ANFIS-based Indoor localization and Tracking in Wireless Sensor Networking
Keywords:
WSN, ANFIS, RSSI, UKF, AccuracyAbstract
Localizing wireless sensor networks poses a persistent challenge in accurately determining sensor node locations based on known anchor node positions, especially when nodes move between different locations. Conventional techniques like Trilateration, relying on Received Signal Strength Indicators (RSSIs), frequently employed in Wireless Sensor Networks (WSNs), serve the purpose of localizing and tracking moving targets. However, the inherent nonlinear relationship between RSSI and distance often leads to substantial errors in localization estimations. This paper introduces an innovative approach by proposing the utilization of an Adaptive Neural Fuzzy Inference System (ANFIS) as a departure from the conventional RSSI-based method. This ANFIS-based approach aims to initially estimate the locations of single moving targets in a 2-D WSN setup. Subsequently, these initial estimates undergo further refinement within an Unscented Kalman Filter (UKF). The results demonstrate the superior performance of the proposed algorithms in tracking targets, showcasing high accuracy levels within a few centimeters is evident from the mean localization errors for standard RSSI, ANFIS, and ANFIS+UKF, that the ANFIS+UKF framework can handle real-time target tracking issues in WSN utilizing RSSI (5.657, 0.805, and 0.068, respectively). By contrast, the proposed method offers an impressive improvement of 98.797% over the standard RSSI method.
References
Abdou, A. S.; M. A. Aziem and A. Aboshosha. (2016). An efficient indoor localization system based on Affinity Propagation and Support Vector Regression. In 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC). Beirut, Lebanon, 1-7.
Ali, R.; R. Liu; Y. He; A. Nayyar and B. Qureshi. (2021). Systematic review of dynamic multi-object identification and localization: Techniques and technologies. IEEE Access, 9: 122924-122950.
Alrajeh, N. A.; M. Bashir and B. Shams. (2013). Localization techniques in wireless sensor networks. International journal of distributed sensor networks, 9(6): 304628.
Bose, A. and Foh, C. H. (2007). A practical path loss model for indoor WiFi positioning enhancement. In 2007 6th International Conference on Information, Communications & Signal Processing. Singapore: 1-5.
Burhanuddin, M. A.; A. A. J. Mohammed; R. Ismail; M. E. Hameed; A.N. Kareem and H. Basiron. (2018). A review on security challenges and features in wireless sensor networks: IoT perspective. Journal of Telecommunication, Electronic and Computer Engineering, 10(1-7): 17-21.
Din, M. M.; N. Jamil; J. Maniam; and M. A. Mohamed. (2018). Indoor positioning: technology comparison analysis. International Journal of Engineering & Technology,7(2.14): 133-137.
Ge H.; F. Jiang; and Z. Zhang. (2019). A hybrid localization algorithm of rss and toa based on an ensembled neural network. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference. Chongqing, China ,1280-1284.
Gharghan S. K.; R. Nordin and M. Ismail. (2016). A wireless sensor network with soft computing localization techniques for track cycling applications. Sensors, 16(8): 1043.
Gharghan, S. K.; R. Nordin; A. M. Jawad; H. M. Jawad and M. Ismail. (2018). Adaptive neural fuzzy inference system for accurate localization of wireless sensor network in outdoor and indoor cycling applications. IEEE Access, 6: 38475-38489.
Ingabire, W.; H. Larijani; R. M. Gibson and A. U. H. Qureshi. (2021). Outdoor node localization using random neural networks for large-scale urban iot lora networks. Algorithms, 14(11): 307.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3): 665-685.
Jondhale, S. R.; A. S. Jondhale; P. S. Deshpande and J. Lloret. (2021). Improved trilateration for indoor localization: Neural network and centroid-based approach. International Journal of Distributed Sensor Networks, 17(11): 15501477211053997.
Khelifi, F.; A. Bradai; A. Benslimane; P. Rawat and M. Atri. (2019). A survey of localization systems in internet of things. Mobile Networks and Applications, 24(3): 761-785.
Le, A. T.; L. C. Tran; X. Huang; C. Ritz; E. Dutkiewicz; S. L. Phung and D. Franklin. (2020). Unbalanced hybrid AOA/RSSI localization for simplified wireless sensor networks. Sensors, 20(14): 3838.
Mohammed, S. L. (2016). Distance estimation based on RSSI and Log-Normal shadowing models for ZigBee wireless sensor network. Engineering and Technology Journal, 34(15): 2950-2959.
Molla, J. P.; D. Dhabliya; S. R. Jondhale; Arumugam, S; A. S. Rajawat; S. B. Goyal and G. S. R. Suciu (2023). Energy efficient received signal strength-based target localization and tracking using support vector regression. Energies, 16(1): 555.
Naseem, Z.; I. Nausheen and Z. Mirza. (2018). Propagation models for wireless communication system. signal, 5(01):237-242.
Oussalah, M.; M. Alakhras and M. I. Hussein. (2015). Multivariable fuzzy inference system for fingerprinting indoor localization. Fuzzy Sets and Systems, 269: 65-89.
Palipana, S.; C. Kapukotuwe; U. Malasinghe; P. Wijenayaka and S. R. Munasinghe. (2012). Localization of a mobile robot using ZigBee based optimization techniques. In 2012 IEEE 6th International Conference on Information and Automation for Sustainability. Beijing, China, 215-220.
Piras, M. and Cina, A. (2010). Indoor positioning using low cost GPS receivers: Tests and statistical analyses. In 2010 International Conference on Indoor Positioning and Indoor Navigation. Zurich, Switzerland, 1-7.
Shalaby, M.; M. Shokair; and N.W. Messiha. (2017). Performance enhancement of TOA localized wireless sensor networks. Wireless Personal Communications, 95: 4667-4679.
Tariq, S. M.; and Al-Mejibli, I. S. (2023). WSN Localization Method Based on Hybrid PSO-GRNN Approach. International Journal of Intelligent Engineering & Systems, 16(5):717-727
Wojcicki, P.; T. Zientarski,; M. Charytanowicz and E. Lukasik. (2021). Estimation of the path-loss exponent by bayesian filtering method. Sensors, 21(6): 1934.
Xu, S.; Z. Wang; H. Zhang and S. S. Ge. (2019). Improved Indoor Localization Based on Received Signal Strength Indicator and General Regression Neural Network. Sensors & Materials, 31(6): 2043–2060.
Zheng, J.; Y. Liu; X. Fan and F. Li. (2016). The study of RSSI in wireless sensor networks. In 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering. 207-209. Atlantis Press.
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