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Multi-criteria IoT Resource Discovery: A Comparative Analysis

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posted on 2016-11-18, 09:09 authored by Luiz Henrique Nunes, Julio Cezar Estrella, Charith Perera, Stephan Reiff-Marganiec, Alexandre Cláudio Botazzo Delbem
The growth of real world objects with embedded and globally networked sensors allows to consolidate the Internet of Things paradigm and increase the number of applications in the domains of ubiquitous and context-aware computing. The merging between Cloud Computing and Internet of Things named Cloud of Things will be the key to handle thousands of sensors and their data. One of the main challenges in the Cloud of Things is context-aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi-criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision methods and their quality of selection comparing them with the Paretooptimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.

History

Citation

Software: Practice and Experience, 2016

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Science

Version

  • AM (Accepted Manuscript)

Published in

Software: Practice and Experience

Publisher

Wiley

issn

0038-0644

eissn

1097-024X

Acceptance date

2016-11-15

Copyright date

2016

Available date

2017-12-14

Publisher version

http://onlinelibrary.wiley.com/doi/10.1002/spe.2469/full

Notes

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

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