Two Dimensional Sensor Localization Using mN/2 Algorithm in Different Types of Distributed Fields

Serap Karagol, Dogan Yildiz

Abstract


Wireless Sensor Network (WSN) refers to a group of locationally dispensed and dedicated sensors that observe and record physical and environmental conditions and coordinate the aggregated data at a centrical location. To serve new applications, localization is largely used in WSNs to define the current location of the sensor nodes. In this paper, first, the proposed mN/2 algorithms performance compared with GPS, 3N, 3/2N and 3/2N(2) algorithms. The mN/2 algorithm is especially effective in very sparse networks where other algorithms usually fail. Even when the algorithm cannot locate a given node, it produces a polygonal estimate of the region in which the node is located. Monte Carlo simulations show that this algorithm performs better than other algorithms. Secondly, Uniform, Beta, Weibull, Gamma and Generalized Pareto distributed networks were used for localization using the mN/2 algorithm. The localization performance of the networks are evaluated and compared using MATLAB simulations.


Full Text:

PDF

References


K. Sohraby, D. Minoli, T. Znati, “Wireless Sensor Networks:

Technology, Protocols, and Applications,” Wiley-Interscience, 1st edition, 2007, pp. 1-38.

Michahelles, F., Matter, P., Schmidt, A., Schiele, B., ''Appliying wearable sensors to avalanche rescue'', Computers & Graphics, vol. 27, issue 6, pp. 839-847, December 2003.

Baldus, H., Klabunde, K., and Muesch, G., "Reliable Set-Up of Medical Body-Sensor Networks", in Proc. European workshop on wireless sensor networks EWSN 2004, 2004, vol. 2920, pp. 353-363.

Sung, M., Hubaux, Pentland, A., ''LiveNet: Health and Lifestyle Networking Through Distributed Mobile Devices'', in Proc. Mobisys 2004 Workshop on Applications of Mobile Embedded Systems, 2004, pp. 15-17.

29 Palms Fixed/Mobile Experiment, Tracking vehicles with a UAV-delivered sensor network, Available: http://robotics.eecs.berkeley.edu/~pister/29Palms0103/.

Pandey, S., Prasad, P., Sinha, P., Agrawal, P., "Localization of sensor networks considering energy accuracy tradeoffs," Collaborative Computing: Networking, Applications and Worksharing, 2005 International Conference on , 19-21 December 2005.

Kai, W., Chun C., "Using RSS with difference method in localization algorithm for sensor networks," Information Science and Engineering (ICISE), 2010 2nd International Conference on , 4-6 December 2010, pp. 2500-2502.

E. W. Dijkstra, "A Note on Two Problems Connexion with Graphs", Numerische Mathematik, vol. 1, pp. 269 - 271, 1959.

Liu, K., Wang, S., Zhang F., Hu, F., Xu, C., "Efficient Localized Localization Algorithm for Wireless Sensor Networks," Computer and Information Technology, 2005. CIT 2005. The Fifth International Conference on, 21-23 September 2005, pp.517-523.

Nussbaum, R., Esfahanian, A-H, Tan P.-N., "Clustering Social Networks Using Distance-Preserving Subgraphs," Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on , 9-11 August 2010, pp. 380-385.

Vahidnia, A, Ledwich, G., Ghosh, A., Palmer, E., "An improved genetic algorithm and graph theory based method for optimal sectionalizer switch placement in distribution networks with DG", Universities Power Engineering Conference (AUPEC), 2011 21st Australasian, 25-28 September 2011, pp.1-7.

Y. Zheng, D. J. Brady, and P. K. Agarwal, "Localization using boundary sensors: An analysisbased on graph theory", ACM Transactions on Sensor Networks (TOSN), vol. 3, no. 4,Oct. 2007, pp. 12:1-21:19.

Zhu, Y., Fu, J., "A node robust enhancing algorithm based on graph theory," Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on , vol.7, 16-18 October 2010, pp.2820-2823.

Nakayama, K., Shinomiya, N., "Distributed control based on tie-set graph theory for smart grid networks," Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2010 International Congress on , 18-20 October 2010, pp. 957- 964.

