Learning With Distributed Data in Wireless Sensor Network

Abstract

Wireless sensor networks (WSN) have been proposed as a solution to environment sensing, target tracking, data collection and others. WSN collect an enormous amount of data over space and time. The objective is to estimate of a parameter or function from these data. Learning is used in detection and estimation problems when no probablistic model relating an observation. This paper investigates a general class of distributed algorithms for data processing, eliminating the need to transmit raw data to a central processor. This can provide significant reductions in the amount of communication and energy required to obtain an accurate estimate. The estimation problems we consider are expressed as the optimization of a cost function involving data from all sensor nodes. Here the distributed algorithm is based on an incremental optimization process. A parameter estimate is circulated through the network, and along the way each node makes a small adjustment to the estimate based on its local data.