Research on smart spaces represents one of the most innovative work being done recently. One of the interesting applications of smart spaces is the smart classroom. It aims at using new technologies and tools to provide adaptive, comfortable and personalized learning which supports and facilitates the learning operation. Often instructors waste a precious time of theirs courses on tasks not directly related to the course content especially adjusting several times both the light system of the classroom which consists of the light bulbs system and window blinds as well as the heating/cooling system and learning tools such as data show, moving board desktop computer. These systems should be adjusted according to the current context which has a high dynamic change. These activities prevent them from focusing on their main tasks and directly affect the proper functioning of the educational process. In some cases, after the end of the lesson and the leaving of the audience, devices of the classroom remain operating which causes an unjustified energy consumption. The aim of this paper is to propose a context-aware automation of classrooms which contributes to improving the quality of the educational process by automating tasks that are not directly related to the content of the courses and consume time and effort that would affect the smooth running of the educational process. The proposed smart classroom will provide a comfortable learning environment that ensures more emphasis on course content by instructor and students.

Semantic similarity measures usage is prevalent in pervasive computing with the following aims: 1) to compare the components of an application; 2) to recommend and rank services by degree of relevance; 3) to identify services by matching the description of a query with the available services; 5) to compare the current context with already known contexts. The existing works that apply semantic similarity measures to pervasive computing focus on one particular issue. Furthermore, surveys in this domain are limited to the recommendation or discovery of context-aware services. In this article, we therefore present a survey of context-aware semantic similarity measures used in various areas of pervasive computing.

Pervasive computing requires that devices must be context-aware in order to provide proactively adapted services to both user and applications according to the current context. Most of the proposed approaches of service adaptation in a pervasive computing system are either not context-aware or based on an inappropriate definition of context. In this paper we propose a context-aware dynamic service adaptation approach for a device operating in a pervasive computing system. The approach is then modeled using colored Petri Nets and simulated using the CPN-Tools in order to validate it.

Purpose: The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user’s location on the basis of a user’s mobility history. Design/methodology/approach: Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering. Findings: The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results. Originality/value: The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.