Authors:
Enrico Ferrera
1
;
Xu Tao
1
;
Davide Conzon
1
;
Victor Sonora Pombo
2
;
Miquel Cantero
3
;
Tim Ward
4
;
Ilaria Bosi
1
and
Mirko Sandretto
1
Affiliations:
1
LINKS Foundation – Leading Innovation & Knowledge for Society, Turin, Italy
;
2
Improving Metrics, A Coruña, Spain
;
3
Robotnik Automation S.L.L., Valencia, Spain
;
4
Paremus Ltd, Wokingham, U.K.
Keyword(s):
Next-Generation Internet of Things, Self-aware Systems, Semi-autonomous Systems, Security, Privacy, Edge Computing, Model-based Development, Smart Behaviours.
Abstract:
Nowadays, the adoption of the Internet of Things is drastically increasing in different domains and is contributing to the fast digitalization of several different critical sectors. In the near future, next generation of IoT-based systems will become more complex to be designed and managed. An opportunity for the development of flexible smart IoT-based systems that drive the business decision-making is to take more precise and accurate decisions at the right time, collecting real-time IoT generated data. This involves a set of challenges including the complexity of IoT-based systems and the management of large-scale systems scalability. With respect to these challenges, we propose to automate the management of IoT-based systems mainly based on an autonomic computing approach; these systems should implement cognitive capabilities that allow them learning and generating decisions at the right time. Consequently, we propose a model-driven methodology for designing smart IoT-based system
s. With this objective, BRAIN-IoT paves the way to develop and demonstrate novel IoT concepts and solutions to underpin the Next Generation Internet of Things vision and architecture, that focusing on self-aware and semi-autonomous IoT systems, as well as on moving away from centralized cloud-computing solutions towards distributed intelligent edge computing systems.
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