Abstract
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.
Highlights
Smart city is a promising city paradigm, which improves the quality of experience (QoE) of citizens through advanced information and communication technologies (ICTs) infrastructure and enormous Internet of Things (IoT) devices [1–3]
A high-cost and high-performance primary mobile edge computing (MEC) server (PMS) with relative strong computing power is deployed in the base station (BS), and multiple low-cost secondary MEC servers (SMSs) with relative weak computing powers are deployed within the coverage area of the BS
Simulation results are provided to evaluate the performance of the proposed deep reinforcement learning (DRL)-based algorithm
Summary
Smart city is a promising city paradigm, which improves the quality of experience (QoE) of citizens through advanced information and communication technologies (ICTs) infrastructure and enormous Internet of Things (IoT) devices [1–3]. A practical problem is that the IoT devices are usually low cost with limited computing powers and storage capacities. It is hard to complete compute-intensive and latency-sensitive tasks independently by the IoT devices. An intuitive method to alleviate this problem is to adopt the cloud computing technology for remote task computation. Most of the clouding computing servers are deployed far away from the IoT devices, in which offloading the tasks of the IoT devices will cause severe transmission delay. Traditional cloud computing technology is difficult to satisfy the latency requirements of applications in smart cities. To solve the issue mentioned above, researchers have proposed the concept of mobile edge computing (MEC)
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