Multi-criteria IoT Resource Discovery: A Comparative Analysis

TL;DR

This paper compares three multi-criteria decision analysis methods (SAW, TOPSIS, VIKOR) for sensor selection in IoT, evaluating their effectiveness and quality against Pareto-optimal solutions in the context of Cloud of Things.

Contribution

It provides a comparative analysis of SAW, TOPSIS, and VIKOR methods for IoT sensor selection, focusing on their selection quality and behavior.

Findings

SAW, TOPSIS, and VIKOR show different behaviors in sensor selection.

The methods vary in the number of optimal solutions and redundancy.

Some methods approach Pareto-optimality more closely than others.

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.