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A smart sensor node has been developed which has (a) the ability to sense strain of the structure under observation (b) process this raw sensor data in cooperation with its neighbors and (c) transmit the information to the end user. These sensor nodes are interconnected by a loosely coupled network called sensor network. The network is designed to be self organizing in the sense of establishing and maintaining the inter node connectivity without the need for human intervention. For the envisioned application of structural health monitoring, wireless communication is the most practical solution not only because they eliminate interconnecting wires but also for their ability to establish communication links even in inaccessible regions. But wireless network brings with it a number of issues such as interference, fault tolerant self organizing, multi-hop communication, energy efficiency, routing and finally reliable operation in spite of massive complexity of the system. This paper addresses the issue of fault tolerant self organizing in wireless sensor networks. We propose a new architecture called the Redundant Tree Network (RTN). RTN is a hierarchical network which exploits redundant links between nodes to provide reliability.

Multipath and shadow fading are the primary cause for positioning errors in a Received Signal Strength Indicator (RSSI) based localization scheme. While fading, in general, is detrimental to localization accuracy, cross-correlation and divergence properties of shadow fading residuals may be utilized to improve localization and tracking accuracy of mobile IEEE 802.15.4 transmitters. Therefore, this paper begins by presenting a stochastic filter that models the fast changing multipath fading as a mean reverting Ornstein-Uhlenbeck (OU) process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering to isolate the slow changing shadow fading residuals from measured RSSI values. Subsequently, a novel wireless transmitter localization scheme that combines the measured cross-correlation in shadow fading residuals between adjacent receivers using a Student-t Copula likelihood function is proposed. However, the long convergence time for this highly non-convex copula function might render our method unsuitable for tracking applications. Therefore, we present a faster tracking method where the velocity and heading of a mobile transmitter are estimated from α-Divergence between shadow fading signals and an onboard gyroscope respectively. To bind the localization error in this tracking method, the transmitter location estimates are smoothed by a Bayesian particle filter. The performance of our proposed localization and tracking method is validated over simulations and hardware experiments.

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