In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions (Wanglei, 2015). The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher's work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.
1A2-S03 Tracking of Human Activities in Smart Home Based on Bayesian Networks(Integrating Ambient Intelligence(2))
