Secure and reliable computation offloading in blockchain-assisted cyber-physical IoT systems

Abstract

With the development of the Cyber-Physical Internet of Things System (CPIoTS), the number of Cyber-Physical System (CPS) applications accessed in networks has increased dramatically. Latency-sensitive resource orchestration in CPS applications is extraordinarily essential for maintaining the Quality of Experience (QoE) for users. Although edge-cloud computing performs effectively in achieving latency-aware resource allocation in CPIoTS, existing methods fail to jointly consider the security and reliability requirements, thereby increasing the process latency of tasks and degrading the QoE of users. This paper aims to minimize the system latency of edge-cloud computing coupled with CPS while simultaneously considering the security and reliability requirements. We first consider a time-varying channel model as a Finite-State Markov Channel (FSMC) and propose a distributed blockchain-assisted CPIoTS to realize secure consensus and reliable resource orchestration by offloading computation tasks in edge-cloud computing. Moreover, we propose an efficient resource allocation algorithm, PPO-SRRA, that optimizes computing offloading and multi-dimension resource (e.g., communication, computation, and consensus resource) allocation by using a policy-based Deep Reinforcement Learning (DRL) method. The experimental results show that the proposed resource allocation scheme can reduce the system latency and ensure consensus security.