Digital Twin Freshness Maximization in Edge Computing

Authors:
Publisher website
Google Scholar
Abstract
Mobile Edge Computing (MEC) shifts powerful computing resource provisioning from remote powerful data centers to the edge of core networks. Meanwhile, Digital Twin (DT) has surfaced as a promising technology to provide comprehensive and dynamic descriptions of physical objects in cyberspace with bidirectional and real-time interactions. Moreover, Internet of Things (IoT) devices have contributed abundant, heterogeneous and continuous data from interconnected devices to the explosion of DTs. With technologies evolution, there is an increasing necessity to address the freshness of both DT states and DT data, through timely synchronizations between DTs and their objects in a highly dynamic IoT environment. In this paper, we develop innovative methodologies to improve the DT freshness while minimizing the cost of various resources consumed on the DT freshness improvement. Specifically, we first formulate two novel optimization problems of DT freshness: (i) The static DT freshness optimization problem, where all DT synchronization tasks are given in advance; and (ii) the dynamic DT freshness optimization problem without any knowledge of future DT synchronization tasks over a given finite time. We then devise an approximation algorithm with a provable approximation ratio for the static DT freshness optimization problem. Also, we develop an online algorithm with a provable competitive ratio for the dynamic DT freshness optimization problem. Finally, we evaluate the performance of the proposed algorithms through simulations. Simulation results show that the proposed algorithms outperform their comparison baselines by no less than 13.2%.
Funding Information
  • National Science Foundation (EPCN-2319995)
  • National Science Foundation (PFI-2431990)
  • National Science Foundation (AI-ENGAGE-2520346)

This publication has 49 references indexed in Scilit: