The rapid deployment of IoT in different areas generates a massive amount of data transferred to the Cloud. To solve this challenge a new paradigm, called Fog Computing, is located at the edge of the network and close to the connected objects. Its main role is to extend the capacities of Cloud and improve the performance and the QoS required by the applications by the use of different methods and techniques based on scheduling algorithms. In this paper, we review various recent studies available in the literature that are interested in the scheduling methods and algorithms used in Fog computing. The use of fog layer, in solving optimization problem, is faced with serious challenges. Therefore, to help practitioners and researchers, we present an in-depth overview of Fog Computing studying various scheduling methods and algorithms. We analyze, compare and classify these different scheduling approaches according to the nature of the algorithm used in the scheduling, the QoS optimized by the proposed approach and the type of applications in order to show what is suitable for critical IoT (CIOT), massive IoT (MIOT) and Industry IoT (IIOT). Finally, we present a comparison of the different simulation tools used to evaluate these approaches to guide fog computing developers/researchers which tool is suitable and most flexible for simulating the application under consideration.

The objective of this article is to model and implement a learner supervision system during a remote session of practical works (PW). This system will serve the users (learners, teachers, tutors) of our virtual laboratory during the assistance and evaluation process. It also makes it possible for the teachers to be aware of the progress status of the work. Another characteristic is added, it is a question of designing a model for the average learner based on his/her actions and reactions. The functionalities supported by this system are: the supervision in real-time (teachers and learners are connected simultaneously), supervision in differed time (using the activities' history) and guiding the learners.

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