Distributed estimation in wireless sensor networks with channel-aware slotted aloha

Abstract

A cross-layered slotted ALOHA protocol is proposed for distributed estimation in sensor networks. Suppose that the sensors in the network record local measurements of a common event and report the data back to the fusion center through direct transmission links. We employ a channel-aware transmission control where the transmission probability of each sensor depends on the quality of the local observation and the conditions of the transmission channel in each time slot. In contrast to conventional ALOHA systems, our goal is to minimize the accumulated estimation error at each instant in time as opposed to maximizing the system throughput. We show that the transmission probability which achieves the maximum throughput does not provide the best solution to the distributed estimation problem. Two strategies are proposed: the maximum mean-square-error (MSE) reduction (MMR) method and the suboptimal two-mode MSE-reduction method. The first scheme maximizes the reduction in MSE for each transmission but requires the information of the number of active sensors and the accumulated estimation accuracy before each time slot. In the second scheme, the sensors utilize the interchange between two fixed transmission control policies without the explicit knowledge of the system parameters required in the first scheme.