A Time-Critical Low Complexity Distributed Self Organizing Hierarchical Particle Swarm Optimization Algorithm for Wireless Sensor Networks

This work explores the possibility of reducing computations in a distributed estimation setup, utilizing a self organizing hierarchical particle swarm optimization algorithm. Reducing computations is essential for any application using wireless sensors in a distributed network, as the sensors need to save processing power in order to last longer. An event triggering approach is used to remove redundant computations from the algorithm. This results in a slight degradation in performance but the trade-off is a significant reduction in computations. In practical applications, such as intruder detection in a cybersecurity setup, the proposed algorithm can prove to be an asset with its rapid estimation compared with the standard algorithms.