Localization of wireless sensor networks with a mobile beacon

被引：10

作者：

Xu, Jiang ^{[1
,2
]}

Qian, Huan-Yan ^{[1
]}

机构：

[1] School of Computer Science and Engineering, Nanjing University of Science and Technolog, Nanjing

[2] School of Computer Science and Engineer, Changshu Institute of Science and Technology, Changshu

来源：

Information Technology Journal | 2013年 / 12卷 / 11期

关键词：

Localization; Mobile beacon; SVM; WSNs;

D O I：

10.3923/itj.2013.2251.2255

中图分类号：

学科分类号：

摘要：

A vast majority of localization techniques proposed for sensor networks are based on triangulation methods in Euclidean geometry. This study proposes a novel approach known as MSVM that utilizes a mobile beacon to implement location estimation on wireless sensor networks. MSVM is based on Support Vector Machine(SVM). In the approach, the problem of localization is converted into classification, which takes advantage of the signal information generated by mobile beacon between virtual beacon nodes and unknown nodes to compute the location. In this way, expensive beacon nodes with GPS function can be avoided, saving investment on wireless sensor nodes. Shown by the simulation test, the approach has quite high localization precision. © 2013 Asian Network for Scientific Information.

引用

页码：2251 / 2255

页数：4

共 12 条

[1]

Brunato M., Battiti R., Statistical learning theory for location fingerprinting in wireless LANs, Computer Networks, 47, 6, pp. 825-845, (2005)

[2]

Bulusu N., Heidemann J., Estrin D., GPS-less low-cost outdoor localization for very small devices, IEEE Personal Communications, 7, 5, pp. 28-34, (2000)

[3]

Cortes C., Vapnik V., Support-vector networks, Mach. Learn., 20, pp. 273-297, (1995)

[4]

He T., Huang C., Blum B.M., Stankovic J.A., Abdelzaher T., Range-free localization schemes in large scale sensor networks, Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, pp. 81-95, (2003)

[5]

Nguyen X., Jordan M.I., Sinopoli B., A kernel-based learning approach to ad hoc sensor network localization, ACM Trans. Sensor Networks, 1, pp. 134-152, (2005)

[6]

Niculescu D., Nath B., Ad-hoc positioning system, Proc. Conf. IEEE Global Telecommunicat, 5, pp. 2926-2931, (2001)

[7]

Niculescu D., Nath B., Ad hoc positioning system (APS) using AOA, Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1734-1743, (2003)

[8]

Pan J.J., Kwok J.T., Yang Q., Chen Y., Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing, IEEE Transactions on Knowledge and Data Engineering, 18, 9, pp. 1181-1193, (2006)

[9]

Priyantha N.B., The Cricket Indoor Location System, (2005)

[10]

Smola A.J., Scholkopf B., Learning with Kernels-Support Vector Machines, Regularization, Optimization and Beyond, (2002)

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