Research On AUV-aided Data Collection Strategy For Underwater Sensor Networks

Underwater sensor networks are an effective way to achieve smart oceans,and marine science and underwater Io T applications rely on reliable data collection from underwater sensor networks.Autonomous Underwater Vehicle(AUV)assisted underwater data collection effectively reduces the packet loss rate and energy consumption of sensor nodes during long-range data transmission,further improving the reliability of data collection.Due to the slow navigation speed of AUV,the ocean currents in the underwater environment change the size and direction of AUV navigation speed and lead to the movement of sensor nodes and dynamic changes in network topology,the AUV-assisted data collection method has problems such as large data transmission delay and difficulty in pre-planning the AUV global path.In this thesis,we investigate both single AUV-assisted data collection strategy and multi-AUV cooperative data collection strategy in a complex underwater environment to improve the information value of sensor networks and reduce the energy consumption of AUVs.The main work of this thesis is as follows:For the problems of sensor node movement and AUV yaw in the ocean current environment,this thesis proposes an adaptive data collection strategy based on greedy algorithm.Firstly,an optimization model is constructed to jointly optimize the information value and AUV energy consumption,and an acoustic-magnetic heterogeneous communication method is designed to effectively reduce the data transmission time delay of the network.Secondly,the feasible domain of AUV under the influence of different ocean current velocities is analyzed,and the cluster head node with the highest energy consumption efficiency is selected as the next visited target node based on greedy algorithm in the feasible domain.Besides,based on the real-time ocean current information,the position of the sensor node and the actual sailing speed of AUV are predicted,and the best sailing direction of AUV to reach the target node is derived.Finally,the simulation results show that the algorithm proposed in this thesis can use ocean currents to assist AUV navigation with higher energy consumption efficiency compared with existing algorithms.Considering that the long data collection time of a single AUV cannot meet the demand of low latency and low power task collection,this thesis proposes a cooperative data collection strategy for multiple AUVs based on the auction mechanism.First,a unified utility function for data collection under emergency and non-emergency situations is constructed,and connections are established between different constraints and optimization objectives under different situations.Second,the data collection of each cluster is considered as a different task,and the task is assigned to AUVs for completion using an improved auction mechanism.The maximum amount of tasks that can be executed by AUVs,the matching degree between the execution capability of AUVs and the task difficulty,and the matching order between tasks and AUVs are considered in the task assignment process to achieve load balancing.Finally,based on the results of task assignment,a path planning algorithm based on the speed synthesis algorithm is used to select the sailing path for each AUV,and task assignment and path planning are dynamically performed to determine the cluster head nodes visited by AUVs each time.The simulation part verifies the superiority of the performance of the cooperative data collection strategy for multiple AUVs based on the auction mechanism in terms of system information value and energy consumption of AUVs by analyzing the effects of different task assignment algorithms on the system utility function.This thesis contains 31 pictures,8 tables,and 82 references.