Coarse-Grain Data Gathering in Continuous Query for Periodical Phenomena in Wireless Sensor Networks

This paper presents a query optimization method for continuous data gathering in sensor networks. The method is based on a coarse-grain data collection approach by using the setting of thresholds and the analysis on the content to be sent over the network. In particular, the paper concentrates on the situation where phenomena under monitoring appear a periodical feature. An initiative data model is first created, and then at each single sensor node, instead of direct delivery of each result data, data communication is needed only when the current data is out of the range constrained by the periodical data model. The performance of the proposed algorithms shows that the proposed methods can achieve better performance than without performing the optimization.