Similar Data Detection for Cooperative Spectrum Monitoring in Space-Ground Integrated Networks

Space-ground aided cooperative spectrum monitoring, which combines the benefits of satellite components and terrestrial components for improving monitoring accuracy and enlarging monitoring area, has been becoming an emerging application of the space-ground integrated networks (SGIN). However, a short transmission window is usually provided for satellite components to connect with ground gateway, which means only a limited transmission time is allowed for the satellite component to upload the collected spectrum data. On the other hand, lots of redundancy may exist among the spectrum data collected by a single sensor during one collection period, which may further reduce the data uploading efficiency. In this paper, we investigate the similar data detection which is a matching problem for comparing two data, and it is important to the following data compression for improving data uploading efficiency. Firstly, the definition of the sharing fragment set is given. Then a metric method is presented to measure the redundancy of one data with respect to another data. We propose a Sharing Fragment Set (SFS) algorithm that can select a good sharing fragment set. Theoretical analysis proves that the proposed SFS algorithm is well suited to determine the redundancy between datas. In addition, we conduct an experiment based on the randomly produced synthetic dataset. Numerical results shows that the SFS algorithm performs better in selecting sharing fragment set compared with the Greedy-String-Tiling (GST) and simple greedy algorithm.