A survey about prediction-based data reduction in wireless sensor networks

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• dc.contributor.author Martins Dias, Gabriel
• dc.contributor.author Bellalta, Boris
• dc.contributor.author Oechsner, Simon
• dc.date.accessioned 2020-04-09T08:24:55Z
• dc.date.available 2020-04-09T08:24:55Z
• dc.date.issued 2016
• dc.description.abstract One of the main characteristics of Wireless Sensor Networks (WSNs) is the constrained energy resources of their wireless sensor nodes. Although this issue has been addressed in several works and received much attention over the years, the most recent advances pointed out that the energy harvesting and wireless charging techniques may offer means to overcome such a limitation. Consequently, an issue that had been put in second place now emerges: the low availability of spectrum resources. Because of it, the incorporation of the WSNs into the Internet of Things and the exponential growth of the latter may be hindered if no control over the data generation is taken. Alternatively, part of the sensed data can be predicted without triggering transmissions that could congest the wireless medium. In this work, we analyze and categorize existing prediction-based data reduction mechanisms that have been designed for WSNs. Our main contribution is a systematic procedure for selecting a scheme to make predictions in WSNs, based on WSNs’ constraints, characteristics of prediction methods, and monitored data. Finally, we conclude the article with a discussion about future challenges and open research directions in the use of prediction methods to support the WSNs’ growth.en
• dc.description.sponsorship This work has been partially supported by the Spanish Government through the project TEC2012-32354 (Plan Nacional I+D), by the Catalan Government through the project SGR-2014-1173 and by the European Union through the project FP7-SME2013-605073-ENTOMATIC. We wish to thank the reviewers for their insightful comments and suggestions that allowed us to improve this paper.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Martins Dias G, Bellalta B, Oechsner S. A survey about prediction-based data reduction in wireless sensor networks. ACM Comput Surv. 2016 Nov;49(3):58. DOI: 10.1145/2996356
• dc.identifier.doi http://dx.doi.org/10.1145/2996356
• dc.identifier.issn 0360-0300
• dc.identifier.uri http://hdl.handle.net/10230/44195
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof ACM Computing surveys. 2016 Nov;49(3):58
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TEC2012-32354
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/605073
• dc.rights © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM computing surveys, 2016 Nov;49(3):58 http://doi.acm.org/10.1145/2996356
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Predictionsen
• dc.subject.keyword Wireless sensor networksen
• dc.subject.keyword Data scienceen
• dc.subject.keyword Data reductionen
• dc.subject.keyword Machine learningen
• dc.title A survey about prediction-based data reduction in wireless sensor networksen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion