Context collection in the intelligent community base on the optimal stopping theory

Abstract

With the increasing popularity of the intelligent community, residents can enjoy a better service. The intelligent community context service can collect context by sensing nodes and handle the context to push the helpful information to the resident. Given the resident requirements in terms of the accuracy of data and timeliness of the service, the service must deliver the right context information to the right user at the right time. Hence, the quality of the collected context is especially important. Because of the dynamic network topology of the intelligent community, the quality indicators of the context vary over time. Consequently, the context quality is a decreasing function of time and context can be processed with a certain delay from its capturing time. The paper proposes the method that collects context based on the optimal stopping theory. The intelligent community context service delays the processing of the incoming context until the better quality context captured by the proposed method. And good quality context is favor of the context reasoning. Finally, the experiments are presented to confirm the validity of the proposed approach.