Energy-Efficient Computing Offloading With Trajectory Optimization and Resource Allocation in UAVs Aided Industrial IoT

In recent years, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) has emerged as a novel paradigm for providing computing services to devices in the industrial Internet of Things (IIoT) domain. However, due to the constraints of a single UAV’s battery and processing capabilities, it is unable to fulfill the computational and communication requirements of IIoT devices. We presents a multi-UAV-assisted IIoT system wherein computational services for IIoT devices are collaboratively provided by a terrestrial base station (BS) and UAVs. Considering the impact of energy consumption on the environment, we formulate a long-term weighted system energy consumption minimization problem by jointly optimizing UAVs trajectories, offloading and resource allocation decisions while considering the constraints of task deadlines. Due to the network dynamics and complexity of the optimization problem, we reformulate it as a Markov decision process (MDP) and apply a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach to solve the problem. The simulation results obtained show that our proposed MATD3-based method achieves 6% higher rewards and 30% faster convergence over previous algorithms such as multi-agent deep deterministic policy gradient and the proximal policy optimization.