基于动态分簇的移动目标追踪方法

doi:10.11772/j.issn.1001-9081.2017.01.0065

计算机应用 ›› 2017, Vol. 37 ›› Issue (1): 65-72.DOI: 10.11772/j.issn.1001-9081.2017.01.0065

• 2016年全国开放式分布与并行计算学术年会(DPCS2016)论文 • 上一篇下一篇

基于动态分簇的移动目标追踪方法

包威, 毛莺池, 王龙宝, 陈小丽

河海大学计算机与信息学院, 南京 211100

收稿日期:2016-07-20 修回日期:2016-08-05 出版日期:2017-01-10 发布日期:2017-01-09
通讯作者: 毛莺池
作者简介:包威(1990-),男,江苏淮安人,硕士研究生,主要研究方向:无线传感器网络、分布式计算、并行处理;毛莺池(1976-),女,上海人,副教授,博士,CCF会员,主要研究方向:分布计算与并行处理、分布式数据管理;王龙宝(1977-),男,江苏盐城人,讲师,主要研究方向:智能数据处理、项目管理;陈小丽(1993-),女,河南洛阳人,硕士研究生,主要研究方向:无线传感器网络、分布式计算。
基金资助:
国家自然科学基金资助项目（U1301252）；国家科技支撑计划项目（2013BAB06B04）；中国华能集团公司总部科技项目（HNKJ13-H17-04）；云南省科技计划项目（2014GA007）；中央高校基本科研业务费专项资金资助项目（2015B22214）。

Moving target tracking scheme based on dynamic clustering

BAO Wei, MAO Yingchi, WANG Longbao, CHEN Xiaoli

College of Computer and Information, Hohai University, Nanjing Jiangsu 211100, China

Received:2016-07-20 Revised:2016-08-05 Online:2017-01-10 Published:2017-01-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (U1301252), the National Science and Technology Support Program (2013BAB06B04), the Technology Project of China Huaneng Group Company Headquarters (HNKJ13-H17-04), the Science and Technology Project of Yunnan Province (2014GA007), the Special Fund for Basic Scientific Research of Central Universities (2015B22214).

摘要/Abstract

摘要： 针对无线传感器网络（WSN）中目标追踪的准确性低、网络能耗过高和网络生命周期短等问题，提出基于动态分簇的移动目标追踪技术。首先，构建了双层环状动态分簇的拓扑模型（TRDC），并提出了动态分簇的更新算法；其次，在质心定位算法基础上，考虑到节点的能量，提出了基于功率级别的质心定位（CLPL）算法；最后，为了进一步减小网络的能耗，改进CLPL算法，提出了随机性定位算法。在仿真实验中，与静态簇相比，网络周期延长了22.73%；与非环状簇相比，丢失率降低了40.79%；而追踪准确性与基于接受信号强度值（RSSI）算法相差不大。所提的追踪技术能够有效保证追踪准确度，同时降低网络能耗，减小目标丢失率。

关键词: 无线传感器网络, 双层环状, 目标追踪, 动态簇, 质心定位

Abstract: Focused on the issues of low accuracy, high energy consumption of target tracking network and short life cycle of network in Wireless Sensor Network (WSN), the moving target tracking technology based on dynamic clustering was proposed. Firstly, a Two-Ring Dynamic Clustering (TRDC) structure and the corresponding TRDC updating methods were proposed; secondly, based on centroid localization, considering energy of node, the Centroid Localization based on Power-Level (CLPL) algorithm was proposed; finally, in order to further reduce the energy consumption of the network, the CLPL algorithm was improved, and the random localization algorithm was proposed. The simulation results indicate that compared with static cluster, the life cycle of network increased by 22.73%; compared with acyclic cluster, the loss rate decreased by 40.79%; there was a little difference from Received Signal Strength Indicator (RSSI) algorithm in accuracy. The proposed technology can effectively ensure tracking accuracy and reduce energy consumption and loss rate.

