Research On Topology Optimization Based On AUV In UWSNs

With the booming information technology, today more and more data is collected to be analysed and used. Perception and the Internet have become a major theme of modern information technology. With the great advances in information technology, wireless sensor networks is being used more and more widely. As a extension and expansion of wireless sensor network, underwater wireless sensor networks(UWSNs) has important applications in ocean exploration, hydrological monitoring, offshore development and other fields.Topology optimization is an important topic on the research of UWSNs. Topology optimization can be divided into two parts: coverage optimization and connectivity optimization. AUV(autonomous underwater vehicle) equipped with underwater wireless sensor module has more storage, better processing capability and it can move autonomously. Compared to the traditional static UWSNs node, AUV has great advantages. This paper proposes two topology optimization algorithm based on AUV movement, respectively, to achieve coverage optimization and connectivity optimization, in UWSNs.This paper proposes a coverage optimization algorithm based on AUV movement in UWSNs. Firstly, the area to be covered is mapped into many hex cells, and then in order to achieve restoration of coverage blind the AUV traverses each cell according to a proper strategy. The algorithm overcomes the problem that the underwater environment is complex and unknown so that it is difficult to reliably cover the scene. It also helps the AUV minimizing the consumption of traversing paths. The paper also analyzes and discusses the situation of 3D scenes and multiple AUVs collaborations. Simulation results show that the algorithm performs well on the restoration of coverage blind, no matter the scenes are regular, irregular or discontinuous.In addition, this paper also proposes a connectivity optimization algorithm based on AUV movement in UWSNs. First, the UWSNs node estimates the approximate time based on the previous energy consumption. Before the energy is running out, the node sends an information to nearby AUV through the UWSNs to report the state of the node. If the situation is satisfied that the region needs to add a new node, an AUV will move to a nearby location of the failed node, optimizing the connectivity and balancing the overall energy cost of the network. The algorithm is distributed, with low complexity and small amount of data transportation. Simulation results show that the algorithm can help to repair the topology vulnerable regions in UWSNs. It improves the connectivity of the network and extends the lifetime of the network.