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

In wireless visual sensor networks, the generation and transmission of huge amounts of image data consume much energy of sensor nodes (SNs), and their routing and processing take quite a long time. It is of great importance to shorten event reporting delay (ERD) and prolong network lifetime, which can be achieved by the appropriate deployment of edge nodes (ENs) that can not only collect but also process data. This work investigates how to jointly optimize SN deployment, EN deployment, data routing, and data offloading to minimize the number of deployed SNs, the number of deployed ENs, and ERD 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.