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Title: Multi-criteria IoT Resource Discovery: A Comparative Analysis
Authors: Nunes, Luiz Henrique
Estrella, Julio Cezar
Perera, Charith
Reiff-Marganiec, Stephan
Delbem, Alexandre Cláudio Botazzo
First Published: 14-Dec-2016
Publisher: Wiley
Citation: Software: Practice and Experience, 2016
Abstract: 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.
DOI Link: 10.1002/spe.2469
ISSN: 0038-0644
eISSN: 1097-024X
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016 John Wiley & Sons, Ltd. Deposited with reference to the publisher’s open access archiving policy.
Description: 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.
Appears in Collections:Published Articles, Dept. of Computer Science

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