Key words: Wireless Sensor Network (WSN), two-ring clustering, target tracking, dynamic cluster, centroid localization

中图分类号:

TP393.02

包威, 毛莺池, 王龙宝, 陈小丽. 基于动态分簇的移动目标追踪方法[J]. 计算机应用, 2017, 37(1): 65-72.

BAO Wei, MAO Yingchi, WANG Longbao, CHEN Xiaoli. Moving target tracking scheme based on dynamic clustering[J]. Journal of Computer Applications, 2017, 37(1): 65-72.

参考文献

[1] 孙利民,李建中,陈渝,等.无线传感器网络[M].北京:清华大学出版社,2005:135-155.(SUN L M, LI J Z, CHEN Y, et al. Wireless Sensor Network[M]. Beijing:Tsinghua University Press, 2005:135-155.)
[2] BRANCH J W, GIANNELLA C, SZYMANSKI B, et al. In-network outlier detection in wireless sensor networks[J]. Knowledge and Information Systems, 2013, 34(1):23-54.
[3] MO L, HE Y, LIU Y, et al. Canopy closure estimates with GreenOrbs:sustainable sensing in the forest[C]//Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. New York:ACM, 2009:99-112.
[4] HE T, KRISHNAMURTHY S, LUO L, et al. VigilNet:an integrated sensor network system for energy efficient surveillance[J]. ACM Transactions on Sensor Networks, 2006, 2(1):1-38.
[5] HACKMANN G, GUO W, YAN G, et al. Cyber-physical codesign of distributed structural health monitoring with wireless sensor networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(1):63-72.
[6] MECHITOV K, SUNDRESH S, KWON Y, et al. Cooperative tracking with binary-detection sensor networks[C]//Proceedings of the 1st International Conference on Embedded Networked Sensor Systems. New York:ACM, 2003:332-333.
[7] KIM W, MECHITOV K, CHOI J Y, et al. On target tracking with binary proximity sensors[C]//IPSN 2005:Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks. Piscataway, NJ:IEEE, 2005:301-308.
[8] ZHAO F, SHIN J, REICH J. Information-driven dynamic sensor collaboration for tracking applications[J]. IEEE Signal Processing Magazine, 2002, 19(2):61-72.
[9] ZHONG Z, ZHU T, WANG D, et al. Tracking with unreliable node sequences[C]//INFOCOM 2009:Proceedings of the 28th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies. Piscataway, NJ:IEEE, 2009:1215-1223.
[10] WANG Z J, BULUT E, SZYMANSKI B K. Distributed energy-efficient target tracking with binary sensor networks[J]. ACM Transactions on Sensor Networks, 2010, 6(4):Article No. 32.
[11] KHALIL E A, ATTEA B A. Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks[J]. Swarm and Evolutionary Computation, 2011, 1(4):195-203.
[12] LIN K W, HSIEH M H, TSENG V S. A novel prediction-based strategy for object tracking in sensor networks by mining seamless temporal movement patterns[J]. Expert Systems with Applications, 2010, 37(4):2799-2807.
[13] SUN K, NING P, WANG C. Secure and resilient time synchronization in wireless sensor networks[C]//Proceedings of the 13th ACM Conference on Computer and Communications Security. New York:ACM, 2006:264-277.
[14] JEONG J, HWANG T, HE T, et al. MCTA:target tracking algorithm based on minimal contour in wireless sensor networks[C]//INFOCOM 2007:Proceedings of the 26th IEEE International Conference on Computer Communications, Joint Conference of the IEEE Computer and Communications Societies. Piscataway, NJ:IEEE, 2007:2371-2375.
[15] VICAIRE P, HE T, CAO Q, et al. Achieving long-term surveillance in VigilNet[J]. ACM Transactions on Sensor Networks, 2009, 5(1):Article No. 39.
[16] TYAGI S, KUMAR N. A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks[J]. Journal of Network and Computer Applications, 2013, 36(2):623-645.
[17] BROOKS R R, RAMANATHAN P, SAYEED A M. Distributed target classification and tracking in sensor networks[J]. Proceedings of IEEE, 2003, 91(8):1163-1171.
[18] YANG H, SIKDAR B. A protocol for tracking mobile targets using sensor networks[C]//Proceedings of the 2003 IEEE International Workshop on Sensor Network Protocols and Applications. Piscataway, NJ:IEEE, 2003:71-81.
[19] BULUSU N, ESTRIN D, GIROD L, et al. Scalable coordination for wireless sensor networks:self-configuring localization systems[C]//ISCTA 2001:Proceedings of the 6th International Symposium on Communication Theory and Applications. Ambleside, UK:[s.n.], 2001:1-6.
[20] 陈维克,李文锋,首珩,等.基于RSSI的无线传感器网络加权质心定位算法[J].武汉理工大学学报(交通科学与工程版),2006,30(2):265-268.(CHEN W K, LI W F, SHOU H, et al. Weighted centroid localization algorithm based on RSSI for wireless sensor networks[J]. Wuhan Science University of Technology (Transportation Science and Engineering), 2006, 30(2):265-268.)

