Reinforcement Learning Based Reliable Route Selection for Internet of Vehicles
Jung-Jae Kim1, Minwoo Ryu2, Si-Ho Cha3
1Jung-Jae Kim, Engineering Solution Office Control and Instrumentation Research Group, POSCO, South Korea, East Asian.
2Minwoo Ryu, Service Laboratory Institute of Convergence Technology, KT R&D Center, South Korea, East Asian.
3Si-Ho Cha, Department of Multimedia Science, Chungwoon University, Incheon, South Korea, East Asian.
Manuscript received on 08 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 22 June 2019 | PP: 148-152 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H10280688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Self-driving cars have been receiving much attention recently, and communication problems between vehicles have become an issue. Due to frequent topology changes, routing problems occur in communication between vehicles. This is an important issue in the VANET (Vehicular Ad-hoc Network) and several papers have been presented to address this issue. However, existing papers are routing protocols that can resolve issues after they occur or only under certain circumstances, such as urban. Therefore, it is necessary to select the optimal relay nodes according to the circumstances surrounding the agent to ensure optimal performance at all times. For this purpose, this paper proposes RLSR (Reinforcement Learning based Selective Route Selection) algorithm that selects relay nodes through reinforcement learning. The algorithm proposed in this paper can ensure reliability by selecting the best relay nodes in any situation.
Keywords: Reinforcement Learning, VANET, Selective Routing, Adaptive Routing, Route Selection Routing
Scope of the Article: Agent-Based Learning and Knowledge Discovery