Distributed classification with dynamic communication for air quality sensing

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dc.contributor.authorNash, Andrewen
dc.contributor.authorPesch, Dirken
dc.contributor.authorGuha, Krishnenduen
dc.date.accessioned2025-11-25T14:19:51Z
dc.date.available2025-11-25T14:19:51Z
dc.date.issued2025-08-27en
dc.description.abstractA body of work has focused on distributed task classification in sensor networks. The majority of works in this domain focus on either decision fusion, edge/fog computing (or equivalently, data fusion), split computing (feature fusion) or fully distributed agents. These methodologies, combined with machine learning classifiers, are being applied to problems such as UAV and robot guidance, smart cities and smart grids [1]. In contrast, we propose and define a novel hybrid strategy that makes greedy local decisions at end devices as to whether to operate in a distributed or split computing context. We devise a communication strategy for distributed classification, which balances communication efficiency with classification accuracy. We demonstrate our proposed methodology on a real-world IoT dataset that consists of air-quality sensor readings taken in a home environment. Experimental results depict that our proposed approach balances communication cost and classification accuracy between known optimal values. In addition to this, our proposed strategy can achieve high accuracy at lower cost than traditional methods. We explore in detail the conditions under which our model performs well, and determine some potentially promising avenues for future extension and development.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationNash, A., Pesch, D. and Guha, K. (2025) ‘Distributed classification with dynamic communication for air quality sensing’, IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Kalamata, Greece, 06-09 July 2025, pp. 1–6. https://doi.org/10.1109/ISVLSI65124.2025.11130290en
dc.identifier.doi10.1109/ISVLSI65124.2025.11130290en
dc.identifier.eissn2159-3477en
dc.identifier.endpage6en
dc.identifier.issn2159-3469en
dc.identifier.journaltitle2025 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/18268
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urihttps://ieeexplore.ieee.org/xpl/conhome/11129697/proceedingen
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.subjectDistributed processingen
dc.subjectSplit computingen
dc.subjectEdge computingen
dc.subjectIoTen
dc.titleDistributed classification with dynamic communication for air quality sensingen
dc.typeConference itemen
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