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Data mining at the edge of the Internet of Things

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The talk examines the benefits of edge mining – data mining that takes place on the wireless, battery-powered, and smart, networked, 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. In addition, edge mining has the potential to reduce the risk in personal privacy through embedding of information requirements at the sensing point, limiting inappropriate use. The benefits of edge mining are examined with respect to two specific algorithms: linear Spanish Inquisition Protocol (L-SIP) and Bare Necessities (BN). 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) while BN reduces packet transmission by 99.98% (5000-fold). Context for the above is provided through a buildings’ monitoring (energy and environment) case study. Results from deployed systems are presented and design guidelines are emerged. Sound designs and minimization of software overheads can lead up to a 10-fold battery life extension for L-SIP, in this application. Concepts presented are also extrapolated to human physiological/movement monitoring, forging the path towards “forever” wearable body sensor networks.

This talk is part of the DIAL seminars series.

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