Localization based on best spatial correlation distance mobility prediction for underwater wireless sensor networks
Localization based on best spatial correlation distance mobility prediction for underwater wireless sensor networks
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DOI：
10.1109/chicc.2015.7260883
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Meiqin Liu;Xiaodong Guo;Senlin Zhang
中科院分区：
文献类型：
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作者：
Meiqin Liu;Xiaodong Guo;Senlin Zhang
为了在保持较高定位覆盖和定位精度的同时降低通信成本，提出了一种新的水下无线传感器网络定位方案，即基于最佳空间相关距离移动预测的定位方案。节点利用水下节点的移动性与GET之间的空间相关性来预测自己的移动模式。为了保持较小的定位误差，计算能力较强的节点计算最佳空间相关距离，以便邻居节点预测自己的移动模式。LBMP定义了节点的置信度，以评估移动模式和位置预测的准确性。通过控制置信度门限的值，LBMP能够保证移动模式和位置预测的质量。仿真实验表明，与没有选择最佳空间相关距离的方案相比，LBMP在保持较高的定位覆盖率和定位精度的同时，明显降低了通信开销。
In order to reduce the communication cost while keeping the localization coverage and localization accuracy high, we propose a new localization scheme for underwater wireless sensor networks, i.e., localization based on best spatial correlation distance mobility prediction (LBMP). Nodes predict their mobility pattern by utilizing the spatial correlation between the mobility of underwater nodes and get located. In order to keep the localization error small, nodes with great computation ability calculate the best spatial correlation distance for the neighbor nodes to predict their mobility pattern. LBMP defines the confidence of a node to evaluate the accuracy of mobility pattern and location prediction. By controlling the value of the confidence threshold, LBMP can guarantee the quality of mobility pattern and location prediction. Simulation experiments show that, comparing to the scheme without the selection of best spatial correlation distance, LBMP has better performance in keeping relatively high localization coverage and localization accuracy while reducing communication cost apparently.