Optimized Transmission for Consensus in Wireless Sensor Networks
It addresses the problem of accurate decentralized parameter estimation in wireless sensor networks, but the approach is incremental, combining known techniques (Gram-Schmidt, power method) in a cyclic optimization framework.
This paper proposes an optimization algorithm for designing sensor gains in consensus-based decentralized estimation for wireless sensor networks, achieving improved estimation accuracy with low computational cost.
In this paper, we present a consensus-based framework for decentralized estimation of deterministic parameters in wireless sensor networks (WSNs). In particular, we propose an optimization algorithm to design (possibly complex) sensor gains in order to achieve an estimate of the parameter of interest that is as accurate as possible. The proposed design algorithm employs a cyclic approach capable of handling various sensor gain constraints. In addition, each iteration of the proposed design framework is comprised of the Gram-Schmidt process and power-method like iterations, and as a result, enjoys a low-computational cost.