Selection of IoT Platform with Multi-Criteria Analysis: Defining Criteria and Experts to Interview

Abstract

Industry 4.0 is having a great impact in all industries. This is not a unique product, but is composed of several technologies. IoT is a key intelligent factor that allows factories to act intelligently. By adding sensors and actuators to the objects, the object becomes intelligent because it can interact with people, other objects, generate data, generate transactions and react to the environment data. Currently there are very varied implementation options offered by several companies, and this imposes a new challenge to companies that want to implement IoT in their processes. The decision processes that companies must follow should not be free will or by hunches, since this contradicts a methodology and would make the decision process unrepeatable and unjustifiable. Decisions must be supported by methods that consider pros and cons of plural points of view that affect the decision process. With a wide range of IoT platforms, which are not directly comparable to each other, it seems that Multi-Criteria Decision Analysis (MCDA) can be useful to help companies make a decision on what platform to implement, depending on the circumstances prevailing in each company at the time to make the choice. This article shows the complexity of selecting an IoT platform and provides the key decision criteria that must be taken into account when evaluating IoT Platforms alternatives.

A Comprehensive Framework for Analyzing IoT Platforms: A Smart City Industrial Experience

“…In the work by Kondratenko et al [41], the authors are concerned with increasing the accuracy of decision making; they suggest using two different methods of multi-criteria decision making, for instance, the linear convolution and multiplicative convolution, to get to know which platform may suit the criteria. The work by Kondratenko et al [41] including other studies [42,43] provides ad hoc quantitative techniques, albeit costly and complex, for developing quantifiable criteria to increase the reliability of decision outcomes in an IoT platform selection scenario. It remains that [41][42][43] have not provided criteria and guidelines examining the suitability of an IoT platform for a given problem domain.…”

“…The work by Kondratenko et al [41] including other studies [42,43] provides ad hoc quantitative techniques, albeit costly and complex, for developing quantifiable criteria to increase the reliability of decision outcomes in an IoT platform selection scenario. It remains that [41][42][43] have not provided criteria and guidelines examining the suitability of an IoT platform for a given problem domain. Our work is in contrast to these quantitative techniques relying on the subjective definition of criteria that have no IoT-specific meaning and can therefore be difficult to use in a real evaluation exercise.…”