Communication and Energy Optimization of Local PSO-assisted Multi-UAVs for Moving Targets Exploration

Limited resources, such as energy, processing power, memory, and communication bandwidth, limit the usage of multi-UAV systems in the target search domain. The limitation of energy, in particular, has a significant impact on the system’s performance, especially because the overall energy consumption is frequently dominated by the cost of communication, i.e. the computational and sensing energy are insignificant in comparison to the communication energy consumption. As a result, the system’s lifetime may be considerably prolonged by reducing the communication as well as the volume of communication data, at the penalty of increased computing cost. This work presents a hierarchic approach for allocating multi-UAV resources based on a cooperative and competitive particle swarm optimization (PSO) algorithm, with the goal of achieving an optimal balance between communication and processing energy. According to the simulation findings, our technique can save a substantial amount of energy when compared to a benchmark strategy namely random search (RS) and PSO.