Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

Abstract

Collaborative Beamforming (CBF) is an essential tool towards increasing transmission range in Wireless Sensor Networks (WSNs). Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. Particle Swarm Optimization (PSO) algorithm is a good candidate for use in CBF owing to its simplicity and low computation complexity. However, the basic PSO algorithm suffers from premature convergence particularly in highly multimodal functions (typical of CBF). This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF. A new fuzzy-logic based confidence and inertia weight parameters adaptation scheme has been developed with an aim of enhancing exploration and exploitation capabilities of the PSO algorithm. Normalized particle quality and iteration count have been used as the inputs to the designed fuzzy-logic inference system. The fuzzy logic based parameters adaptation scheme has been implemented in the form of a lookup table to minimize “on-line” computation complexity. Furthermore, a particle culling/ re-initialization procedure is utilized at half the number of maximum iterations to enhance overall swarm diversity. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. The developed CFAPSO algorithm is noted to outperform other metaheuristic algorithms in a statistical performance analysis procedure (on the basis of a set of standard unimodal and multimodal functions). Upon application to CBF, the CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering.