  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.
