MARKOV-BASED DEPLOYMENT APPROACH TO IMPROVE WSN COVERAGE

Abd alnasir R. Finjan; Saad T. Hasson

doi:10.25212/lfu.qzj.2.2.37

Authors

Abd alnasir R. Finjan Software Dep., Information Technology College, University of Babylon - Iraq
Saad T. Hasson Software Dep., Information Technology College, University of Babylon – Iraq

DOI:

https://doi.org/10.25212/lfu.qzj.2.2.37

Keywords:

Sensors deployment, Coverage, WSN, Markov process, GSO.

Abstract

A "wireless sensor network (WSN)" represents a gathering of limited number of sensors that are closely deployed in a recognizing area. The efficiency of any WSNs is heavily depending on the coverage delivered by the deployed sensors. In this paper, a developed "deployment approach" is suggested to improve the WSN coverage, connectivity and reliability. This approach is based on the "Markov process". The distances between sensor node and its neighboring sensors are calculated, and then converted to the probabilities that create the transition matrix. Depending on this transition matrix the distance and toward movement for each sensor are estimated in each iteration. The Simulation results were compared with the GSO results. Our results show that this deployment approach can provide high coverage and good reliability.

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References

X. M. Guo, C. J. Zhao, X. T. Yang et al, “A Deterministic Sensor Node Deployment Method with Target Coverage Based on Grid Scan”, Chinese Journal of Sensors and Actuators, Vol.25, No.1, 2012, pp.104-109.

Hasson, Saad Talib, and Abdul Nasir Reyadh. "A Modified Virtual Approach to Deploy Border Line Sensors." Asian Journal of Information Technology 15.16 (2016): 2750- 2755.

Liao, Wen-Hwa, Yucheng Kao, and Ying-Shan Li. "A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks." Expert Systems with Applications 38.10 (2011): 12180-12188.

Y. Yoon and Y. H. Kim, “An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks,” IEEE on Cybernetics, (2013).

Alduraibi Fahad, Noureddine Lasla, and Mohamed Younis. "Coverage-based node placement optimization in wireless sensor network with linear topology." Communications (ICC), 2016 IEEE International Conference on. IEEE, 2016.

Abbasi, Abu Zafar, Noman Islam, and Zubair Ahmed Shaikh. "A review of wireless sensors and networks' applications in agriculture." Computer Standards & Interfaces 36.2 (2014): 263-270.

Noureddine Boudriga; "AWSN-Based system for country boarder surveillance and target tracking", advances in remote sensing, 5, 2016. (http://www.scrip.org/gurnal/ars).

Luo, Qiang, and Zhongming PAN. "An algorithm of deployment in small-scale underwater wireless sensor networks." Chinese Journal of Sensors and Actuators 24.7 (2011): 1043-1047.

Wang, Xue, Sheng Wang, and Jun-jie Ma. "Dynamic sensor deployment strategy based on virtual force-directed particle swarm optimization in wireless sensor networks." Acta Electronica Sinica 35.11 (2007): 2038.

Lin, Frank YS, and Pei-Ling Chiu. "A near-optimal sensor placement algorithm to achieve complete coverage-discrimination in sensor networks." IEEE Communications Letters 9.1 (2005): 43-45.

Yi,L. "Wireless sensor network deployment based on genetic algorithm and simulated annealing algorithm." Computer simulation (2011): 171-174

Boukerche, Azzedine, Xin Fei, and Regina B. Araujo. "WSN04-4: A CoveragePreserving and Hole Tolerant Based Scheme for the Irregular Sensing Range in Wireless Sensor Networks." IEEE Globecom 2006. IEEE, 2006.

David A. Levin, Yuval Peres, and Elizabeth L. Wilmer. Markov chains and mixing times. American Mathematical Society, Providence, RI, 2009. With a chapter by James G. Propp and David B. Wilson.

Ding, Xin, Ziyi Qiu, and Xiaohui Chen. "Sparse transition matrix estimation for highdimensional and locally stationary vector autoregressive models." arXiv preprint arXiv:1604.04002 (2016)