Dual-Driven Resource Management for Sustainable Computing in the Blockchain-Supported Digital Twin IoT

Abstract

Nowadays, emerging sixth-generation (6G) mobile networks, the Internet of Things (IoT), and mobile-edge computing (MEC) technologies have played significant roles in developing a sustainable computing network. In sustainable computing networks, with the increasing scale of data-driven applications, massive privacy-sensitive data are generated. How to effectively process such data on resource-limited IoT devices is challenging. Although edge intelligence (EI) is designed to maintain an appropriate level of ultradelay reliability, low-latency communication (URLLC), real-time data processing, and security and privacy are concerning. In this article, we propose a novel blockchain-supported hierarchical digital twin IoT (HDTIoT) framework, which combines the digital twin to edge network and adopts blockchain technology to achieve secure and reliable real-time computation. We first propose a data and knowledge dual-driven learning solution to ensure real-time interaction and efficient optimization between the physical and the digital worlds. To improve communication and computation efficiency with data and knowledge dual-driven learning, the optimization goal is to minimize the system delay and energy consumption and ensure system reliability and the learning accuracy of IoT devices. Moreover, we propose a proximal policy optimization (PPO)-based multiagent reinforcement learning (MARL) algorithm to solve the resource allocation (RA) problem. Experimental results show that the proposed RA scheme can improve the efficiency of the HDTIoT system, guarantee learning accuracy, reliability, and security, and make a balance between system delay and energy consumption.