@inproceedings{f986f381d3054295a1259621fc8d1d09,
title = "Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning",
abstract = "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.",
keywords = "Internet of Things (IoT), Maximum Concurrent Flow Problem (MCFP), Optimization, Q Learning, Reinforcement Learning",
author = "Djaker, \{Abou Bakr\} and Bouabdellah Kechar and Hatem Ibn-Khedher and Hassine Moungla and Hossam Afifi",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020 ; Conference date: 15-06-2020 Through 19-06-2020",
year = "2020",
month = jun,
day = "1",
doi = "10.1109/IWCMC48107.2020.9148257",
language = "English",
series = "2020 International Wireless Communications and Mobile Computing, IWCMC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "539--544",
booktitle = "2020 International Wireless Communications and Mobile Computing, IWCMC 2020",
}