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工程科学与技术:2018,50(5):183-188,215
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受限浮动水下传感器网络定位
(1.青岛科技大学 信息科学技术学院, 山东 青岛 266061;2.中国海洋大学 信息科学与工程学院, 山东 青岛 266100)
Restricted Floating Localization in Underwater Sensor Networks
(1.School of Info. Sci. & Technol., Qingdao Univ. of Sci. and Technol., Qingdao 266061, China;2.College of Info. Sci. and Eng., Ocean Univ. of China, Qingdao 266100, China)
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投稿时间:2017-09-08    修订日期:2018-07-27
中文摘要: 为了确保水下传感器网络节点不会随水流离开监测区域,通常用缆绳把节点与固定在水底的锚相连,使水下节点具有受限浮动性,然而考虑这一重要特性的研究成果非常有限。针对水下节点受限浮动考虑不足的问题,提出了受限浮动水下传感器网络定位算法(restricted floating localization,RFL)。首先,根据水下节点在重力、浮力、水流冲力、缆绳拉力作用下的活动规律,建立受限浮动节点模型;然后,采用一个移动信标辅助定位,该移动信标在部署区域内沿直线移动,每过一段时间变换一次方向并广播位置信息,利用移动信标的位置信息结合水下节点的受限移动规律,通过理论分析推导锚的位置,并通过多次计算取中值以降低锚位置的求解误差;接下来,利用锚和移动信标的位置信息,计算出水下节点的位置。仿真分析了RFL算法中锚位置误差、锚对节点定位的影响以及节点位置误差,并将RFL算法与现有的TL算法、MFLA算法和LSLS算法进行比较。仿真结果表明,RFL算法的平均定位误差分别是TL算法的49.6%,MFLA算法的44.8%,LSLS算法的32.1%,其最大误差与最小误差都小于TL算法、MFLA算法和LSLS算法。RFL算法定位精度高于现有算法,而且具有较好的稳定性,简单可行,具备较高的实用价值。
Abstract:In order to prevent from leaving the monitoring area due to the water flow and have restricted mobility, the underwater sensor nodes are usually connected with anchors fixed in the bottom. However, most of previous studies do not consider about this important property. In this paper, a restricted floating localization (RFL) algorithm was proposed based on the mobile characteristics of underwater nodes. A restricted floating node model was firstly established according to the activity law of the underwater nodes under the action of gravity, buoyancy, water impulse, and cable tension. Then, a mobile beacon was used to assist localization. It moves along a straight trajectory in the deployment area, changes its direction and broadcasts its position information over a period of time. The mobile beacon's position information and the underwater node's moving rule were used by RFL makes to deduce the position of anchor. In order to reduce the estimation error, the median value was taken by multiple calculations. Then, the node localization process with the assist of anchor and mobile beacon's position information was completed. The anchor position error, the influence of anchor on node localization and node localization error in RFL algorithm were analyzed by simulation, and the RFL algorithm was compared with the existing algorithms (such as TL, MFLA and LSLS). Simulation results showed the average localization error of RFL was 49.6% of TL, 44.8% of MFLA and 32.1% of LSLS, and the maximum error and minimum error were smaller than TL, MFLA and LSLS. In conclusion, RFL has higher accuracy and stability than the existing algorithms. It is simple, feasible and highly practical.
文章编号:201700729     中图分类号:TP311    文献标志码:
基金项目:山东省自然科学基金资助项目(ZR2016FQ10);国家自然科学基金资助项目(61671261)
作者简介:郭瑛(1981-),女,副教授,博士.研究方向:物联网、传感器网络、海洋网络.E-mail:guoying@qust.edu.cn
引用文本:
郭瑛,王进新,韩清荷.受限浮动水下传感器网络定位[J].工程科学与技术,2018,50(5):183-188,215.
GUO Ying,WANG Jinxin,HAN Qinghe.Restricted Floating Localization in Underwater Sensor Networks[J].Advanced Engineering Sciences,2018,50(5):183-188,215.