Inter-data commonality detection for spectrum monitoring in wireless sensor networks

Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks. However, there exists redundancy among the spectrum data collected by a sensor node within a data collection period, which may reduce the data uploading efficiency. In this paper, we investigate the inter-data commonality detection which describes how much two data have in common. We define common segment set and divide it into six categories firstly, then a method to measure a common segment set is conducted by extracting commonality between two files. Moreover, the existing algorithms fail in finding a good common segment set, so Common Data Measurement (CDM) algorithm that can identify a good common segment set based on inter-data commonality detection is proposed. Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings. In addition, we conduct an synthetic dataset which are produced randomly. Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling (GST) algorithm and simple greedy algorithm.