Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things

TL;DR

This paper proposes a deep reinforcement learning approach for optimizing task offloading, bandwidth allocation, and CAP selection in mobile edge computing networks to improve latency and energy efficiency for IoT applications.

Contribution

It introduces a novel deep reinforcement learning framework using Deep Q-Networks for intelligent offloading and resource management in MEC-based IoT systems.

Findings

Significant reduction in latency and energy consumption achieved.

Effective offloading decisions learned automatically via deep reinforcement learning.

Bandwidth and CAP selection strategies enhance system performance.

Abstract

In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet of things (IoT), where multiple users have some computational tasks assisted by multiple computational access points (CAPs). By offloading some tasks to the CAPs, the system performance can be improved through reducing the latency and energy consumption, which are the two important metrics of interest in the MEC networks. We devise the system by proposing the offloading strategy intelligently through the deep reinforcement learning algorithm. In this algorithm, Deep Q-Network is used to automatically learn the offloading decision in order to optimize the system performance, and a neural network (NN) is trained to predict the offloading action, where the training data is generated from the environmental system. Moreover, we employ the bandwidth allocation in order to optimize the wireless spectrum for the links between the users and CAPs, where several bandwidth allocation schemes are proposed. In further, we use the CAP selection in order to choose one best CAP to assist the computational tasks from the users. Simulation results are finally presented to show the effectiveness of the proposed reinforcement learning offloading strategy. In particular, the system cost of latency and energy consumption can be reduced significantly by the proposed deep reinforcement learning based algorithm.