Distributed Estimation in Clustered Wireless Sensor Networks

Abstract

We consider the problem of distributed estimation of a deterministic vector parameters using in clustered wireless sensor networks (WSNs). The paper using the method of multipliers in conjunction with a block coordinate descent approach, we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation based on maximum likelihood estimators (MLE) in nonlinear and non-Gaussian data models. the iterative algorithms based on the communication between nodes and the head node that generate a local estimation, and the head nodes can be seen as the neighbor node of all the other nodes in the cluster, it can broadcast its estimation in the cluster, and the local iterates converge to the global MLE, We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, corroborating simulations demonstrate the merits of the novel distributed estimation algorithms.