Adapting sampling interval of sensor networks using on-line reinforcement learning

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• dc.contributor.author Martins Dias, Gabrielca
• dc.contributor.author Nurchis, Maddalenaca
• dc.contributor.author Bellalta, Borisca
• dc.date.accessioned 2018-02-05T08:36:11Z
• dc.date.available 2018-02-05T08:36:11Z
• dc.date.issued 2016
• dc.description Comunicació presentada al 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), celebrat els deis 12 a 14 de desembre de 2016 a Reston, Virginia.
• dc.description.abstract Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73% the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.en
• dc.description.sponsorship This work has been partially supported by the Catalan Government through the project SGR-2014-1173, the European Union through the project FP7-SME-2013-605073-ENTOMATIC, and by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdf
• dc.identifier.citation Martins Dias G, Nurchis M, Bellalta B. Adapting sampling interval of sensor networks using on-line reinforcement learning. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT); 2016 Dec 12-14; Reston, VA. Piscataway (NJ): IEEE; 2016. p. 460-5. DOI: 10.1109/WF-IoT.2016.7845391
• dc.identifier.doi http://dx.doi.org/10.1109/WF-IoT.2016.7845391
• dc.identifier.uri http://hdl.handle.net/10230/33799
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
• dc.relation.ispartof 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT); 2016 Dec 12-14; Reston, VA. Piscataway (NJ): IEEE; 2016. p. 460-5.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/605073
• dc.rights © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://ieeexplore.ieee.org/document/7845391/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Performance optimizationen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Wireless sensor networksen
• dc.subject.keyword Autonomic computingen
• dc.title Adapting sampling interval of sensor networks using on-line reinforcement learningca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion