Localized Recursive Estimation in Energy Constrained Wireless Sensor Networks

Abstract

This paper proposes a localized recursive estimation scheme for parameter estimation in wireless sensor networks. Given any parameter of a target occurring at some location and time, a number of sensors recursively estimate the parameter by using their local measurements of the parameter that is attenuated with the distance between a sensor and the target location and corrupted by noise. Compared with centralized estimation schemes that transmit all encoded measurements to a sink (or a fusion center), the recursive scheme needs only to transmit the final estimate to a sink. When the sink is faraway from the sensors and multihop communications have to be used, using localized recursive estimation can help to reduce energy consumption and reduce network traffic load. A sensor sequence with the fastest convergence rate is identified, by which the variance of estimation error reduces faster than all other sequences. In the case of adjustable transmission power, a heuristic has been proposed to find a sensor sequence with the minimum total transmission power when performing the recursive estimation. Numerical examples have been used to compare the performance of the proposed scheme with that of a centralized estimation scheme and have also shown the effectiveness of the proposed heuristic.