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4 results on '"Mohamed Said Frikha"'

3. Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

Author

Laurent Andrey, Abdelkader Lahmadi, Mohamed Said Frikha, Sonia Mettali Gammar, Centre de Recherche Réseau Image SysTème Architecture et MuLtimédia (CRISTAL), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems (RESIST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer Networks and Communications, Computer science, Internet of Things, Wearable computer, 02 engineering and technology, computer.software_genre, Scheduling (computing), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Complete information, Reinforcement learning, 0202 electrical engineering, electronic engineering, information engineering, Wireless, Deep reinforcement learning, Wireless Networks, Multimedia, business.industry, 020206 networking & telecommunications, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Drone, Identification (information), Resource allocation, 020201 artificial intelligence & image processing, business, computer
Abstract

International audience; Nowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal.

Published
2021

4. Leveraging Reinforcement Learning for Adaptive Monitoring of Low-Power IoT Networks

Author

Abdelkader Lahmadi, Mohamed Said Frikha, Sonia Mettali Gammar, Laurent Andrey, École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA), Centre de Recherche Réseau Image SysTème Architecture et MuLtimédia (CRISTAL), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), Resilience and Elasticity for Security and ScalabiliTy of dynamic networked systems (RESIST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
050101 languages & linguistics, business.industry, Computer science, Network packet, 05 social sciences, Real-time computing, Adaptive monitoring, 02 engineering and technology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Power (physics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Polling, Internet of Things, business
Abstract

International audience; Low-power Internet of Things (IoT) networks are widely deployed in various environments with resource constrained devices, making their states monitoring particularly challenging. In this paper, we propose an adaptive monitoring mechanism for low-power IoT devices, by using a reinforcement learning (RL) method to automatically adapt the polling frequencies of the collected attributes. Our goal is to minimize the number of monitoring packets while keeping accurate and timely detection of threshold crossings associated to supervised attributes. We study the various RL parameter settings under different monitoring attribute behaviors using OpenAi Gym simulator. We implement the RL based adaptive polling in Contiki OS and we evaluate its performance using Cooja simulator. Our results show that our approach converges to optimal polling frequencies and outperforms static periodic notification-based methods by reducing the number of monitoring packets, with a percentage of correctly detected threshold crossings exceeding 80%.

Published
2020

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