Performance Optimization of Wireless Video Sensor Networks using Swarm Optimization with Convex Mapping

A wireless video sensor network (WVSN) is a system of spatially distributed video sensors which capture, process and transmit video information over a wireless ad hoc network. The performance optimization in WVSN is a nonlinear high-dimension constrained optimization problem. In this work, we consider the unique characteristics of WVSN and develop an evolutionary optimization scheme using a swarm intelligence principle to solve the WVSN performance optimization problem. We transform the solution space defined by flow balance and energy constraints into a convex region in a low-dimensional space. We then merge the convex condition with the swarm intelligence principle to guide the movement of each particle during the evolutionary optimization process. Our theoretical analysis and experimental results demonstrate that the proposed performance optimization scheme is very efficient.