Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm.

Unmanned aerial vehicles (UAVs) have been envisioned as a promising technique to provide relaying and mobile edge computing (MEC) services for ground user equipment (UE). In this paper, we propose a UAV-assisted MEC architecture in dynamic environment, where a UAV flies with a fixed trajectory and may act as a MEC server to process the tasks offloaded from the UE or act as a relay to help the UE to offload their tasks to the ground base station (BS). The objective of this work is to maximize the long-term number of completed tasks of the UE. An optimization problem is formulated to optimize the task offloading decisions of the UE. Considering the random demands of the UE, a deep reinforcement learning- (DRL-) based algorithm is proposed to solve the formulated nonconvex optimization problem. Simulation results verify the effectiveness and correctness of the proposed algorithm.

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