The Internet-of-Things (IoT), considered as Internet first real evolution, has become
immensely important to society due to revolutionary business models with the potential
to radically improve Human life. Manufacturers are engaged in developing embedded
systems (IoT Systems) for different purposes to address this new variety of application
domains and services. With the capability to agilely respond to a very dynamic market
offer of IoT Systems, the design phase of IoT ecosystems can be enhanced. However,
select the more suitable IoT System for a certain task is currently based on stakeholder’s
knowledge, normally from lived experience or intuition, although it does not mean that
a proper decision is being made. Furthermore, the lack of methods to formally describe
IoT Systems characteristics, capable of being automatically used by methods is also an
issue, reinforced by the growth of available information directly connected to Internet
spread.
Contributing to improve IoT Ecosystems design phase, this PhD work proposes a
framework capable of fully characterise an IoT System and assist stakeholder’s on the decision
of which is the proper IoT System for a specific task. This enables decision-makers
to perform a better reasoning and more aware analysis of diverse and very often contradicting
criteria. It is also intended to provide methods to integrate energy consumptionsimulation
tools and address interoperability with standards, methods or systems within
the IoT scope. This is addressed using a model-driven based framework supporting a
high openness level to use different software languages and decision methods, but also
for interoperability with other systems, tools and methods