Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

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.

Automated summary

This paper investigates the optimization problem of IoT devices' offloading decisions, CPU frequencies, and transmit powers in a multi-mobile edge computing server and multi-IoT device cellular network. A deep reinforcement learning-based optimization algorithm is proposed to solve the nonconvex problem, and simulation results demonstrate the correctness and effectiveness of the algorithm.