Multiobjective Optimized Deployment of Edge-Enabled Wireless Visual Sensor Networks for Target Coverage

Abstract

In wireless visual sensor networks, the generation and transmission of huge amounts of image data consume much energy of sensor nodes, and their routing and processing take quite a long time. It is of great importance to shorten event reporting delay and prolong network lifetime, which can be achieved by the appropriate deployment of edge nodes that can not only collect but also process data. This work investigates how to jointly optimize sensor node deployment, edge node deployment, data routing, and data offloading to minimize the number of deployed sensor nodes, the number of deployed edge nodes, and event reporting delay and maximize network lifetime. We formulate this problem as a mixed integer nonlinear program and propose a multiobjective differential evolution algorithm to solve it. A large number of simulation results demonstrate that it can deliver a more accurate Pareto set than the nondominated sorting genetic algorithm III.