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Communication Dans Un Congrès Année : 2020

Scalable and cost efficient maximum concurrent flow over IoT using reinforcement learning

Résumé

The Internet of Things (IoT) is a network of billion of objects. Data streaming over IoT network is a tedious task that requires intelligent flow management and steering. In this paper, we propose a Distributed Maximum Concurrent Flow (DMCF) algorithm to solve the problem of distributing massive IoT video/data to large consumers over IP/data-centric networks. We propose two approaches based on graph theories, and using reinforcement learning techniques. The proposed approaches are implemented and evaluated over different complex graphs. Results show that in large graphs, reinforcement learning methods outperform classical graph theoretic ones.
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Dates et versions

hal-03119533 , version 1 (24-01-2021)

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Abou-Bakr Djaker, Bouabdellah Kechar, Hatem Ibn-Khedher, Hassine Moungla, Hossam Afifi. Scalable and cost efficient maximum concurrent flow over IoT using reinforcement learning. IWCMC 2020: 16th International Wireless Communications and Mobile Computing conference, Jun 2020, Limassol (online), Cyprus. pp.539-544, ⟨10.1109/IWCMC48107.2020.9148257⟩. ⟨hal-03119533⟩
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