Computing Offloading for Digital Twinning Empowered Industrial IoT
Weibo Qin, Ying Wang, Haipeng Yao, Jiaqi Xu, Tianle Mai, Yunjie Liu, Zehui Xiong, Fei Richard Yu · IEEE Transactions on Services Computing · 2025
The Digital Twin (DT) represents a rapidly advancing technological innovation within the Industrial Internet of Things (IIoT) domain. DT leverages the power of simulation, machine learning, and data mining to facilitate optimal decision-making for physical objects. However, the creation of a dynamic and living digital counterpart comes at a considerable cost. It requires continuous massive data updating and processing every time the physical object changes. As most data collected by IIoT devices are in their original form, such as images and videos, transmitting such data to remote cloud computing will result in large delays. Furthermore, data processing is often a computationally intensive operation, such as image recognition and video coding, making it impractical to perform processing tasks directly in IIoT devices. To overcome this problem, we introduced the Multi-access/mobile Edge Computing (MEC) architecture to enhance capabilities of DT-enabled IIoT devices. IIoT devices can leverage the extra computing resources in MEC to process raw data, transmitting only the calculation results to update the digital counterpart. To efficiently allocate resources between IIoT devices and MEC, we propose a double auction-based resource allocation scheme. The IIoT devices can purchase computing power from MEC, and an iterative double auction scheme is applied to achieve system efficiency within this market. Furthermore, we propose the Win or Learn Fast Algorithm Policy Hill Climbing (Wolf-PHC) algorithm, which enables agents to improve their strategies continuously through participation in auctions. Simulation results demonstrate that this algorithm accelerates the process of market equilibrium convergence.