Recommendation technology is an essential component of the Internet of Things (IoT) services that can help users get information at any time and from any place. Traditional recommendation algorithms, on the other hand, are unable to satisfy the IoT environment’s swift and reliable recommendation criteria. The use of mathematical and information discovery methods to overcome the relationship with target consumers in order to have desired items is known as a recommendation system. In this paper, a recommendation algorithm based on collaborative filtering is proposed. In this sense, the recommendation method (Recommender Systems) was developed; it is focused on the user’s characteristics, such as hobbies, and it is recommended to satisfy the object’s user specifications, also known as customized recommendation system (Personalized Recommender Systems), The majority of modern e-commerce recommender programs tend to recommend the best goods to a customer, believing that each product’s properties remain constant. Some properties, such as price discounts, can, however, be customized to respond to the preferences of each customer..

The Internet of Things (IoT) is expected to have a significant impact during the pandemic. Individuals are using IoT for educational purposes (as students and trainers), office work, banking, and medical jobs during the pandemic, according to the (COVID-19) survey. Individuals who have used IoT services during pandemic situations have found that it allows them to maintain a close physical distance from illness. On the other hand, individuals face the main challenge of using IoT as it causes social isolation and limits the human touch. An anonymous survey and an immediate randomized process were used to collect data. This paper aims to provide a framework for supermarkets. The proposed approach focuses on different retail operations using Internet of Things technology. The process of collecting and organizing the various store operations becomes noticeably faster once items are connected to the platform. The obtained results show the feasibility of the proposed framework.

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Abstract-Recent years have witnessed the increase of the field of mobile learning, fostered by the continuous development of mobile computing and wireless technology, today the mobile learning presents a fundamental approach to satisfy our daily needs and requirements. Specifically, in this work, we aim to study as model and simulate the ambient mobile system which is based on intelligent agents. The mobile agent is not based on the traditional client server however it is based on the distributed ones. The present article proposes a mobile intelligent agent based architecture for the MLearning that aims to facilitate the teacher and student acquisition. M-Learning is a new research area which became a principal tool for our education system. So we produced an adapted agent based approach for an efficient flexible. In our work we proceed as follows: first, we introduce the scope and the genesis of our research, second, we hold out the m-learning is the next generation of e-learning, afterwards, we present our AMMAS (Ambient Mobile Multi-Agents System) model for the M-Learning and an overview of the system implementation, and finally we conclude our work and give some perspectives.