Performance Optimisation of Wireless Visual Sensors for Wild Life Monitoring

Wireless Visual Sensor Network (VSN) has revolutionized the emerging world of Internet of Things, providing visual data as images and videos for emergency detection, localization, tracking, pattern recognition etc.  For wild life surveillance, very limited research is explored using VSNs due to visual occlusion and dynamics of coverage failures. 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. The Wild life habitat is modeled as surveillance space where occlusion (obstacle) is impairing the performance of VS. 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 Grey Wolf Optimization (GWO) problem. The proposed algorithm is computationally lighter and converges very fast as compared to Contemporary Genetic Algorithms (GA).