A novel task offloading algorithm based on an integrated trust mechanism in mobile edge computing

Abstract

As a new computing model, mobile edge computing (MEC) is designed to better deal with various forms of service requests, such as computing intensity and delay sensitivity, in the era of big data and the Internet of Things (IoT). However, the development of MEC is still in its infancy, and many issues need to be further investigated. One of the key issues that needs to be addressed in MEC is rational task offloading. Due to the dynamic, real time and complex nature of the MEC environment, the security and reliability of edge data are becoming increasingly important. Based on the above problems, we construct a task offloading integrated trust evaluation mechanism and, combined with the double deep Q-network (DDQN) algorithm in deep reinforcement learning (DRL), propose a novel task offloading algorithm, named DDTMOA. Simulation results show that the DDTMOA algorithm can effectively reduce the average task response time and total system energy consumption while ensuring task offloading performance compared to other classical algorithms.