Trajectory Planning, Phase Shift Design, and IoT Devices Association in Flying-RIS-Assisted Mobile Edge Computing

With the blossom of Internet of Things (IoT) technology, the big data volumes raised by the large number of IoT devices have posed great burden on the communication and computing network. Considering the advantages of reflecting intelligent surface (RIS), mobile edge computing (MEC), and unmanned aerial vehicle (UAV), this article proposes a flying-RIS-assisted MEC system to assist offloading services to alleviate the ground computation burden in IoT. The UAV equipped with RIS is dispatched to fly over a specific area to assist in offloading the ground’s computing mission to MEC server situated nearby access point (AP) in IoT. The cost of the IoT device is introduced as the weighted sum of the device’s energy consumption and the time consumed to accomplish all computation tasks. To prolong the lifetime and guarantee the communication quality of the IoT devices, this article minimizes the sum cost of all IoT devices by collaboratively planning UAV’s trajectory, scheduling the IoT devices’ association with flying-RIS, and optimizing the phase shift value of each reflecting components. To address the posed nonconvex optimization challenge, a deep deterministic policy gradient (DDPG)-based algorithm is brought forward. Besides, the state and action normalization mechanism is used to ease up on the training difficulty. Finally, the numerical simulation results prove the superiority of the proposed algorithm compared with other benchmark schemes.