Digital Twin Assisted Task Offloading for Workload Management at Fog Nodes

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

1-1-2025

Abstract

The convergence of urban informatics and vehicle intelligence has given rise to smart connected vehicles, which have immense potential as edge computing platforms for various applications. However, harnessing the full efficiency of these platforms presents challenges due to the diverse resource requirements, capabilities, and vehicle types, as well as unpredictable vehicle movements. To address these obstacles, a novel task offloading framework based on Digital Twin (DT) technology has been proposed for the Internet of Vehicles (IoV). This DT-based framework capitalizes on historical data and workload predictions to optimize the utilization of edge devices. It streamlines the offloading process by enabling tasks to be accepted and processed by the source vehicle without relying on external devices. The proposed system is designed to learn and forecast vehicle mobility patterns and computation waiting times, facilitating efficient allocation of computing resources at edge locations. Consequently, this approach enhances the quality of service by ensuring swift and effective task processing, irrespective of the vehicles' unpredictable movements. The proposed approach is compared with a deep sequential model based on reinforcement learning, collaborative multiaccess edge computing (MEC), and energy-efficient MEC via reinforcement learning model. Our method demonstrates an improvement in task execution and overall offloading performance compared to these techniques during peak vehicle arrival rates. Likewise, substantial enhancements are observed in other benchmark parameters.

ISSN

2327-4662

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Disciplines

Computer Sciences

Keywords

connected vehicles, digital twin, edge computing, task offloading

Scopus ID

05000039012

Indexed in Scopus

yes

Open Access

no

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