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<p>The rapid expansion of the Internet of Vehicles (IoV) has exacerbated challenges in computation offloading. In such highly dynamic environments, the simultaneous optimization of latency and energy consumption is critical. Existing approaches, such as standard Deep Q-Networks (DQN), often struggle with slow convergence and difficulties in achieving an optimal trade-off between metrics when confronted with large state spaces and temporal dependencies induced by high vehicular mobility. This paper proposes LA-DRQN, a novel hierarchical decision-making framework that synergistically integrates Learning Automata (LA) at the strategic level for macro-policy selection with a Deep Recurrent Q-Network (DRQN) at the tactical level for fine-tuning computation offloading ratios. Key contributions include: (1) replacing stochastic mobility models with Cellular Automata (CA)-based simulations to enhance environmental realism; (2) designing a dynamic Roadside Unit (RSU) connectivity mechanism incorporating distance-dependent latency and bandwidth modeling; and (3) developing an instantaneous multi-objective reward function to expedite convergence. Extensive experiments in iFogSim environment over approximately 7,200 episodes demonstrate the superiority of LA-DRQN over standard baselines and state-of-the-art algorithms. Experimental results reveal that the proposed method achieves a 24.7% reduction in average processing latency and a 35.3% reduction in energy consumption compared to baseline methods. Furthermore, the strategic LA layer demonstrates context-aware adaptability by effectively steering the system toward optimal macro-policies. Statistical analysis validates the scalability and efficiency of the proposed approach for delay-sensitive applications within Intelligent Transportation Systems (ITS).</p>

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Optimizing Computation Offloading in IoV Environments: A Hybrid Learning Automata and Deep Recurrent Q-Network Approach

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Sahar Mohammadpourian Navidi,
  • Mohammad Hossein Rezvani,
  • Esmaeil Zeinali Khosraghi

摘要

The rapid expansion of the Internet of Vehicles (IoV) has exacerbated challenges in computation offloading. In such highly dynamic environments, the simultaneous optimization of latency and energy consumption is critical. Existing approaches, such as standard Deep Q-Networks (DQN), often struggle with slow convergence and difficulties in achieving an optimal trade-off between metrics when confronted with large state spaces and temporal dependencies induced by high vehicular mobility. This paper proposes LA-DRQN, a novel hierarchical decision-making framework that synergistically integrates Learning Automata (LA) at the strategic level for macro-policy selection with a Deep Recurrent Q-Network (DRQN) at the tactical level for fine-tuning computation offloading ratios. Key contributions include: (1) replacing stochastic mobility models with Cellular Automata (CA)-based simulations to enhance environmental realism; (2) designing a dynamic Roadside Unit (RSU) connectivity mechanism incorporating distance-dependent latency and bandwidth modeling; and (3) developing an instantaneous multi-objective reward function to expedite convergence. Extensive experiments in iFogSim environment over approximately 7,200 episodes demonstrate the superiority of LA-DRQN over standard baselines and state-of-the-art algorithms. Experimental results reveal that the proposed method achieves a 24.7% reduction in average processing latency and a 35.3% reduction in energy consumption compared to baseline methods. Furthermore, the strategic LA layer demonstrates context-aware adaptability by effectively steering the system toward optimal macro-policies. Statistical analysis validates the scalability and efficiency of the proposed approach for delay-sensitive applications within Intelligent Transportation Systems (ITS).