Call for Paper - Upcoming Issues

Investigating the Impact of Kalman Filter to Minimize the Localization Error in Wireless Sensor Networks

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 9
Year of Publication : 2024
Authors : Srivani Reddy, A. Kamala Kumari, Ch. Sita Kumari
10.14445/23488379/IJEEE-V11I9P125
pdf
How to Cite?

Srivani Reddy, A. Kamala Kumari, Ch. Sita Kumari, "Investigating the Impact of Kalman Filter to Minimize the Localization Error in Wireless Sensor Networks," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 9, pp. 274-283, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I9P125

Abstract:

Wireless Sensor Networks (WSN) comprise sensors that are spatially distributed, collecting information and reporting the data back to the control module. Knowing the sensor (node) positions is necessary for most applications, and the sensor positions are estimated using Localization algorithms. This paper proposes a strategy that uses the Kalman Filter (KF) approach with the proposed range free centroid localization algorithm to increase the accuracy of any unknown node's predicted position in a network. The parameters, namely nodes, network size, communication range, and deployment of the network, have been varied, and the performance of the proposed system comprising the range free centroid localization algorithm with Kalman filter is studied. Combining the Kalman filter with the proposed range free centroid localization algorithm enhances the methodology and increases the success rate in the presence of measurement errors. The simulation results demonstrate that the suggested technique enhances the location accuracy of unknown nodes.

Keywords:

Centroid, Kalman filter, Localization, Range-free, Wireless Sensor Networks.

References:

[1] Mohammed Sulaiman BenSaleh et al., “Wireless Sensor Network Design Methodologies: A Survey,” Journal of Sensors, vol. 2020, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] C. Balasubramanian, M. Maragatharajan, and S.P. Balakannan, “A Range Based and Range Free Localization in Wireless Sensor Network,” International Journal of Recent Technology and Engineering, vol. 8, no. 4S2, pp. 699-705, 2019.
[CrossRef] [Publisher Link]
[3] Shivakumar Kagi, and Basavaraj S. Mathapati, “Localization in Wireless Sensor Networks: A Compact Review on State-of-the-Art models,” 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, pp. 5-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] S. Sivasakthiselvan, and V. Nagarajan, “Localization Techniques of Wireless Sensor Networks: A Review,” International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 1643-1648, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Nabil Ali Alrajeh, Maryam Bashir, and Bilal Shams, “Localization Techniques in Wireless Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 9, no. 6, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Fatiha Mekelleche, and Hafid Haffaf, “Classification and Comparison of Range-Based Localization Techniques in Wireless Sensor Networks,” Journal of Communications, vol. 12, no. 4, pp. 221-227, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Santar Pal Singh, and S.C. Sharma, “Range Free Localization Techniques in Wireless Sensor Networks: A Review,” Procedia Computer Science, vol. 57, pp. 7-16, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. Khadim, M. Erritali, and A. Maaden, “Range-Free Localization Schemes for Wireless Sensor Networks,” TELKOMNIKA Indonesian Journal of Electrical Engineering, vol. 16, no. 2, pp. 323-332, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[9] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-Less Low-Cost Outdoor Localization for Very Small Devices”, IEEE Personal Communications, vol. 7, no. 5, pp. 28-34, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Linqing Gui et al., “An Adaptive Range-Free Localization Protocol in Wireless Sensor Networks,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 15, no. 1-3, pp. 38-56, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jun Tian et al., “A Centroid Localization Algorithm for Wireless Sensor Networks Based on Finite Element Method,” IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, pp. 139-143, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yunfei Suo et al., “A Triangular Centroid Location Method Based on Kalman Filter,” Communications, Signal Processing, and Systems, pp 448-458, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jyoti Kumari, Prabhat Kumar, and Sunil Kumar Singh, “Localization in Three Dimensional Wireless Sensor Networks: A Survey,” The Journal of Supercomputing, vol. 75, pp. 5040-5083, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Wint Yi Poe, and Jens B. Schmitt, “Node Deployment in Large Wireless Sensor Networks: Coverage, Energy Consumption, and Worst-Case Delay,” AINTEC '09: Proceedings of the 4th Asian Internet Engineering Conference, pp. 77-84, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Gayatri Devi, Rajeeb Sankar Bal, and Sasmita Manjari Nayak, “Node Deployment and Coverage in Wireless Sensor Network,” International Journal of Innovative Research in Advanced Engineering, vol. 2, no. 1, pp. 139-145, 2015.
[Google Scholar] [Publisher Link]
[16] Wenyan Liu et al., “Node Localization Algorithm for Wireless Sensor Networks Based on Static Anchor Node Location Selection Strategy,” Computer Communications, vol. 192, pp. 289-298, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hakan Koyuncu, and Baki Koyuncu, “An Application of Kalman Filtering and Artificial Neural Network with K-NN Position Detection Technique,” Wireless Sensor Network, vol. 9, no. 8, pp. 239-249, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ashwin Yadav et al., “A Constant Gain Kalman Filter Approach to Target Tracking in Wireless Sensor Networks,” IEEE 7th International Conference on Industrial and Information Systems (ICIIS), Chennai, India, pp. 1-7, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Katerina Fronckova, and Pavel Prazak, “Possibilities of Using Kalman Filters in Indoor Localization,” Mathematics, vol. 8, no. 9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Israr Ullah et al., “Ann Based Learning to Kalman Filter Algorithm for Indoor Environment Prediction in Smart Greenhouse”, IEEE Access, vol. 8, pp. 159371-159388, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Etikala Raja Vikram Reddy, and Sushil Thale, “A Novel Efficient Dual-Gate Mixed Dilated Convolution Network for Multi-Scale Pedestrian Detection,” Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 11973-11979, 2023. [CrossRef] [Google Scholar] [Publisher Link]
[22] Grigore Cican et al., “Design, Manufacturing, and Testing Process of a Lab Scale Test Bench Hybrid Rocket Engine,” Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12039-12046, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Shraddha Panbude et al., “DFPC: Dynamic Fuzzy-based Primary User Aware Clustering for Cognitive Radio Wireless Sensor Networks,” Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12058-12067, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Bonu SatishKumar, “Soft Computing Approach to Enhance the Performance of AODV (Ad-Hoc on-Demand Distance Vector) Routing Protocol Using Active Route Time Out (ART) Parameter in MANETs,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 3060-3068, 2021.
[Google Scholar] [Publisher Link]
[25] Bonu Satish Kumar et al., “Performance Analysis of Prophet Routing Protocol in Delay Tolerant Network by Using Machine Learning Models,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Yan Wang, Junhui Wan, and Jie Lai, “A Wireless Sensor Networks Positioning Method in NLOS Environment Based on TOA and Parallel Kalman Filter,” IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, pp. 446-450, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Yue Qi et al., “An Improved Kalman Filter for TOA Localization Using Maximum Correntropy Criterion,” 28th Wireless and Optical Communications Conference (WOCC), Beijing, China, pp. 1-4, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Abdelhady Naguib, “Localization in Mobile Sensor Networks Using Kalman Filter,” Indian Journal of Science and Technology, vol. 15, no. 13, pp. 570-583, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Salaheldin Edam et al., “Comparative Evaluation of Localization Range-Free Algorithms in Wireless Sensor Networks,” International Journal of Engineering, Science and Technology, vol. 16, no. 2, pp. 1-10, 2024.
[CrossRef] [Google Scholar] [Publisher Link]