Cooperative parameter estimation using PSO in ad-hoc WSN

Abstract

In this work, a particle swarm optimization (PSO) algorithm is used to cooperatively estimate a monitored parameter by sensor nodes in an ad-hoc wireless sensor network (WSN). In the proposed algorithm, every sensor node of a wireless sensor network is equipped with a modified particle swarm optimization (MPSO) algorithm to estimate a parameter of interest. A diffusion scheme is used to cooperatively estimate this parameter by sharing the local best particle and the corresponding particle error value to the neighboring nodes. Thus the performance of the wireless sensor network is improved by exploiting the spatial and temporal diversity of the network by collaboratively estimating this parameter. The simulation results show that the diffusion MPSO (DMPSO) algorithm outperforms the non-cooperative MPSO (NCMPSO) algorithm, the diffusion least-mean-squares (DLMS) algorithm and the diffusion recursive-least-squares (DRLS) algorithm by considerable margin.