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Collaborative Multiagent Computation Offloading in Digital Twin-Driven IoV Networks
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DOI：10.1109/tii.2026.3692468.png)
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Abstract
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With the rapid advancement of the industrial Internet of Things and vehicle-to-everything (V2X) technology, the synergistic collaboration between automated guided vehicles (AGVs) and roadside units (RSUs) has become a crucial enabler for improving logistics efficiency and transportation scheduling in industrial settings. However, V2X systems face challenges in task offloading and resource scheduling in dynamic industrial networks with heterogeneous computing resources. To address these challenges, this work proposes a digital twin-driven edge computing architecture, combined with a collaborative multiagent offloading framework. Specifically, this architecture virtualizes the real-time states of AGVs and RSUs, enhancing system awareness and coordination for industrial operations of Internet of Vehicles. Task offloading is formulated as a multiagent Markov decision process, and a collaborative multiagent deep deterministic policy gradient strategy is developed for adaptive decision-making. Extensive simulations demonstrate that the proposed approach significantly outperforms state-of-the-art baselines in terms of task completion time and overall efficiency in dynamic, resource-constrained industrial V2X environments.
Keywords:
Digital twin (DT)
edge computing
industrial Internet of Things (IIoT)
Internet of Vehicles (IoV)
task offloading
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