Predictive intelligence to the edge: impact on edge analytics

Harth, N., Anagnostopoulos, C. and Pezaros, D. (2018) Predictive intelligence to the edge: impact on edge analytics. Evolving Systems, 9(2), pp. 95-118. (doi: 10.1007/s12530-017-9190-z)

141774.pdf - Published Version
Available under License Creative Commons Attribution.



We rest on the edge computing paradigm where pushing processing and inference to the edge of the Internet of Things (IoT) allows the complexity of predictive analytics to be distributed into smaller pieces physically located at the source of the contextual information. This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud. We propose a lightweight, distributed, predictive intelligence mechanism that supports communication efficient aggregation and predictive modeling within the edge network. Our idea is based on the capability of the edge nodes to (1) monitor the evolution of the sensed time series contextual data, (2) locally determine (through prediction) whether to disseminate contextual data in the edge network or not, and (3) locally re-construct undelivered contextual data in light of minimizing the required communication interaction at the expense of accurate analytics tasks. Based on this on-line decision making, we eliminate data transfer at the edge of the network, thus saving network resources by exploiting the evolving nature of the captured contextual data. We provide comprehensive analytical, experimental and comparative evaluation of the proposed mechanism with other mechanisms found in the literature over real contextual datasets and show the benefits stemmed from its adoption in edge computing environments.

Item Type:Articles
Additional Information:A correction to this article is available at
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos and Pezaros, Professor Dimitrios and Harth, Miss Natascha
Authors: Harth, N., Anagnostopoulos, C., and Pezaros, D.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Evolving Systems
ISSN (Online):1868-6486
Published Online:28 August 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Evolving Systems 9(2): 95-118
Publisher Policy:Reproduced under a Creative Commons License
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
643481A Situation-aware information infrastructureDimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/L026015/1COM - COMPUTING SCIENCE
709131Network Measurement as a Service (MaaS)Dimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/N033957/1COM - COMPUTING SCIENCE
722161FRuIT: The Federated RaspberryPi Micro-Infrastructure TestbedJeremy SingerEngineering and Physical Sciences Research Council (EPSRC)EP/P004024/1COM - COMPUTING SCIENCE
608831IMC2: Instrumentation, Measurement and Control for the CloudDimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/L005255/1COM - COMPUTING SCIENCE