A TRUSTED COMPUTING RESOURCES OPTIMAL SCHEDULING ALGORITHM IN INDUSTRIAL INTERNET AND HEALTHCARE INTEGRATING DRL, BLOCKCHAIN AND END-EDGE-CLOUD

Abstract

With the rapid development of the Internet of Things (IoT) and Internet technology, the product of the combination of the two, the Industrial Internet, has also received extensive attention and there are more and more research achievements related to the Industrial Internet. In the industrial Internet system, the communication network system composed of sensors, communication nodes, controllers and other intelligent devices can realize efficient and convenient data interaction between people and machines, providing an important infrastructure and technical support for industrial big data analysis and intelligent production. However, in the current industrial Internet system, industrial equipment users generally have the problem of low computing energy efficiency, and the collected industrial data has a high-security risk in the transmission, processing and other processes. At the same time, the size and scale of the industrial Internet equipment group is huge, and the lack of rational resource allocation leads to excessive waste of computing resources in the system, which is also a prominent problem of the current industrial Internet system. In response to the above questions, this paper, on the basis of reading a large number of documents, integrates the improved DRL algorithm, End-Edge-Cloud architecture and blockchain to form a new industrial Internet architecture. The architecture realizes computing offload through the three-tier structure of terminal layer, edge layer and cloud layer, and guarantees the security of industrial data through the decentralized feature of blockchain, ultimately achieving the goal of reducing energy consumption, computing overhead and trusted computing. In the architecture proposed in this paper, the dynamic unloading of industrial data and computing tasks is achieved through a three-tier architecture. The MDP is used to build an optimization problem model, and the improved DRL algorithm is used to iteratively solve the optimal computing resource scheduling strategy. The main research contents of this paper include (1) Using MDP to model optimization problems; (2) Propose an industrial Internet system architecture that integrates and improves DRL, “end edge cloud” and blockchain; (3) The MDP problem is solved iteratively based on deep reinforcement learning. The simulation results show that the proposed architecture has more advantages than the existing six architectures in terms of computing cost, equipment energy consumption and total working time.