No abstract

This paper addresses a multi-criteria decision problem regarding the more suitable device (system) to perform a certain task for Cyber-Physical Systems (CPS). New embedded systems provided everyday by manufacturers makes the decision on which device best fulfil an objective a very difficult work for engineers and developers. The proposed framework establishes a set of components that goes from formally describe possible solutions, criteria, constraints and priorities, capable of taking into account users' specific aspects, to finally propose a more suitable solution. To materialise all formal descriptions, a model-driven approach is followed. It allows the use and design of enablers for interoperability with standards or systems (e.g. Cyber-Physical Enterprise Systems). Models and methods are proposed to describe a device in terms of hardware, software and energy. It is enabled the use of different software languages as the integration of different, new or user-defined multi-criteria decision methods. The proposed framework shows that a better aware IoT System choice can be made and therefore stakeholders can perform a more suitable design of their Cyber-Physical Enterprise Systems.

The Internet-of-Things (IoT) is today one of the hypes in the technological world but despite the enormous attention and research investment, the clear business value is still hard to perceive. IoT deployments are costly to be installed, managed and maintained, and need to provide a very clear value to justify the investments. For another viewpoint, IoT technologies need to be proven before deployment, which implies the need to test and assess IoT solutions in real settings and involve the actual target users. And as such, this presents an opportunity to have IoT deployments with a clear business model mainly focused on real-life large-scale research and technological experimentation. This would mean having a sustainable IoT infrastructure in-place based on the provision of experimentation services and a trial environment to industry and research, which then could also present an opportunity to establish added-value (business) services. This is the exact idea of the flagship SmartSantander testbed facility and especially its major deployment in the city of Santander, Spain. The SmartSantander facility business model is built around experimentally-driven research and technology development thus attracting many experimenters from industry and European research projects. This model makes it possible to sustain an outstanding large-scale IoT deployment of around 12,000 sensors and on top of it the development of new the development of new services and applications especially targeting the needs of users (citizens, businesses, authorities) in smart-cities. This paper studies the business model of outstanding SmartSantander facility in order to provide a generic Business Model for IoT testbeds that can provide guidance and be adapted by owners (or owners to-be) wishing to exploit their IoT deployments as facilities supporting experimentation and trials of IoT solutions.

The Internet-of-Things (IoT) is today a reality, and Smart Systems have taken advantage of this to improve its own sense, act and control capabilities. IoT is a highly heterogeneous environment composed by a vast number of "things" (sensors, smart objects, etc.). These "things" are based on hardware platforms which can differ widely since manufactures are being capable of develop new devices every day to tackle different application domains. Consequently, a problem emerges regarding which will be the suitable, proper hardware solution for an IoT deployment. Make a right decision is probably one of the toughest challenges for science and technology managers. This work proposes a novel methodology to analyze a set of hardware alternatives based on user's multi-criteria requirements, and advice on the more suitable hardware solution for a specific situation. For proof-ofconcept it is used different Arduino boards as hardware alternatives, in which user requirements are based on hardware features. This methodology foresees its use during the development of Smart Systems (e.g.: Transportation, Healthcare) to optimize the selection of hardware platforms.