Study On Localizability Judgment In Underwater Sensor Networks Based On Skeleton Extraction And Rigidity Theory

Underwater sensor networks (UWSNs) is an underwater monitoring network system, which consists of numerous sensor nodes that have the ability to calculate and perform acoustic communication. UWSNs, one of the hottest research spots at present, has been widely applied in many fields, such as sea resources reconnaissance, environment monitoring, auxiliary navigation and tactical surveillance, etc. In most cases, only when the position information is obtained, does the data collected by the sensor nodes make sense. In the three dimensional underwater space, it is a key topic to judge the localizability of the unknown sensor node in all kinds of applications. The main problem studied in this paper and its major innovations are summarized as follows.(1) This paper researches the localizability problem of the UWSNs and for which, a localizability judgment method is proposed based on skeleton extraction and Rigidity Theory. Firstly, UWSNs is converted to an undirected graph. Secondly, by eliminating its pseudo node and cutting edge, the graph will shrink to the structure with one or more independent connected sub-graph. Lastly, the global rigidities of the connected sub-graphs are judged one after another, and then the localizable nodes are figured out. The extensive simulations demonstrate that the proposed localizability judgment method can achieve excellent success ratio and efficiency in the networks with different sensor numbers, sensor density and connectivity.(2) A comprehensive assessment method based on Analytic Hierarchy Process is proposed to evaluate whether the localization of the nodes is reliable. When calculating the position of the localizable nodes, the reliability of the results varies due to many factors, including number of anchor nodes connected with the node, degree of the node and distance between the nodes, etc. In this paper, Analytic Hierarchy Process is used to evaluate the localization credibility of the localizable nodes, and then the sequence of the localization calculation is figured out, which can help improve the quality of the localization results. The experiment results demonstrate the effectiveness of the method.