Liu Meiqin, Zhang Duo, Zhang Senlin, Zhang Qunfei
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2017 Dec 4;17(12):2807. doi: 10.3390/s17122807.
Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme.
水下无线传感器网络（UWSN）可为水下目标跟踪提供一个很有前景的解决方案。由于计算和带宽资源有限，在每个时间间隔仅选择一小部分节点来跟踪目标。如何用少量节点提高跟踪精度是一个关键问题。近年来，已开发出一种节点深度调整系统并将其应用于网络部署和路由协议问题。据我们所知，所有现有的跟踪方案都使水下节点保持静止或随水流移动，并且节点深度调整尚未用于水下目标跟踪。本文研究UWSN中用于目标跟踪的节点深度调整方法。首先，由于费希尔信息矩阵（FIM）可以量化估计精度，推导其与节点深度的关系作为一种度量。其次，我们将节点深度调整公式化为一个优化问题，以确定激活节点的移动深度，在移动范围的约束下，将FIM的值用作目标函数，旨在在节点的移动距离上使其最小化。第三，为了有效地解决该优化问题，提出了一种改进的和声搜索（HS）算法，其中修改了生成概率以提高搜索速度和精度。最后，给出仿真结果以验证我们方案的性能。
