Auction-based adaptive resource allocation optimization in dense and heterogeneous IoT networks

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dc.contributor.authorWickramasinghe, Nirmal D.en
dc.contributor.authorDooley, Johnen
dc.contributor.authorPesch, Dirken
dc.contributor.authorDey, Indrakshien
dc.contributor.funderResearch Irelanden
dc.contributor.funderHORIZON EUROPE Marie Sklodowska-Curie Actionsen
dc.date.accessioned2025-11-26T10:11:22Z
dc.date.available2025-11-26T10:11:22Z
dc.date.issued2025-10-22en
dc.description.abstractEfficient and reliable resource allocation within densely-deployed massive IoT networks remains a key challenge due to resource constraints among low size, weight and power (SWaP) IoT devices and within the network and limitations of conventional centralized methods under incomplete information. We propose a novel auction-based framework for adaptive resource allocation, combining space-time-frequency spreading (STFS) techniques with Bayesian Game approaches. We introduce novel modified Simultaneous Ascending Auction (mSAA) mechanism tailored to densely-deployed and low-complexity IoT networks, enabling distributed computation and reduced power consumption. By incorporating Bayesian game-based bidding strategies and optimizing dispersion matrices for signal transmission, the proposed approach ensures enhanced channel throughput and energy efficiency. Comparative analysis against traditional auction types, including First-Price and Second-Price Sealed-Bid Auctions, as well as the Vickrey–Clarke–Groves (VCG) mechanism, demonstrates the superiority of mSAA in terms of surplus maximization, revenue efficiency, and robustness in risk-prone bidding environments. Simulation results validate the model’s adaptability to heterogeneous IoT nodes and its potential for dense deployment across different environments and verticals.en
dc.description.sponsorshipResearch Ireland (13/RC/2077 P2); HORIZON EUROPE Marie Sklodowska-Curie Actions (101130739)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWickramasinghe, N. D., Dooley, J., Pesch, D. and Dey, I. (2025) 'Auction-based adaptive resource allocation optimization in dense and heterogeneous IoT networks', IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2025.3624456en
dc.identifier.doi10.1109/jiot.2025.3624456en
dc.identifier.issn2327-4662en
dc.identifier.issn2372-2541en
dc.identifier.journaltitleIEEE Internet of Things Journalen
dc.identifier.urihttps://hdl.handle.net/10468/18278
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofIEEE Internet of Things Journalen
dc.relation.project101130739en
dc.relation.project13/RC/2077 P2en
dc.rights© 2025, 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.en
dc.subjectIoT networksen
dc.subjectAuction game theoryen
dc.subjectResource allocationen
dc.subjectSpace-time-frequency spreadingen
dc.titleAuction-based adaptive resource allocation optimization in dense and heterogeneous IoT networksen
dc.typeArticle (peer-reviewed)en
dc.typejournal-articleen
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