Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Cloud and IoT

Participants : Valeria Loscri, Nathalie Mitton, Riccardo Petrolo.

Innovative and effective solutions to the fragmentation issues in the Internet of Things (IoT) landscape have been designed and proof of concept have been implemented to show the feasibility and effectiveness of the Cloud of Things (CoT) paradigm. In other words, we have focused on the convergence of Web semantic technologies and the Cloud computing concept as key enabler of an horizontal integration of various IoT applications and platforms [21]. The heterogeneity has to be considered not only in terms of applications and platforms, but another "type of heterogeneity" that deserves to be considered and analyzed is based on different devices and their interoperability.

A feasible solution to make different and heterogeneous devices to "interoperate" is based on the exploitation of a gateway. In particular, we have considered a Gateway-as-a-Service (Gaas) in [36], where we have shown that it is an efficient and lightweight device, which can be shared between several final users. Through the container virtualization technologies, we have been able to show how several platform requirements can be met, in a context where constrained devices have been considered. This study has demonstrated the Gateway-as-a-Service (GaaS) effectiveness and its exploitability in several IoT contexts, such as smart home, buildings, farms, agriculture environments, etc.

A different and complementary, to the previous solutions, perspective of IoT paradigm is represented by the management of the huge amount of data that have to be treated in the different IoT based applications. In [45], an infer algorithm has been proposed and more specifically an Bayesian Inference Approach (BIA) with the amin objective to avoid the transmission of high spatio-temporal correlated data.