IoT‐fog‐cloud based architecture for smart systems: Prototypes of autism and COVID‐19 monitoring systems

Abstract

Making resources closer to the user might facilitate the integration of new technologies such as edge, fog, cloud computing, and big data. However, this brings many challenges shall be overridden when distributing a real-time stream processing, executing multiapplication in a safe multitenant environment, and orchestrating and managing the services and resources into a hybrid fog/cloud federation. In this article, first, we propose a business process model and notation (BPMN) extension to enable the Internet of Things (IoT)-aware business process (BP) modeling. The proposed extension takes into consideration the heterogeneous IoT and non-IoT resources, resource capacities, quality of service constraints, and so forth. Second, we present a new IoT-fog-cloud based architecture, which (i) supports the distributed inter and intralayer communication as well as the real-time stream processing in order to treat immediately IoT data and improve the entire system reliability, (ii) enables the multiapplication execution within a multitenancy architecture using the single sign-on technique to guarantee the data integrity within a multitenancy environment, and (iii) relies on the orchestration and federation management services for deploying BP into the appropriate fog and/or cloud resources. Third, we model, by using the proposed BPMN 2.0 extension, smart autistic child and coronavirus disease 2019 monitoring systems. Then we propose the prototypes for these two smart systems in order to carry out a set of extensive experiments illustrating the efficiency and effectiveness of our work.

Ameni Kallel Chaari

Computer Technologist Teacher

My main fields of interest include Virtualization, Cloud Computing, Internet of Things, with a current focus on dynamic allocation and management of virtualized compute and network resources.

Molka Rekik

Assistant professor

My research interests include cloud engineering, business intelligence, and optimization.

Mahdi Khemakhem

Associate Professor

My research interests are mainly in artificial intelligence including complex systems modeling, heuristics, meta-heuristics, and exact algorithms for combinatorial optimization problems in transportation and networks, resources management, cloud computing, IoT, etc.