Wildlife Surveillance using Wireless Visual Sensors based on hybrid Grey Wolf and Particle Swarm Optimization (H-G-P)

Abstract

Next generation of Wild Life Surveillance System (WLS) is envisaged to be equipped with Wireless Visual Sensors (VSN) along with powerful Artificial Intelligence (AI) and Deep Learning (DL) module. VSN are flexible and energy efficient but the performance is greatly affected by inherent frequent data advertisements and overheads for monitoring mobile animals.  In this context, algorithms and optimization approaches have been investigated to perform different types of quality assessment and performance enhancement. Proposed work presents a faster method of optimum selection of Visual Sensors for maximum coverage of the predefined surveillance space. Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are carefully investigated, and a hybrid module is proposed accommodating the merits of both the algorithms. The hybrid module is capable of addressing agile and scrum approaches.   The other sets of VSs in the VSN provides feasible locations for wider coverage using an optimized search algorithm. The problem of optimum VS selection for maximum coverage considering both static and randomly moving obstacles is mapped as a GWO-PSO Optimization (GWO) problem. The results show that the proposed hybrid algorithm (H-G-P) could indeed reduce the computational time and converge very fast by an order, during wild life surveillance.