Trusted Task Offloading and Resource Allocation Strategy in MEC Environment

In recent years, with the rapid development of Wise Information Technology of med, Intelligent Transportation, Industrial Internet, and other emerging business scenarios, people’s production and lifestyle have changed but also put forward higher requirements for computing resources. Therefore, based on deep reinforcement learning (DRL), an efficient task unloading and resource allocation strategy under a moving edge computing (MEC) environment is proposed. This method adaptively learns and adjusts the allocation strategy through real-time sensing of the environment to improve system performance. In addition, the dynamic, real time, and complex nature of the MEC environment challenges whether tasks can be successfully processed. Therefore, in order to ensure the credibility of the system, a trust evaluation mechanism is considered to improve the quality of service (QoS).