Leveraging Edge Computing through Collaborative Machine Learning

Portelli, K. and Anagnostopoulos, C. (2017) Leveraging Edge Computing through Collaborative Machine Learning. In: IKIT: The International Workshop on Information and Knowledge in the Internet of Things, in conjunction with the IEEE 5th International Conference on Future Internet of Things and Cloud (IEEE FiCloud 2017), Prague, Czech Republic, 21-23 Aug 2017, (Accepted for Publication)

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Abstract

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices.

Item Type:Conference Proceedings
Status:Accepted for Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Portelli, Mr Kurt and Anagnostopoulos, Dr Christos
Authors: Portelli, K., and Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
736751PRIMES: Personalised Recommendations and Internationalisation for MOOCs in European SchoolsChristos AnagnostopoulosEuropean Commission (EC)KA201-024631COM - COMPUTING SCIENCE