov structure, to within a desired accuracy.Comment: To appear in the proceedings of the 2005 IEEE International Symposium on Information Theory, Adelaide, Australia, September 4-9, 200