Building Context Aware Network of Wireless Sensors Using a Scalable Distributed Estimation Scheme for Real-time Data Manipulation

Abstract

Wireless sensor networks are an “exciting emerging domain of deeply networked systems of low-power wireless motes with a tiny amount of CPU and memory and large federated networks for high-resolution sensing of the environment” (Welsh et al., 2004). The capability to support plethora of new diverse applications has placed Wireless Sensor Network technology at threshold of an era of significant potential growth. The technology is advancing rapidly under the push of the new technological developments and the pull of vast and diverse potential applications. The near ubiquity of the internet coupled with recent engineering achievements, are opening the door to a new generation of low-cost and powerful sensor devices which are capable of delivering high-grade spatial and temporal resolution. In that regard, distributed estimation and tracking is one of the most fundamental collaborative information processing challenges in wireless sensor networks (WSNs). Moreover, estimation issues in wireless networks with packet-loss are gaining lion share of attention over the last few years. However, due to the inherent limitations of WSNs in terms of power and computational resources, deploying any distributed estimation technique within WSNs requires modifying the existing methods to address those limitations effectively. In fact, the problem with current approaches lies in the significant increase in the computational expenses of the deployed methods as the result of increase in the size of the network. This increase puts a heavy practical burden on deployment of those algorithms for resource-constrained wireless sensor networks. Current decentralized Kalman filtering involves state estimation using a set of local Kalman filters that communicate with all other nodes. The information flow is all-to-all with approximate communication complexity of O(n2) which is not scalable for WSNs. In this chapter an attempt is made to explore new ways in provisioning distributed estimation in WSNs by introducing a light-weight distributed pattern recognition scheme which provides single-cycle learning and entails a large number of loosely coupled parallel operations. In fact, the focus of our approach would be on a novel scalable and distributed filtering scheme in which each node only communicates messages with its neighbours on a network to 1