Distributed classification with dynamic communication for air quality sensing
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Date
2025-08-27
Authors
Nash, Andrew
Pesch, Dirk
Guha, Krishnendu
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
A 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.
Description
Keywords
Distributed processing , Split computing , Edge computing , IoT
Citation
Nash, 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/ISVLSI65124.2025.11130290
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Copyright
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