The Simulator Research of the Traffic Network Particle Swarm Optimization Based on Wireless Sensor

Abstract:

To solve the problem of optimizing node deployment of wireless sensor network for urban traffic informat ion acquisit ion, a constraint optimization model for wireless sensor network node deployment was proposed. Both the comprehensive evaluation function for connectivity and coverage and the restriction on the practical demands of connectivity and coverage are used. The constraint optimization model is converted to unconstraint one using penalty function. The particle swarm optimization algorithm is used to solve the problem. The dynamically changing weight method is used as an improved algorithm to avert the premature convergence. Sensors inside the Second Ring Road in Beijing are taken as examples in simulation experiments. Experiment results indicate that compared with initial manual deployment the evaluation function value has been increased by 1.71% and 3.18%, respectively after using particle swarm optimization and its improved a lgorithm. The results show that the proposed algorithms have the ability to improve the node deployment of wireless sensor network in urban traffic information acquisition.

• Periodical: Advanced Materials Research (Volumes 926-930)
• Pages: 2638-2641
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.2638
• Online since: May 2014
• Authors: Xing Bin Wang*
• Keywords: Particle Swarm Optimization (PSO), Sensor Node Deployment, Traffic Information Acquisition, Wireless Sensor Network (WSN)

References

[1] Y. Zou and Y.K. Chakrabarty: Proceeding of the 22nd Annual Joint Conference of the IEEE Computer and Communications, Vol. 53 (2003). 2, p.1293.

[2] X. Wang, S. Wang and J. J Ma: Acta Electronica Sinica, Vol. 35 (2007). 11, p. (2038).

[3] F. Lin and P. Chen: Communications Letters, Vol. 9 (2005). 1, p.43.

[4] X. Xu and S. Sahni: IEEE Transactions on Computers, Vol. 56 (2007). 12, p.1681.

[5] B.A. Aziznzba, A.W. Mohemmed and M.Y. Alias: Proceeding of the 2009 IEEE International Conference on Networking, Sensing and Contro, Vol. 31 (2009). 7, p.602.