Abstract

In most current wireless sensor network (WSN) implementations, information is communicated (via multiple hops if necessary) to a sink or central processor, which then computes the central (global) estimate of the phenomena of interest. However, in large networks, nodes that are near the sink are heavily burdened by relaying packets from more distant nodes. Such inequitable load sharing can quickly lead to traffic congestion and node energy depletion. Instead, it is beneficial to distribute computation throughout the network, with each node performing data fusion and compression. In the distributed estimation approach presented here, raw measurements collected at each sensor are processed locally to generate local estimates of the states of interest. The sufficient statistics of these estimates provide a lossless compression of all measurement data used to generate the local estimates. Information packets based on these sufficient statistics are then transmitted to other nearby nodes and fused. The communication and computation burden is shared equally across nodes, leading to improved network longevity and scalability. Additional benefits of this distributed architecture include real-time situational awareness within the network, robustness to node and sink failures, and the capability of ad hoc operation in the absence of any infrastructure.