A Hybrid Modular Context-aware Services Adaptation for a Smart Living Room

Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task to be done proactively according to the inhabitant’s current context and in an unobtrusive and seamlessly manner. Although there are several works conducted for the development of smart homes with various technologies, in most cases, robust. However, the context-awareness aspect of services adaptation was not based on clear steps for context elements extraction (resp. clear definition of context). In this paper, we use the divide and conquer approach to master the complexity of automation task by proposing a hybrid modular system for context-aware services adaptation in a smart living room. We propose to use for the context-aware adaptation three techniques of machine learning, namely Naïve Bayes, fuzzy logic and case-based reasoning techniques according to their convenience.