Gravitational Particle Swarm Optimization Localization Algorithm for Wireless Sensor Network Nodes

Abstract:

A range-based localization approach which named gravitational particle swarm optimization localization algorithm (GL) has been proposed. This algorithm considered the influence from the position of anchor nodes to the localization results, GL can directly searched out the coordinates of unknown nodes by the distance from anchor nodes to unknown nodes. As is shown in the experiment results, GL not only has high positioning accuracy, but also overcomes the defect that location error increases rapidly as the ranging error increases, compares with normal schemes (such as method of least squares, ML ) GL’s accuracy can improve 40% as the ranging error is 35%.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 4622-4627
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.4622
• Online since: May 2014
• Authors: Shu Wang Zhou*, Ming Lei Shu, Ming Yang, Ying Long Wang
• Keywords: Localization, Particle Swarm Optimization (PSO), Unknown Node, Wireless Sensor Network (WSN)

References

[1] DING HAIBIN，ZENG PENG。Intelligent wireless sensor networks [M]，BEI JING，science press，(2006).

[2] K. MECHITOV, W. KIM, G . AGHA. High-frequency distributed sensing for structure monitoring[A]. In Trans. of the Society of Instrument and Control Engineers(SICE)[C], 2006. 109–114.

[3] R. SZEWCZYK, A. MAINWARING, J. POLASTRE. An analysis of a large scale habitat monitoring application[J]. In Sensys, (2004).

[4] K. MECHITOV, S. SUNDRESH, Y. KWON. Cooperative tracking with binary-detection sensor networks. In International Conference on Embedded Networked Sensor Systems(Sensys)[C], pages 332–333. ACM Press, (2003).

DOI: 10.1145/958491.958546

[5] M. MAROTI, G. SIMON, A. LEDECZI. Shooter localization in urban terrain[J]. In Computer, pages 60–61. IEEE Computer Society Press, (2004).

DOI: 10.1109/mc.2004.104

[6] MAO GETAL. Wireless Sensor Network Localization Tech-niques[J], Comput[EB/OL]. Netw. (2007), doi: 10. 1016/j. comnet. 2006. 11. 018.

[7] GIROD L. Robust Range Estimation using Acousticand Multimodal Sensing[C]/ Proceedings of IEEE/RSJ Inter-national Conference on Intelligent Robots and Systems(IROS'01), Maui, Hawaii, USA{IEEE Computer Society, 2001, 3: 1312-1320.

DOI: 10.1109/iros.2001.977164

[8] PRIYANTHA N, CHAKRABORTHY A, BA LAKRISHNAN H. The CricketLocation-Support System [C]. Proceedings of InternationalConference on Mobile Computing and Networking, Boston, MA, August 6-11. 2000: 32-43.

[9] KENNEDY J EBERHART R. Particle swarm optimization[A]. Proc IEEE Int Conf on Neural Networks[C]. Perth, 1995. 1942-(1948).

[10] EBERHART R, KENNEDY J. A new optimizer using particleswarm theory[A]. Proc6th Int Symposium on MicroMachine and Human Science[C]. Nagoya, 1995. 39-43.

[11] SHI YUHUI EBERHART R. A modified particle swarm optimizer [A]. Proc IEEE Int Conf on Evolutionary Computation[C]. Anchorage, 1998. 69-73.

[12] EBERHART R, SHI YUHUI. Particle swarm optimization: Developments, applications and resources [A]. Proc IEEE Int Conf on Evolutionary Computation[C]. Seoul, 2001. 81-86.

DOI: 10.1109/cec.2001.934374

[13] KENNEDY J, EBERHART R. Particle swarm optimizatio[A]. Proc IEEE Int Conf on Neural Networks[C]Perth, 1995. 1942-(1948).