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Journal Articles IEEE Transactions on Network and Service Management Year : 2021

Dynamic Controller Assignment in Software Defined Internet of Vehicles through Multi-Agent Deep Reinforcement Learning

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Abstract

In this paper, we introduce a novel dynamic controller assignment algorithm targeting connected vehicle services and applications, also known as Internet of Vehicles (IoV). The proposed approach considers a hierarchically distributed control plane, decoupled from the data plane, and uses vehicle location and control traffic load to perform controller assignment dynamically. We model the dynamic controller assignment problem as a multi-agent Markov game and solve it with cooperative multi-agent deep reinforcement learning. Simulation results using real-world vehicle mobility traces show that the proposed approach outperforms existing ones by reducing control delay as well as packet loss. Index Terms-Internet of Vehicles (IoV), Software Defined Networking (SDN), multi-agent deep reinforcement learning, controller assignment.
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Dates and versions

hal-03000911 , version 1 (12-11-2020)
hal-03000911 , version 2 (17-12-2020)

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Cite

Tingting Yuan, Wilson Da Rocha Neto, Christian Esteve Rothenberg, Katia Obraczka, Chadi Barakat, et al.. Dynamic Controller Assignment in Software Defined Internet of Vehicles through Multi-Agent Deep Reinforcement Learning. IEEE Transactions on Network and Service Management, 2021, 18 (1), pp.12. ⟨10.1109/TNSM.2020.3047765⟩. ⟨hal-03000911v2⟩
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