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RHRA-DRL: RSU-Assisted Hybrid Road-Aware Routing Using Distributed Reinforcement Learning in Internet of Vehicles
In this paper, we propose a novel RSU-assisted hybrid road-aware routing algorithm, RHRA-DRL, designed for urban vehicular networks to optimize real-time data delivery considering dynamic road conditions. The algorithm minimizes broadcast overhead and efficiently determines optimal routing paths by...
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Published in: | IEEE access 2024-01, Vol.12, p.1-1 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | In this paper, we propose a novel RSU-assisted hybrid road-aware routing algorithm, RHRA-DRL, designed for urban vehicular networks to optimize real-time data delivery considering dynamic road conditions. The algorithm minimizes broadcast overhead and efficiently determines optimal routing paths by incorporating two key components. Firstly, a multihop road-segment reward-based ad-hoc (RRAH) routing algorithm is introduced to adaptively respond to changing vehicle topologies within road segments. Rewards are calculated based on performance metrics, and the segment reward integrates into RSU-to-RSU (R2R) routing. Secondly, a distributed Q-learning-based road-aware (DQRA) routing algorithm determines RSUs traversed during data transmission using a decentralized agent reinforcement learning approach. The combination of these algorithms in RHRA-DRL ensures effective and consistent path establishment with a unified reward system. Simulation results demonstrate the superiority of RHRA-DRL over AODV in Internet of Vehicles (IoV) networks, showcasing enhanced communication, prolonged link lifetime, rapid establishment and repair of routing paths, and reduced overhead. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2024.3366280 |