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dc.contributor.authorAstudillo León, Juan Pablo
dc.contributor.authorRico Novella, Francisco José
dc.contributor.authorCruz Llopis, Luis Javier de la
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Telemàtica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica
dc.identifier.citationAstudillo, J.P.; Rico, F.; De la Cruz Llopis, L.J. Predictive traffic control and differentiation on smart grid neighborhood area networks. "IEEE access", 2020, vol. 8, p. 216805-216821.
dc.description.abstractSmart Grid (SG) networks include an associated data network for the transmission and reception of control data related to the electric power supply service. A subset of this data network is the SG Neighborhood Area Network (SG NAN), whose objective is to interconnect the subscribers' homes with the supplier control center. The data flows transmitted through these SG NANs belong to different applications, giving rise to the need for different quality of service requirements. Additionally, other subscriber appliances could use this network to communicate over the Internet. To avoid network congestion, as well as to differentiate the quality of service (QoS) received by the different data flows, a congestion control mechanism with traffic differentiation capabilities is required. The main contribution of this work is the proposal of a new congestion control mechanism based on machine learning techniques to try to guarantee the different QoS requirements to the different data flows. A main problem when applying machine learning techniques is the need for datasets to be used in the training steps. In this sense, a second contribution of this article is the proposal of a method to generate such datasets by means of simulation techniques. The proposed mechanism is then evaluated in the context of a wireless SG NAN. The nodes of this network are the subscriber's smart meters, which in turn perform the function of concentrating the data traffic sent and received by the rest of the home appliances. Besides, different machine learning classification methods are taken into account. The evaluation carried out shows significant improvements in terms of network throughput, transit time, and quality of service differentiation. Finally, the computational cost of the algorithms used in this proposal has also been evaluated, using real low-cost IoT hardware platforms.
dc.format.extent17 p.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Serveis telemàtics i de comunicació multimèdia
dc.subject.lcshSmart power grids
dc.subject.lcshInternet of things
dc.subject.lcshMachine learning
dc.subject.otherSmart grid
dc.subject.otherNeighborhood area networks
dc.subject.otherMachine learning
dc.subject.otherDeep learning
dc.subject.otherCongestion control
dc.titlePredictive traffic control and differentiation on smart grid neighborhood area networks
dc.subject.lemacXarxes intel·ligents (Electricitat)
dc.subject.lemacInternet de les coses
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. SISCOM - Smart Services for Information Systems and Communication Networks
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
local.citation.authorAstudillo, J.P.; Rico, F.; de la Cruz Llopis, Luis J.
local.citation.publicationNameIEEE access

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Attribution 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 3.0 Spain