Channel-Aware Random Access Control for Distributed Estimation in Sensor Networks

Abstract

A cross-layered slotted ALOHA protocol is proposed and analyzed 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 is chosen according to the quality of its local observation and transmission channels. As opposed to maximizing the system throughput, our goal is to design transmission control policies that optimize the estimation performance. Two transmission control strategies are proposed: the maximum mean-square-error (MSE) reduction (MMR) scheme and the suboptimal two-mode MSE-reduction (TMMR) scheme. The MMR maximizes the MSE-reduction of the estimate after each time slot. However, this method requires knowledge of the number of active sensors and the accumulated estimation performance in each time slot, which must be provided through feedback from the fusion center. In TMMR, the sensors switch between two predetermined transmission control functions without explicit knowledge of the estimation performance and the number of active sensors in each time slot. Moreover, we notice that, if new observations are made by the sensors in each time slot, diversity combining techniques can be employed to fully exploit the data that the sensors measure over their idle time slots. Specifically, we perform selective combining on the observations that are made in between transmissions. As a result, we are able to exploit both the spatial and temporal diversity gains inherent in the multisensor system.