Edge mining the internet of things

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

doi:10.1109/JSEN.2013.2266895

Edge mining the internet of things

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

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92 Citations (Scopus)

158 Downloads (Pure)

Abstract

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 language	English
Pages (from-to)	3816-3825
Journal	IEEE Sensors
Volume	13
Issue number	10
DOIs	https://doi.org/10.1109/JSEN.2013.2266895
Publication status	Published - 2013

Bibliographical note

© 2013 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.

Keywords

edge mining
data mining
Internet of Things

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10.1109/JSEN.2013.2266895

Edge mining the internet of things

Cite this

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title = "Edge mining the internet of things",

abstract = "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. ",

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author = "Elena Gaura and James Brusey and Michael Allen and Ross Wilkins and Daniel Goldsmith and Ramona Rednic",

note = "{\textcopyright} 2013 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.",

year = "2013",

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AU - Brusey, James

AU - Allen, Michael

AU - Wilkins, Ross

AU - Goldsmith, Daniel

AU - Rednic, Ramona

N1 - © 2013 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.

PY - 2013

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Edge mining the internet of things

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Keywords

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