Najmeh Kamyabpour, Doan B.Hoang, ’Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)’, IEEE 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 20-23, 2012.

Imtiaz Rasool, Andrew H. Kemp, ‘Statistical analysis of wireless sensor network Gaussian range estimation errors’, IET Wireless Sensor Systems, Vol. 3, Iss. 1, pp. 57–68, 2013.

S. Aldalahmeh, Mounir Ghogho, ‘Statistical Analysis of Optimal Distributed Detection Fusion Rule in Wireless Sensor Networks’, Wireless Advanced (WiAd), pp. 49-53, 2012.

Seung Tae Hong, Jae Woo Chang, ‘A New Data Filtering Scheme Based on Statistical Data Analysis for Monitoring Systems in Wireless Sensor Networks’, IEEE International Conference on High Performance Computing and Communications, pp. 636-640, 2011.

Zhibin Zhao, Bo Wei, Xiaomei Dong, Lan Yao, Fuxiang Gao, ‘Detecting Wormhole Attacks in Wireless Sensor Networks with Statistical Analysis’, WASE International Conference on Information Engineering, pp. 251-254, 2010.

Hsin-Mu Tsai, Wantanee Viriyasitavat, Ozan K. Tonguz, Cem Saraydar, Timothy Talty, Andrew Macdonald, ‘Feasibility of In-car Wireless Sensor Networks: A Statistical Evaluation’, IEEE SECON 2007, pp. 101-111, 2007.

Barbeau, M., Kranakis, E., Krizanc D., Morin P., ''Improving Distance Based Geographic Location Techniques in Sensor Networks'', 3rd International Conference on AD-HOC Networks & Wireless, July 22-24 2004.

Md. Kamrul Hossain, Anton Abdulbasah Kamil, Adli Mustafa And Md. Azizul Baten ‘Estimating DEA Efficiency Using Uniform Distribution’, Malaysian Mathematical Sciences Society, vol.37(4), 2014, pp.1075-1083.

Sourav Chakraborty, Akshay Kamath, Rameshwar Pratap, ‘Testing whether the uniform distribution is a stationary distribution’, ELSEVIER, Information Processing Letters, vol. 116, 2016, pp. 475-480.

Catherine Forbes, Merran Evans, Nicholsa Hastings, Brian Peacock, ‘Statistical Distributions’, John Wiley&Sons, Inc., Publication, fourth Edition, 2011, ch. 40.

Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, Probability & Statistics for Engineers & Scientists, Ninth Edition, Prentice Hall, 2012, ch. 6.

So-Youn Park and Ju-Jang Lee, ‘Stochastic Opposition-Based Learning Using a Beta Distribution in Differential Evolution’, IEEE Transactions On Cybernetics, vol. 46, no. 10, pp.2184-2194, October 2016.

Can Ozay, Melih Soner Celiktas, ‘Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region’, ELSEVIER Energy Conversion and Management Journal, vol.121, pp. 49-54, 2016.

Kasra Mohammadi, Omid Alavi, Ali Mostafaeipour, Navid Goudarzi, Mahdi Jalilvand, ‘Assessing different parameters estimation methods of Weibull distribution to compute wind power density’, ELSEVIER Energy Conversion and Management Journal, vol.108, pp. 322-335, 2016.

Ilhan Usta, ‘An innovative estimation method regarding Weibull parameters for wind energy applications’, ELSEVIER Energy Journal, vol. 106, pp. 301-314, 2016.

Brenton R. Clarke, Peter L. McKinnon ,Geoff Riley, ‘A fast robust method for fitting gamma distributions’, Springer Regular Article, Nov 2012, vol. 53 Issue 4, p1001-1014.

P.K. Bhunya, R. Berndtsson, Sharad. K. Jain, Rakesh Kumar, ‘Flood analysis using negative binomial and Generalized Pareto models in partial duration series (PDS)’, ELSEVIER Journal of Hydrology, vol. 497, pp. 121–132, 2013.




DOI: http://dx.doi.org/10.11601/ijates.v6i2.217

Refbacks

  • There are currently no refbacks.