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

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.     

Measuring Iron Loss With a No-Load Torque Rotating Field Device

Most devices used to forecast the iron loss in rotating electrical machines do not take into account the real working conditions. This paper describes a new measurement apparatus that does account for real working conditions. We have tested it with two kinds of iron silicon sheets. The experimental results allow us to model the evolution of their iron loss.     

Effect of cotton waste and spinning parameters on rotor yarn quality

In this paper, we study the effect of cotton wastes and different spinning parameters on the rotor yarn quality. Statistical models are established to predict a total quality index and the optimum spinning conditions by using maximum cotton waste rate. The results indicate that rotor parameters (diameter, form and speed) have an effect as significant as waste proportion. With optimum spinning conditions, the secondary raw material can be mixed in a proportion between 15% and 25% without hardly noticeable changes in quality.     

Performance enhancement of a household refrigerator by addition of latent heat storage

This paper studies the effect of adding a phase change material (PCM) slab on the outside face of a refrigerator evaporator. A dynamic model of the vapour compression cycle including the presence of the phase change material and its experimental validation is presented. The simulation results of the system with PCM show that the addition of thermal inertia globally enhances heat transfer from the evaporator and allows a higher evaporating temperature, which increases the energy efficiency of the system. The energy stored in the PCM is yielded to the refrigerator cell during the off cycle and allows for several hours of continuous operation without power supply.