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Performance Analysis of Wireless Sensor Network Localization algorithms


Affiliations
1 Department of Computer Science, Brahijaya University, Indonesia
2 Department of Computer Science, Sri lanka Institute of Technology, Sri Lanka
 

In recent years, the use of Wireless Sensor Networks (WSNs) has been increasing. Like WSNs protocols development, localization is the main issue and needed to address several areas including 3D and mobile anchor-based localization algorithms. WSNs are being used widely in different fields like environment, disaster relief, target tracking and several other applications like the SCADA system for the high voltage of electricity. In many applications, node localization is very important in which the system needed to know the exact location of the event. This paper addresses the main ideas and techniques published in recent years. We highlight the error performance analysis between the algorithms stated static 3D localization.

Keywords

Wireless Sensor Network, Localization, RSSI, GPS, Ad-Hoc Network, Centroid, Distributed, Coordinate System.
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  • Ukani, V., Thakkar, P., & Parikh, V. (2019). A Range-Based Adaptive and Collaborative Localization for Wireless Sensor Networks. In Information and Communication Technology for Intelligent Systems (pp. 293-302). Springer, Singapore.

  • Ahmad, T., Li, X. J., & Seet, B. C. (2016, May). 3D localization based on parametric loop division and subdivision surfaces for wireless sensor networks. In Wireless and Optical Communication Conference (WOCC), 2016 25th (pp. 1-6).

  • Yan, J., Qiao, R., Tang, L., Zheng, C., & Fan, B. (2019). A fuzzy decision-based WSN localization algorithm for wise healthcare. China Communications, 16(4), 208-218.

  • Supate, K., Bhosale, S., & Student, M. E. (2019). RSSI Based Indoor Localization of WSN Sensor Nodes. International Journal of Engineering Science, 21479.

  • L. Girod, D. Estrin, Robust range estimation using acoustic and multimodal sensing, in Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Expanding the Societal Role of Robotics in the Next Millennium (Cat. No.01CH37180), vol. 3, pp. 1312–1320.

  • X. Cheng, A. Thaeler, TPS: A time-based positioning scheme for outdoor wireless sensor networks, in the 23rd IEEE International Conference on Computer Communications, 2004, INFOCOM 2004, vol. 00(C), pp. 2685–2696.

  • T.S. Rappapport, Wireless Communications: Principles and Practice, seconded. Prentice Hall, New Jersey, 1996. pp. 50–143

  • D. Moore, J. Leonard, D. Rus, S. Teller, Robust distributed network localization with noisy range measurements, in Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys '04), Baltimore, MD. November 3–5, 2004

  • L. Doherty, K. Pister, and L. Ghaoui, “Convex Position Estimation in Wireless Sensor Networks,” Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2001), Apr 22-26, 2001.

  • N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less low-cost outdoor localization for very small devices”, IEEE Personal Communications Magazine, 7(5):28–34, October 2000.

  • Wang, Feng, Lei Shi, Weiguo Fan, and Cong Wang. "Application of computational geometry in coal mine roadway 3D localization." Int. Arab J. Inf. Technol. 15, no. 4 (2018): 668-674.

  • Parras, J., Zazo, S., Pérez-Álvarez, I. A., & Sanz González, J. L. (2019). Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN. Sensors, 19(16), 3530.

  • Kouroshnezhad, S., Peiravi, A., Haghighi, M. S., & Zhang, Q. (2019). A mixed-integer linear programming approach for energy-constrained mobile anchor path planning in wireless sensor networks localization. Ad Hoc Networks, 87, 188-199.

  • Kouroshnezhad, S., Peiravi, A., Haghighi, M. S., & Zhang, Q. (2019). A mixed-integer linear programming approach for energy-constrained mobile anchor path planning in wireless sensor networks localization. Ad Hoc Networks, 87, 188-199.

  • Zheng, J., Liscano, R., Aghababaei, S., Chepurna, I., & Eklund, M. J. (2019). Improving Localization in Sensor Networks by Leveraging Inter-node Ranking of Received Signal Strength and Clustering. Adhoc & Sensor Wireless Networks, 44.

  • Parulpreet, S., Arun, K., Anil, K., & Mamta, K. (2019). Computational Intelligence Techniques for Localization in Static and Dynamic Wireless Sensor Networks—A Review. In Computational Intelligence in Sensor Networks (pp. 25-54). Springer, Berlin, Heidelberg.

  • Wen-Chen Hu, Naima Kaabouch, Sara Faraji Jalal Apostal, Hung-Jen Yang,” Location-Aware Mining for Privacy-Preserving Location-Based Advertising”, IEEE 2017, pp. 569-574.

