A multiscale algorithm for joint forecasting-scheduling to solve the massive access problem of IoT

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dc.authoridNAKIP, Mert / 0000-0002-6723-6494
dc.authoridTursel Eliiyi, Deniz / 0000-0001-7693-3980
dc.authorscopusid6602651842
dc.authorscopusid57212473263
dc.authorscopusid14521079300
dc.authorscopusid55937768800
dc.authorwosidNAKIP, Mert/AAM-5698-2020
dc.authorwosidTursel Eliiyi, Deniz/J-9518-2014
dc.contributor.authorRodoplu, Volkan
dc.contributor.authorNakip, Mert
dc.contributor.authorEliiyi, Deniz Türsel
dc.contributor.authorGuzelis, Cuneyt
dc.date.accessioned2022-02-15T16:58:24Z
dc.date.available2022-02-15T16:58:24Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractThe massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article, we describe a multiscale algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT gateway to solve the massive access problem at the medium access control (MAC) layer. Our algorithm operates at multiple time scales that are determined by the delay constraints of IoT applications as well as the minimum traffic generation periods of IoT devices. In contrast with the current approaches to the massive access problem that assume random arrivals for IoT data, our algorithm forecasts the upcoming traffic of IoT devices using a multilayer perceptron architecture and preallocates the uplink wireless channel based on these forecasts. The multiscale nature of our algorithm ensures scalable time and space complexity to support up to 6650 IoT devices in our simulations. We compare the throughput and energy consumption of MSA with those of reservation-based access barring (RAB), priority based on average load (PAL), and enhanced predictive version burst-oriented (E-PRV-BO) protocols, and show that MSA significantly outperforms these beyond 3000 devices. Furthermore, we show that the percentage control overhead of MSA remains less than 1.5%. Our results pave the way to building scalable joint forecasting-scheduling engines to handle a massive number of IoT devices at IoT gateways.en_US
dc.description.sponsorshipProject Support Commission of Yasar University [BAP060]; TUBITAK (Scientific and Technological Research Council of Turkey) under the 1001 ProgramTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [118E277]en_US
dc.description.sponsorshipThe application of JSTW to IoT burst scheduling was funded by the Project Support Commission of Yasar University within the scope of the Scientific Research Project BAP060 Scheduling Algorithms for Wireless Communication. The development of joint-forecasting scheduling, including the Multiscale Algorithm in this article, was funded by TUBITAK (Scientific and Technological Research Council of Turkey) under the 1001 Program Grant 118E277.en_US
dc.identifier.doi10.1109/JIOT.2020.2992391
dc.identifier.endpage8589en_US
dc.identifier.issn2327-4662
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85092169356en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage8572en_US
dc.identifier.urihttps://doi.org/10.1109/JIOT.2020.2992391
dc.identifier.urihttps://hdl.handle.net/20.500.14034/400
dc.identifier.volume7en_US
dc.identifier.wosWOS:000571765000060en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.journalIeee Internet Of Things Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInternet of Thingsen_US
dc.subjectProtocolsen_US
dc.subjectLogic gatesen_US
dc.subjectForecastingen_US
dc.subjectPerformance evaluationen_US
dc.subjectWireless communicationen_US
dc.subjectDelaysen_US
dc.subjectForecastingen_US
dc.subjectmachine learningen_US
dc.subjectmachine-to-machine (M2M) communicationen_US
dc.subjectmassive accessen_US
dc.subjectschedulingen_US
dc.subjectMac Protocolen_US
dc.subjectLow-Latencyen_US
dc.subjectNetworksen_US
dc.subjectMachineen_US
dc.subjectSchemeen_US
dc.titleA multiscale algorithm for joint forecasting-scheduling to solve the massive access problem of IoTen_US
dc.typeArticleen_US

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