Edge mining the internet of things

Elena Gaura, James Brusey, Michael Allen, Ross Wilkins, Daniel Goldsmith, Ramona Rednic

    Research output: Contribution to journalArticle

    85 Citations (Scopus)
    110 Downloads (Pure)


    This paper examines the benefits of edge mining – data mining that takes place on the wireless, battery-powered smart sensing devices that sit at the edge points of the Internet of Things. Through local data reduction and transformation, edge mining can quantifiably reduce the number of packets that must be sent, reducing energy usage and remote storage requirements. Additionally, edge mining has the potential to reduce the risk to personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to three specific algorithms: Linear Spanish Inquisition Protocol (L-SIP), ClassAct, and Bare Necessities (BN), which are all instantiations of the General SIP (G-SIP). In general, the benefits provided by edge mining are related to the predictability of data streams and availability of precise information requirements; results show that L-SIP typically reduces packet transmission by around 95% (20-fold), BN reduces packet transmission by 99.98% (5000-fold) and ClassAct reduces packet transmission by 99.6% (250-fold). Although energy reduction is not as radical due to other overheads, minimisation of these overheads can lead to up to a 10-fold battery life extension for L-SIP, for example. These results demonstrate the importance of edge mining to the feasibility of many IoT applications.
    Original languageEnglish
    Pages (from-to)3816-3825
    JournalIEEE Sensors
    Issue number10
    Publication statusPublished - 2013

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    • edge mining
    • data mining
    • Internet of Things


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