  • Kaur, A., Gupta, G. P., & Mittal, S. (2020). Impact of Nature-Inspired Algorithms on Localization Algorithms in Wireless Sensor Networks. In Nature-Inspired Computing Applications in Advanced Communication Networks (pp. 1-18). IGI Global.

  • Kovner, R., Souaiaia, T., Fox, A., Roseboom, P., French, D., Oler, J., ... & Kalin, N. (2019). 208. Transcriptional Profiling of Primate Central Nucleus of the Amygdala Neurons: A Role for PKCd Neurons in Early Life Anxious Temperament. Biological Psychiatry, 85(10), S86.

  • A Decentralized Positioning Method for Wireless Sensor Networks Based on Weighted Interpolation C. -L. Wang; Y. -W. Hong; Y. -S. Dai 2007 IEEE International Conference on Communications Year: 2007 Pages: 3167 – 3172.

  • Giovanni Zanca, Francesco Zorzi, Andrea Zanella, and Michele Zorzi,(2008)Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks, Department of Information Engineering University of Padova, Italy

  • Shi, P., Li, G., Yuan, Y., & Kuang, L. (2019). Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks. Sensors, 19(21), 4712.

  • Ahmad, T., Li, X. J., & Seet, B. C. (2019). Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering. Journal of Sensor and Actuator Networks, 8(2), 24.

  • Ahmad, T., Li, X., & Seet, B. C. (2017). Parametric loop division for 3d localization in wireless sensor networks. Sensors, 17(7), 1697.

  • Mohsen Jamalabdollahi & Seyed A. Reza Zekavat. (2015).Joint Neighbor Discovery and Time of Arrival Estimation in Wireless Sensor Networks via OFDMA, IEEE Sensors Journal (Volume:15, Issue: 10 ).pp:5821 – 5833.

  • Wang, L., Zhang, J., & Cao, D. (2012). A new 3-dimensional DV-hop localization algorithm. Journal of Computational Information Systems, 8(6), 2463-2475.

  • Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Hichem Snoussi, Joumana Farah, "Kernel-based localization using fingerprinting in wireless sensor networks" Workshop on signal processing Advances in wireless communications,2013, pp.744-748.

  • Wasim Q. Malika, Ben Allen,” Wireless Sensor positioning with Ultrawideband fingerprinting”.

  • Wipassorn Vinicchayakul, Sathaporn Promwong, and Pichaya Supanakoon," Study of UWB Indoor Localization Using Fingerprinting Technique with Different Number of Antennas", IEEE 2016.

  • Sandy Mahfouz, FarahMourad-Chehade, Paul Honeine, Joumana Farah, HichemSnoussi,” decentralized localization using fingerprinting and kernel methods in sensor networks”, EUSIPCO 2013, pp.1-5.

  • Przemyslaw Woznica, Lukasz Kulas,” Influence of a Radio Frequency on RF Fingerprinting Accuracy Based on Ray Tracing Simulation", EuroCon 2013, pp.202-206.

  • Ahmad, T., Li, X. J., & Seet, B. C. (2016, June). A self-calibrated centroid localization algorithm for indoor ZigBee WSNs. In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) (pp. 455-461).

  • Ahmad, Tanveer, Xue Jun Li, and Boon-Chong Seet. "3D Localization Using Social Network Analysis for Wireless Sensor Networks." 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS). IEEE, 2018.


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  • Performance Analysis of Wireless Sensor Network Localization algorithms

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Authors

Putri Kevin
Department of Computer Science, Brahijaya University, Indonesia
Dian viely
Department of Computer Science, Brahijaya University, Indonesia
Udeni T. Samarakoon
Department of Computer Science, Sri lanka Institute of Technology, Sri Lanka

Abstract


In recent years, the use of Wireless Sensor Networks (WSNs) has been increasing. Like WSNs protocols development, localization is the main issue and needed to address several areas including 3D and mobile anchor-based localization algorithms. WSNs are being used widely in different fields like environment, disaster relief, target tracking and several other applications like the SCADA system for the high voltage of electricity. In many applications, node localization is very important in which the system needed to know the exact location of the event. This paper addresses the main ideas and techniques published in recent years. We highlight the error performance analysis between the algorithms stated static 3D localization.

Keywords


Wireless Sensor Network, Localization, RSSI, GPS, Ad-Hoc Network, Centroid, Distributed, Coordinate System.

References





DOI: https://doi.org/10.22247/ijcna%2F2019%2F189009