基于动态分簇的移动目标追踪方法

Moving target tracking scheme based on dynamic clustering

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	孙环, 陈宏滨. 基于萤火虫算法的无线传感器网络节点重部署策略[J]. 计算机应用, 2021, 41(2): 492-497.
[2]	韩雨涝, 房鼎益. 基于链路交点相对位置信息的轻量级覆盖空洞检测算法[J]. 计算机应用, 2020, 40(9): 2698-2705.
[3]	胡润彦, 李翠然. 基于模糊控制的自供能无线传感器网络分簇算法[J]. 计算机应用, 2020, 40(9): 2691-2697.
[4]	韩雨涝, 房鼎益. 联合无线充电与数据收集的移动充电装置多目标路径规划算法[J]. 计算机应用, 2020, 40(6): 1745-1750.
[5]	卢光跃, 周亮, 吕少卿, 施聪, 苏可可. 基于图信号处理的无线传感器网络异常节点检测算法[J]. 计算机应用, 2020, 40(3): 783-787.
[6]	吴三柱, 李鹏, 吴三斌. 移动无线传感器网络中抑制病毒传播模型[J]. 计算机应用, 2020, 40(1): 129-135.
[7]	任秀丽, 陈洋. 无线传感网中数据传输延时优化的路由协议[J]. 计算机应用, 2020, 40(1): 196-201.
[8]	董发志, 丁洪伟, 杨志军, 熊成彪, 张颖婕. 基于遗传算法和模糊C均值聚类的WSN分簇路由算法[J]. 计算机应用, 2019, 39(8): 2359-2365.
[9]	何庆, 徐钦帅, 魏康园. 基于改进正弦余弦算法的无线传感器节点部署优化[J]. 计算机应用, 2019, 39(7): 2035-2043.
[10]	钱开国, 卜春芬, 王玉见, 申时凯. 基于可靠信标和节点度估计距离的无线传感器网络定位算法[J]. 计算机应用, 2019, 39(3): 817-823.
[11]	任晓奎, 李锋, 程琳. 基于动态损耗因子和权重的改进质心定位算法[J]. 计算机应用, 2019, 39(3): 824-828.
[12]	汪汉新, 洪思琴. 基于环分块的能耗均衡分簇路由算法[J]. 计算机应用, 2019, 39(1): 251-255.
[13]	党小超, 邵晨光, 郝占军. 半径可调的无线传感器网络三维覆盖算法[J]. 计算机应用, 2018, 38(9): 2581-2586.
[14]	王国玲, 杨文忠, 张振宇, 夏扬波, 殷亚博, 杨慧婷. 移动传感网中基于虚拟货币的路由策略[J]. 计算机应用, 2018, 38(9): 2587-2592.
[15]	马梦莹, 曾雅丽, 魏甜甜, 陈志德. 基于邻居节点预状态的无线传感器网络故障诊断算法[J]. 计算机应用, 2018, 38(8): 2348-2352.