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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Yuan, Tingting [1] ; Neto, Wilson da Rocha [2] ; Rothenberg, Christian Esteve [2] ; Obraczka, Katia [3] ; Barakat, Chadi [1] ; Turletti, Thierry [1]
Total Authors: 6
Affiliation:
[1] Inria, Diana Project Team, F-06902 Sophia Antipolis - France
[2] Univ Estadual Campinas, Fac Elect & Comp Engn, BR-13086902 Campinas - Brazil
[3] Univ Calif Santa Cruz, Dept Comp Engn, Santa Cruz, CA 95064 - USA
Total Affiliations: 3
Document type: Journal article
Source: IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT; v. 18, n. 1, p. 585-596, MAR 2021.
Web of Science Citations: 5
Abstract

In this article, 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. (AU)

FAPESP's process: 17/50361-0 - Distributed inteligent vehicular environment: enabling ITS through programmable networks
Grantee:Christian Rodolfo Esteve Rothenberg
Support type: Regular Research Grants