Collaborative Beamforming in Wireless Sensor Networks Using a Novel Particle Swarm Optimization Algorithm Variant

Abstract

Wireless Sensor Network (WSN) nodes inherently feature limited transmission range. A recent solution to the long range transmission problem is Collaborative Beamforming (CBF). The outcome of an ideal CBF process ought to be a radiation pattern whose main beam is oriented towards the intended transmission destination (WSN sink), inherently increasing the transmission range. Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF from the perspective of a 3 dimension WSN configuration (wherein the sink is elevated from the nodes’ plane). The modifications done on the PSO algorithm entail use of fuzzy-adaptive confidence and inertia weight parameters alongside a particle culling procedure. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. Comparisons against use of a linearly-adaptive PSO algorithm variant, basic PSO algorithm and the Genetic Algorithm (GA) in CBF have established the superiority of the proposed CFAPSO algorithm. The CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering. It is noteworthy that conventional beamsteering is inapplicable to beam-pattern optimization with respect to processes such as sidelobe minimization, nulling among others. The developed CFAPSO algorithm befits utilization in a plethora of CBF schemes.