Multi‐criteria IoT resource discovery: a comparative analysis

Nunes, Luiz H., Estrella, Julio C., Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346, Reiff-Marganiec, Stephan and Delbem, Alexandre N. 2017. Multi‐criteria IoT resource discovery: a comparative analysis. Software: Practice and Experience 47 (10) , pp. 1325-1341. 10.1002/spe.2469

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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 Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Publisher:	Wiley
ISSN:	1097-024X
Date of First Compliant Deposit:	23 November 2020
Date of Acceptance:	15 November 2016
Last Modified:	19 Nov 2024 05:30
URI:	https://orca.cardiff.ac.uk/id/eprint/134075

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