The Research Of Capacity In Cluster-Based Underwater Acoustic Sensor Networks Based On Stochastic Geometry

Underwater acoustic sensor networks (UASNs) with high civil and military value, can be widely used in ocean information collection, resource exploration, environmental monitoring, disaster forecast, auxiliary navigation, distributed tactical observation, etc. However, the unique characteristics of the underwater acoustic communication channel, such as limited bandwidth, long propagation delay, time-variation, severe multipath and energy-restricted batteries of the underwater sensors, bring huge research challenges to UASNs. This article mainly focuses on the network capacity of the cluster-based underwater acoustic sensor networks by some theoretical research and simulation analysis. The main contributions of this paper are described as follows:Firstly, this paper briefly discusses the composition, characteristic, topology structure and network protocol architecture of UASNs, and expounds the research status at home and abroad and the key techniques. The structure characteristics and research focus of the cluster-based UASNs are introduced. This paper also expounds several classic definition of network capacity in academia, and discusses the research progress on network capacity of the terrestrial wireless sensor networks and UASNs in detail.Secondly, according to the propagation characteristics of underwater acoustic channel and structure features of cluster-based UASNs, with the combination of signal to interference plus noise ratio, we defined some capacity performance index, such as success probability, throughput density and transport capacity. Based on the theory of random geometry, a network capacity analytic model used in the cluster-based UASNs is presented in this paper.Thirdly, the influence of the channel allocation methods, the network's frequency, the node's emission power and the size of the cluster structure on network capacity is studied in this paper by means of computer simulation. The simulation results verify the validity of the theoretical analysis, and can provide some reference for the application and the design of the cluster-based UASNs.