Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning

Abou Bakr Djaker; Bouabdellah Kechar; Hatem Ibn-Khedher; Hassine Moungla; Hossam Afifi

doi:10.1109/IWCMC48107.2020.9148257

Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning

Abou Bakr Djaker
, Bouabdellah Kechar
, Hatem Ibn-Khedher
, Hassine Moungla
, Hossam Afifi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2020 International Wireless Communications and Mobile Computing, IWCMC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	539-544
Number of pages	6
ISBN (Electronic)	9781728131290
DOIs	https://doi.org/10.1109/IWCMC48107.2020.9148257
Publication status	Published - 1 Jun 2020
Event	16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020 - Limassol, Cyprus Duration: 15 Jun 2020 → 19 Jun 2020

Publication series

Name	2020 International Wireless Communications and Mobile Computing, IWCMC 2020

Conference

Conference	16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Country/Territory	Cyprus
City	Limassol
Period	15/06/20 → 19/06/20

Keywords

Internet of Things (IoT)
Maximum Concurrent Flow Problem (MCFP)
Optimization
Q Learning
Reinforcement Learning

Access to Document

10.1109/IWCMC48107.2020.9148257

Cite this

Djaker, A. B., Kechar, B., Ibn-Khedher, H., Moungla, H., & Afifi, H. (2020). Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning. In 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 (pp. 539-544). Article 9148257 (2020 International Wireless Communications and Mobile Computing, IWCMC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWCMC48107.2020.9148257

Djaker, Abou Bakr ; Kechar, Bouabdellah ; Ibn-Khedher, Hatem et al. / Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning. 2020 International Wireless Communications and Mobile Computing, IWCMC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 539-544 (2020 International Wireless Communications and Mobile Computing, IWCMC 2020).

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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",

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language = "English",

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Djaker, AB, Kechar, B, Ibn-Khedher, H, Moungla, H & Afifi, H 2020, Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning. in 2020 International Wireless Communications and Mobile Computing, IWCMC 2020., 9148257, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 539-544, 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15/06/20. https://doi.org/10.1109/IWCMC48107.2020.9148257

Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning. / Djaker, Abou Bakr; Kechar, Bouabdellah; Ibn-Khedher, Hatem et al.
2020 International Wireless Communications and Mobile Computing, IWCMC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 539-544 9148257 (2020 International Wireless Communications and Mobile Computing, IWCMC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Afifi, Hossam

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N2 - 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.

AB - 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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KW - Optimization

KW - Q Learning

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Djaker AB, Kechar B, Ibn-Khedher H, Moungla H, Afifi H. Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning. In 2020 International Wireless Communications and Mobile Computing, IWCMC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 539-544. 9148257. (2020 International Wireless Communications and Mobile Computing, IWCMC 2020). doi: 10.1109/IWCMC48107.2020.9148257

Scalable and Cost Efficient Maximum Concurrent Flow over IoT using Reinforcement Learning

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