A novel characteristic correlation approach for aggregating data in wireless sensor networks

Numerous solutions have been proposed to improve the efficiency of wireless sensor networks (WSNs). Data aggregation, which reduces the data redundancy so as to mitigate energy consumption, is one of desirable solutions. One common feature of geographically close-by data known as spatial correlation, has been utilized for eliminating redundant information. To reduce redundancy and enhance eventual performance, we explore the possibility of combining sensing data with similar characteristics without considering spatial information.We define this relationship of data as characteristic correlation and propose an automatic procedure to discover characteristic correlation between sensor nodes (SNs) with limited overheads. Furthermore, we introduce a novel characteristic correlation based data aggregation approach that allows any SN to compress unlimited number of packets into virtual packets up to a constant number. With experimental and simulation results, our proposed approach is illustrated as an effective data aggregation method in term of data accuracy.