An Optimal Node Depth Adjustment Method with Computation-Efficiency for Target Tracking in UWSNs

作者：Luo, Junhai; Han, Ying; Chen, Yue

来源：21st International Conference on Information Fusion, FUSION 2018, 2018-07-10 To 2018-07-13.

DOI：10.23919/ICIF.2018.8455792

• Computation efficiency
• Distance measuring sensors
• Mobility of sensor nodes
• Multiplicative noise
• Objective functions
• Tracking accuracy
• Underwater environments
• Underwater wireless sensor networks

The effective node depth adjustment of distance-measuring sensors for target tracking in underwater wireless sensor networks (UWSNs) is investigated in this paper. Due to the limited energy and bandwidth in UWSNs, there is only a part of sensors participating in the tracking task. In this paper, the mobility of sensor nodes in depth is utilized to improve the tracking accuracy. Firstly, considering the complexity of the underwater environment, the measurement error is formulated as addictive and multiplicative noise. Secondly, the relationship between the depth of sensor nodes and the Fisher information matrix (FIM) is derived and taken as the metric for tracking accuracy. Thirdly, the optimal depth adjustment is determined for active sensors with low complexity by simplifying the objective function. Finally, by combining the optimal depth adjustment and traditional sensor selection algorithm, the best task sensors are selected for the purpose of the energy-efficiency. The simulation results illustrate the performance of the proposed method for improving the tracking accuracy and computational efficiency on the premise of employing the same number of sensors.

• 出版日期2018
• 单位电子科技大学