Multi-Attribute Monitoring for Anomaly Detection: a Reinforcement Learning Approach based on Unsupervised Reward

International audienceThis paper proposes a new method to solve the monitoring and anomaly detection problems of Low-power Internet of Things (IoT) devices. However, their performances are constrained by limited processing, memory, and communication, usually using battery-powered energy. Polling driven mechanisms for monitoring the security, performance, and quality of service of these networks should be efficient and with low overhead, which makes it particularly challenging. The present work proposes the design of a novel method based on a Deep Reinforcement Learning (DRL) algorithm coupled with an Unsupervised Learning reward technique to build a pooling monitoring of IoT networks. This combination makes the network more secure and optimizes predictions of the DRL agent in adaptive environments

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

International audienceNowadays, 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

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

International audienceLow-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%

Using Information Centric Networking in Internet of Things: A Survey

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Towards a New Internetworking Architecture: A New Deployment Approach for Information Centric Networks

International audienceNew research efforts are trying to evolve the current Inter-net. With satisfying communication hardware, the intent is to switch to data oriented networks. In this new vision, data will be the heart of the architecture and protocols have to be changed to dial with this concept. Promising ideas are proposed up in order to develop clean slate design solutions. However, these propositions encounter many deployment problems. In this paper, we propose new approach based on Bloom Filter to cope with storage space problem in data oriented architecture DONA

Memory management optimization for content routers in DONA

International audienceNowadays, content retrieval is marking the Internet usage. User communications are no longer tied up to host interconnection. Information Centric Networking (ICN) models are proposed to cope with these changes. The new paradigm redesigns the Internet architecture to bring out content to the first level. Over the last decade, many key projects have proposed a large solution spectrum to rebuilt networking primitives focused on the content. One important and direct challenge of this shift is the large amount of routing states due to identifying contents rather than hosts.In this paper, we focus especially on DONA, one of the first ICN architecture, and analyse the required memory space to store routing states. Our study shows that today’s technologiesare not able to satisfy the content routing needs. Thus, we propose an enhancement of DONA called BADONA to deal with this problem. It uses a Bloom filter to drastically reduce theusage of the memory space

Hybrid approach for Experimental Networking Research

Abstract. Simulation is often used for the evaluation of new network protocols and architectures. In order to perform more realistic simulations, modern simulators such as ns-3 integrate more detailed models and even support direct execution of real protocol code. However, such complex models require more computational and memory requirements. In this paper, we study the feasibility of a hybrid approach based on distributing a complex simulation scenario on several nodes in a grid network. We show that by exploiting the real time operation of the ns-3 simulator, it is possible to map such complex scenarios on grid nodes. We run experiments to define the operational zone in which the obtained results are accurate. We also propose a basic mapping algorithm to distribute a simulation scenario in several nodes.