Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedy

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• dc.contributor.author Barrachina Muñoz, Sergioca
• dc.contributor.author Bellalta, Borisca
• dc.date.accessioned 2018-02-26T11:17:56Z
• dc.date.available 2018-02-26T11:17:56Z
• dc.date.issued 2017
• dc.description Comunicació presentada al 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), celebrat els dies 8 a 13 d'octubre de 2017 a Montreal, Canadà.
• dc.description.abstract Despite being a relatively new communication technology, Low-Power Wide Area Networks (LPWANs) have shown their suitability to empower a major part of Internet of Things applications. Nonetheless, most LPWAN solutions are built on star topology (or single-hop) networks, often causing lifetime shortening in stations located far from the gateway. In this respect, recent studies show that multi-hop routing for uplink communications can reduce LPWANs' energy consumption significantly. However, it is a troublesome task to identify such energetically optimal routing through trial-and-error brute-force approaches because of time and, especially, energy consumption constraints. In this work we show the benefits of facing this exploration/exploitation problem by running centralized variations of the multi-arm bandit's e-greedy, a well-known online decision-making method that combines best known action selection and knowledge expansion. Important energy savings are achieved when proper randomness parameters are set, which are often improved when conveniently applying similarity, a concept introduced in this work that allows harnessing the gathered knowledge by sporadically selecting unexplored routing combinations akin to the best known one.en
• dc.description.sponsorship This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), and by the ENTOMATIC FP7-SME-2013 EC project (605073).
• dc.format.mimetype application/pdf
• dc.identifier.citation Barrachina-Muñoz S, Bellalta B. Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedy. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017 Oct 8-13; Montreal, Canada. Piscataway (NJ): IEEE; 2017. [5 p.]. DOI: 10.1109/PIMRC.2017.8292373
• dc.identifier.doi http://dx.doi.org/10.1109/PIMRC.2017.8292373
• dc.identifier.uri http://hdl.handle.net/10230/34000
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)ca
• dc.relation.ispartof 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017 Oct 8-13; Montreal, Canada. Piscataway (NJ): IEEE; 2017. [5 p.].
• dc.relation.isreferencedby http://github.com/sergiobarra/DRESG_lpwan
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/605073
• dc.rights © 2017 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/8292373/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Routingen
• dc.subject.keyword Topologyen
• dc.subject.keyword Energy consumptionen
• dc.subject.keyword Network topologyen
• dc.subject.keyword Uplinken
• dc.subject.keyword Payloadsen
• dc.subject.keyword Wide area networksen
• dc.title Learning optimal routing for the uplink in LPWANs using similarity-enhanced e-greedyca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion