WSN中基于稀疏观测和异步传输的分布式实时定位算法

doi:10.11959/j.issn.1000-0801.2016237

摘要/Abstract

摘要：

针对无线传感器网络（WSN）中现有集中式多维标度（MDS-MAP）节点定位算法在定位精度和分布式方面的不足，提出一种基于稀疏观测和异步传输的分布式实时定位算法。首先在传统MDS-MAP算法中融入稀疏观测机制，使其能够更好地符合实际观测场景；然后提出一种异步传输序列，使节点能够分布式计算距离观测，并通过分布式计算结果给出位置估计；最后通过提出的位置估计精化操作减小估计误差，最终实现节点的精确定位。实验结果表明，该算法具有较高的定位精度。

关键词: 无线传感器网络, 分布式实时定位, 稀疏观测, 异步传输序列, 多维标度

Abstract:

For the issues that the existing centralized multi-dimensional scaling（MDS-MAP）localization algorithms in wireless sensor network （WSN）have localization accuracy and distributed problem, a distributed real-time localization algorithm based on sparse observation and asynchronous transmission in WSN was proposed. Firstly, the sparse observation mechanism was fused into the traditional MDS-MAP algorithm to make it better conform to the actual observation scene. Then an asynchronous transmission sequence was used to enable the nodes can distribute computing distance and obtain the position estimation. Finally, the estimation error was reduced by an elaboration operation, and the precise positioning of the node was realized. The experimental results show that this algorithm has high positioning accuracy.

Key words: wireless sensor network, distributed real-time localization, sparse observation, asynchronous transmission sequence, multi-dimensional scaling

马勇,刘玉春. WSN中基于稀疏观测和异步传输的分布式实时定位算法[J]. 电信科学, 2016, 32(10): 94-100.

Yong MA,Yuchun LIU. Distributed real-time localization algorithm based on sparse observation and asynchronous transmission in WSN[J]. Telecommunications Science, 2016, 32(10): 94-100.

图/表 4

参考文献 15

[1]	唐琳，夏越 . 基于定向天线的无线传感网络环境自适应定位算法[J]. 电信科学， 2012,28(7):80-85. TANG L , XIA Y . Wireless sensor network environment adaptive localization algorithm based on directional antenna[J]. Telecommunications Science, 2012,28(7):80-85.
[2]	陈岁生，卢建刚，楼晓春 . 基于 MDS-MAP 和非线性滤波的 WSN 定位算法[J]. 浙江大学学报：工学版， 2012,46(5):866-872. CHEN S S , LU J G , LOU X C . Localization algorithm for wireless sensor networks based on MDS-MAP and nonlinearfiltering[J]. Journal of Zhejiang University（Engineering Science）, 2012,46(5):866-872.
[3]	谭志，张卉 . 无线传感器网络RSSI定位算法的研究与改进[J]. 北京邮电大学学报， 2013,36(3):88-91. TAN Z , ZHANG H . A modified mobile location algorithm based on RSSI[J]. Journal of Beijing University of Posts and Telecommuni-cations, 2013,36(3):88-91.
[4]	VANHEEL F , VERHAEVERT J , LAERMANS E , et al. Pseudo-3D RSSI-based WSN localization algorithm using linear regression[J]. Wireless Communications ＆ Mobile Computing, 2015,15(9): 1342-1354.
[5]	SHON M , JO M , CHOO H . An interactive cluster-based MDS localization scheme for multimedia information in wireless sensor networks[J]. Computer Communications, 2012,35(15): 1921-1929.
[6]	刘健苗，许新忠，黄书广，等. 改进的分布式无线传感器网络多维标度定位算法[J]. 传感技术学报， 2014,27(5):692-697. LIU J M , XU X Z , HUANG S G , et al. Advanced distributed multidimensional scaling localization algorithm for wireless sensor network[J]. Chinese Journal of Sensors and Actuators, 2014,27(5):692-697.
[7]	PATIL S , ZAVERI M . MDS and trilateration based localization in wireless sensor network[J]. Wireless Sensor Network, 2011,3(6): 198-208.
[8]	庞博，秦小麟，江国华，等. 权重感知的三维室内无线传感器网络部署算法[J]. 计算机科学， 2014,41(6):31-36. PANG B , QIN X L , JIANG G H , et al. Weight aware wireless sensor network deployment in 3D indoor space with obstacles[J]. JComputer Science, 2014,41(6):31-36.
[9]	GRACIOLI G , FROHLICH A A , PIRES R P , et al. Evaluation of an RSSI-based location algorithm for wireless sensor networks[J]. IEEE Latin America Transactions, 2011,9(1): 830-835.
[10]	LU X , DONG D , LIAO X . MDS-based wormhole detection using local topology in wireless sensor networks[J]. International Journal of Distributed Sensor Networks, 2012,11(2): 1548-1551.
[11]	ATSHAN W G . On a new subclass of univalent function with negative coefficients defined by Hadamard product[J]. European Journal of Scientific Research, 2014,119(3): 462-472.
[12]	陈杨，吴海燕，季仲梅 . 基于图刚性的无线传感器网络定位新算法[J]. 计算机应用研究， 2015,32(5):1467-1470. CHEN Y , WU H Y , JI Z M . New node localization algorithm for wireless sensor networks based on graph rigidity[J]. Application Research of Computers, 2015,32(5):1467-1470.
[13]	钟丽鸿，胡成全，金京姬 . 基于RSSI极大似然估计定位算法的分析与实现[J]. 吉林大学学报：理学版， 2014,22(3):1083-1089. ZHONG L H , HU C Q , JIN J J . Analysis and implementation of maximum likelihood estimation positioning algorithm based on RSSI[J]. Journal of Jilin University（Science Edition）, 2014,22(3):1083-1089.
[14]	ZHONG L , CUI Y , HE L , et al. Error analysis for node localization using maximum likelihood estimation method in wireless sensor networks[J]. Energy Procedia, 2011,13(5): 994-1001.
[15]	丁海强，齐光快，庄华亮，等. 基于负约束条件下最大似然估计的无线传感网络定位算法[J]. 传感技术学报， 2014,27(11):1545-1550. DING H Q , QI G K , ZHANG H L , et al. Localization in WSN based on maximum likelihood estimation with negative constraint[J]. Chinese Journal of Sensors and Actuators, 2014,27(11):1545-1550.

操作		RMSE
操作	MDS-MAP	MDS-MAP（P）	dwMDS	DRT-MDS
精化之前	1.87	1.58	1.37	1.26
精化之后	1.05	0.79	0.55	0.41
提高百分比	44%	50%	60%	67%