HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Connectivity in remote areas remains an unsolved problem, especially in developing countries. An adequate communications infrastructure can provide important services (e.g., telemedicine, virtual education, etc.) to communities of these regions. However, the management of these networks is complex because, on the one hand, they experience highly variable environmental conditions that require the continuous intervention of a human operator and, on the other hand, most of them are deployed in inaccessible areas. Self-management is proposed as an alternative solution to the management problem for these networks, thus reducing human intervention and, conversely, operational costs. This paper shows a network self-management architecture based on the SDN paradigm and Deep Reinforcement Learning algorithms that can learn the network dynamics and make autonomous decisions to optimize the network performance and adapt to the changing conditions of the environment to meet the QoS demands of the different network services. The proposed architecture has been successfully implemented in a simulated environment and was tested using a case study of QoS-aware routing optimization in a rural scenario.
IoT systems grow quickly and are massively present in urban areas. Their successful deployment requires autonomy that can be built on automated learning technologies such as Deep Learning. The IoT applications require important computational resources, rarely available on devices. Autonomous IoT systems require the computation power available on the edge and cloud servers in order to offload some tasks related to the supported applications and the underlying platforms. Task offloading constitutes a big challenge in autonomous IoT systems due to the huge number of IoT devices for scenarios of the family of smart cities. Managing task offloading in such contexts requires adaptive strategies capable of taking into consideration the rapid evolution of available resources and proposing efficient offloading solutions to all received requests. In this paper we use a Deep Reinforcement Learning (DRL) approach capable of handling large state spaces, and resolve the optimization problem in this context, where other techniques can not scale efficiently. Our solution is based on a DRL agent that was developed in the ns3-gym framework and was tested on IoT system scenario implemented in the NS3 simulator. The results obtained show that the DRL agent can adapt quickly to resource evolution in the IoT system and can handle big number of demands fulfilling scalabilty requirements of autonomous IoT systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.