Soft Computing Prediction Techniques in Ambient Intelligence Environments

In this paper, a review of prediction techniques suitable for ambient intelligence environments is presented. Prediction challenges in sensor networks are considered in two phases including pattern extraction and rule matching. The prediction techniques reviewed in this paper come from two main research areas, namely, data mining and soft computing techniques. Moreover, a statistical modelling technique based on Markov chain is also considered. In this paper, we identify the centralized and distributed techniques of both data mining and soft computing areas. In addition, we identify the distributed approaches that utilize computational power of sensors in an ambient intelligence environment. Moreover, we show that some techniques use compression, regression or fuzzy methods to reduce the size of the collected sensory data.