Memory Distributed LMS for Wireless Sensor Networks

Abstract EN

Due to the limited communication resource and power, it is usually infeasible for sensor networks to gather data to a central processing node.

Distributed algorithms are an efficient way to resolve this problem.

In the algorithms, each sensor node deals with its own input data and transmits the local results to its neighbors.

Each node fuses the information from neighbors and its own to get the final results.

Different from the existing work, in this paper, we present an approach for distributed parameter estimation in wireless sensor networks based on the use of memory.

The proposed approach consists of modifying the cost function by adding extra statistical information.

A distributed least-mean squares (d-LMS) algorithm, called memory d-LMS, is then derived based on the cost function and analyzed.

The theoretical performances of mean and mean square are analyzed.

Moreover, simulation results show that the proposed algorithm outperforms the traditional d-LMS algorithm in terms of convergence rate and mean square error (MSE) performance.