Learning to Survive: Achieving Energy Neutrality in Wireless Sensor Networks Using Reinforcement Learning

Abstract : Energy harvesting is a promising approach to enable autonomous long-life wireless sensor networks. As typical energy sources present time-varying behavior, each node embeds an energy manager, which dynamically adapts the power consumption of the node to maximize the quality of service, while preventing power failure. In this work, RLMan, a novel energy management scheme based on reinforcement learning theory, is proposed. RLMan dynamically adapts its policy to time-varying environment by continuously exploring, while exploiting the current knowledge to improve the quality of service. The proposed energy management scheme has a very low memory footprint, and requires very few computational power, which makes it suitable for online execution on sensor nodes. Moreover, it only necessitates the state of charge of the energy storage device as an input, and therefore is practical to implement. RLMan was compared to three state-of-the-art energy management schemes, using simulations and energy traces from real measurements. Results show that using RLMan can enable almost 70 % gains regarding the average throughput.
Type de document :
Communication dans un congrès
IEEE International Conference on Communications (ICC), May 2017, Paris, France. IEEE International Conference on Communications (ICC)
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01530098
Contributeur : Matthieu Gautier <>
Soumis le : mercredi 31 mai 2017 - 16:17:52
Dernière modification le : vendredi 9 février 2018 - 15:38:48
Document(s) archivé(s) le : mercredi 6 septembre 2017 - 17:01:23

Fichier

aitaoudia2017ieeeicc.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01530098, version 1

Collections

Citation

Fayçal Ait Aoudia, Matthieu Gautier, Olivier Berder. Learning to Survive: Achieving Energy Neutrality in Wireless Sensor Networks Using Reinforcement Learning. IEEE International Conference on Communications (ICC), May 2017, Paris, France. IEEE International Conference on Communications (ICC). 〈hal-01530098〉

Partager

Métriques

Consultations de la notice

207

Téléchargements de fichiers

83