Super-Resolution Information Collection in Underwater Sensor Networks with Random Node Deployment: A Compressed Sensing Approach
In order to accurately realize the required information of the vast ocean area in a practical way, in this paper a novel compressed sensing (CS) based information collection scheme is proposed for the large-scale underwater networks. The proposed scheme consists of two main components: (1) A random node deployment method along with the consequent data gathering and transmitting; (2) A super-resolution information reconstruction method. The former is aimed at reducing the required sensor nodes to an affordable number, and the latter is aimed at enhancing the spatial resolution of information map. Compared to the traditional data collection methods, in the proposed scheme much fewer sensors, as well as less energy consumption and less channel bandwidth, are required to obtain the accurate information map of the same resolution. Mathematical analysis and experiment results both illustrate the validity and efficiency of the proposed scheme, not only for the synthesized scenario but also for the real scenario. Furthermore, because most of the nature phenomena are sparse in certain domain (for example, spatial domain or frequency domain), the CS-based scheme can be widely used for the large-scale ocean monitoring purposes, as well as other appreciate situations.