Sensor Localization Using Fixed and Dynamic Communication Ranges in Different Types of Distributed Sensor Networks

Abstract

Abstract: Wireless Sensor Network (WSN) refers to a group of locationally dispensed and dedicated sensors for observing and recording the physical conditions of the environment and coordinating the aggregated data at a centrical location. To serve such new applications, localization is largely used in WSNs to define the current location of the sensor nodes. Time of Arrival (ToA) localization is one of the prevalent schemes due to its high estimation accuracy. ToA is a method to estimate the location of a target based on the correlation of the signals and calculating the distances from each anchor to the target by multiplying the speed of light and the time at which the signal is received. In our recent study, we propose Modified 3N algorithm in 2D space. In the Modified 3N algorithm in 2D, three circles were used to localize the target nodes in the network. In this paper; Uniform, Beta, Weibull, Gamma and Generalized Pareto distributed networks are used for localization with the Modified 3N algorithm in 2D and the localization performance of the networks are evaluated and compared using MATLAB simulations. For these simulations, firstly, constant communication range of 10% of the field dimension is used and then dynamic communication ranges that depend on the number of total nodes are used for the same areas.

Keywords

• Wireless Sensor Networks,Localization,Time of Arrival,Statistical Distributions

References

1. Romer K., Mattern F. The design space of wireless sensor networks, IEEE Wirel. Commun., Vol. 11, Issue 6, 2004, pp. 54–61.
2. Kuriakose J., Joshi S and George V.I. Localization in Wireless Sensor Networks: A Survey, CSIR Sponsored X Control Instrumentation System Conference (CISCON-2013), pp.73-75, 2013, India, Tamilnadu
3. Jamalabdollahi M., Zekavat S. A. R. Joint Neighbor Discovery and Time of Arrival Estimation in Wireless Sensor Networks via OFDMA, IEEE Sensors Journal, Vol. 15, Number 10, 2015, pp. 5821-5833.
4. 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, 22-24 July 2004, Canada, Vancouver, British Columbia.
5. Shen H., Ding Z., Dasgupta S., Zhao C. Multiple Source Localization in Wireless Sensor Networks Based on Time of Arrival Measurmement, IEEE Transactions on Signal Processing, Vol. 62, Issue 8, 2014, pp. 1938-1949.
6. Mogi T., Ohtsuki T. TOA Localization using RSS Weight with Path Loss exponents Estimation in NLOS Environments, Proceedings of 14th Asia Pasific Conference (APCC2008), 14-16 October 2008, Japan, Tokyo.
7. Kamyabpour N., Hoang D. B. 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, 14-16 December 2012, China, Beijing.
8. Rasool I., Kemp A. H. Statistical analysis of wireless sensor network Gaussian range estimation errors, IET Wireless Sensor Systems, Vol. 3, Issue 1, 2013, pp. 57–68.

9. Aldalahmeh S., Ghogho M. Statistical Analysis of Optimal Distributed Detection Fusion Rule in Wireless Sensor Networks, Wireless Advanced (WiAd) Published by IEEE, 25-27 June 2012, United Kingdom, London.
10. Hong S. T., Chang J. W. 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, 2-4 September 2011, Canada, Banff.
11. Zhao Z., Wei B., Dong X., Yao L., Gao F. Detecting Wormhole Attacks in Wireless Sensor Networks with Statistical Analysis, WASE International Conference on Information Engineering, 14-15 August 2010, China, Beidaihe.
12. R. P. Enciso R. P., Gallo A., Rosas D. R., Vidal E. F., Rabaga C. P. A simple method to achieve a uniform flux distribution in a multi-faceted point focus generator, ELSEVIER, Renewable Energy Journal, Vol. 93, 2016, pp. 115-124
13. Hossain Md. K., Kamil A. A., Mustafa A. , Baten Md. A. Estimating DEA Efficiency Using Uniform Distribution, Bulletin of the Malaysian Mathematical Sciences Society, Vol. 37 Number 4, 2014, pp. 1075-1083.
14. Forbes C., Evans M., Hastings N., Peacock B. Statistical Distributions, John Wiley&Sons, Inc., Publication, fourth Edition, New Jersey ,2011.
15. Park S. Y., Lee J. J., Stochastic Opposition-Based Learning Using a Beta Distribution in Differential Evolution, IEEE Transactions On Cybernetics, Vol. 46, Number 10, October 2016, pp.2184-2194.
16. Walpole R. E., Myers R. H., Myers S. L., Ye K. Probability & Statistics for Engineers & Scientists, Ninth Edition, Prentice Hall, Boston, USA,2012.
17. He B., Cui W., Du X. An additive modified Weibull distribution, ELSEVIER Reliability Enginnering & System Safety Journal, Vol.145, 2016, pp. 28-37.
18. Mohammadi K., Alavi O., Mostafaeipour A., Goudarzi N., Jalilvand M. Assessing different parameters estimation methods of Weibull distribution to compute wind power density, ELSEVIER Energy Conversion and Management Journal, Vol.108, 2016, pp. 322-335.
19. Usta I. An innovative estimation method regarding Weibull parameters for wind energy applications, ELSEVIER Energy Journal, Vol. 106, 2016, pp. 301-314.
20. Clarke B. R., McKinnon P. L., Riley G. A fast robust method for fitting gamma distributions, Springer Regular Article, , Vol. 53, Issue 4, Nov 2012, pp. 1001-1014.
21. Tsai H. M., Viriyasitavat W., Tonguz O. K., Saraydar C., Talty T., Macdonald A. Feasibility of In-car Wireless Sensor Networks: A Statistical Evaluation, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, 18-21 June 2007, USA, California, San-Diego.