Research and Implementation of the Context-Aware Middleware Based on Neural Network

Abstract

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a user's preference inside a smart home. The context-aware middleware utilizes 6 basic data for learning and predicting the user's preference on the home appliances: the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The user profile manager maintains history information for home appliances chosen by the user. The user-pattern learning and predicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an individual's preferences can be effectively evaluated and predicted by adopting the proposed context model.