Abstract
Intelligent environments, in general, represent the future evolutionary development step for the real world environment. However, to achieve their aims, an intelligent system is required to collect data from the surrounding environment. Wireless Sensor Networks (WSN) is one of the technologies that extensively used to collect such data. It has been used in many applications such as surveillance, machine failure diagnosis, weather forecast, intelligent environments, intelligent campuses and chemical/biological detection. Nonetheless, their nodes suffer from energy starvation due to the large number of messages need to be transferred through the network. The purpose of this paper is to investigate new approaches for data reduction in single and multimodal WSN. The proposed approaches are based on exponential smoothing predictors. At the same time, we believe that such approaches will enhance the reliability of the sensed data. Through large number of experiments, we test our approach through real data as well as through simulation.