Hybrid-based framework for COVID-19 prediction via federated machine learning models.

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.

Hybrid-based framework for COVID-19 prediction via federated machine learning models

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS);(i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.

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

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.