Energy efficient exponentially weighted algorithm - Based resource allocation in LoRa networks

Sani, Y., Ahmed Ouameur, M., Massicotte, D. et Martin, T. (2021, October 13-15). Energy efficient exponentially weighted algorithm - Based resource allocation in LoRa networks. Dans 2021 IEEE 4th 5G World Forum (5GWF), Montreal, Canada DOI 10.1109/5GWF52925.2021.00045.

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Résumé

Low-power wide-area networks (LPWANs) increasingly attract attention in the IoT community. The provision of long communication ranges with low energy consumption is the main reason behind LPWAN’s growing popularity. Energy efficiency is crucial for LPWAN devices that are mostly battery-powered and required to function in a crowded environment. To reduce energy consumption over these networks, minimizing the collision rate in the packet transmission process is one of the possible solutions. However, existing radio resource allocation management algorithms do not fulfill the energy efficiency required by IoT devices. We propose Energy Efficient Exponentially Weighted Algorithm Based Resource Allocation, which considers each packet’s energy consumption level and transmission time in learning the best set of resources to be allocated to each end-device. We achieve 30% lower energy consumption per packet transmission than the baseline methods, which is noticeable when considering the whole network packet transmission, at the expense of losing 2% of the successful transmission rate.

Type de document:	Document issu d'une conférence ou d'un atelier
Mots-clés libres:	IoT LoRaWAN Resource allocation Energy efficient Reinforcement learning MAB EXP3
Date de dépôt:	09 mai 2022 15:37
Dernière modification:	09 mai 2022 15:38
URI:	https://depot-e.uqtr.ca/id/eprint/10127

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