Localization of mobile targets in a wireless sensor network using Diffusion Least Mean Square algorithm based on Huber loss function

Document Type : Original Article

Authors

1 Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

2 Department of Computer Engineering, Salman Institute of Higher Education, Mashhad, Iran.

3 Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Abstract

Localization of mobile targets is one of the important topics in wireless sensor networks. The challenge lies in deploying techniques capable of estimating the subject's location amidst system noise, with minimal deviation from the actual location. In this paper, we propose a robust variant of the Diffusion Least Mean Square algorithm. This version involves distributing the estimation of the target's location across network nodes, facilitated by the pseudo-Huber loss function. Through this method, the accuracy of estimation in localization and tracking the target improves even in the presence of various noise types. The paper formulates target location using two criteria: received signal strength and signal propagation time, based on the proposed algorithm within an adaptive filter network. Experimental results highlight the algorithm's capability to enhance the accuracy of localization and tracking operations. This improvement remains consistent across wireless sensor network scenarios influenced by both Gaussian and non-Gaussian noises, with varying signal-to-noise ratios.

Keywords

Main Subjects

References

[1] S. Doostali and M. Khalily-Dermany, “A multi-hop PSO based localization algorithm for wireless sensor networks,” Soft Comput. J., vol. 8, no. 1, pp. 58-69, 2019, doi: 10.22052/8.1.58 [In Persian].
[2] M. Rezaeenejad, M. Rahiminasab, and S.A. Mousavi, “Energy Aware Routing in Wireless Sensor Networks Using Harmony Search Algorithm,” Soft Comput. J., vol. 1, no. 1, pp. 2-15, 2012, dor: 20.1001.1.23223707.1391.1.1.109.6 [In Persian].
[3] R. Rafeh and M. Totonchy, “Proposing a Distributed Fault Detection Algorithm for Wireless Sensor Networks,” Soft Comput. J., vol. 2, no. 2, pp. 26-35, 2014, dor: 20.1001.1.23223707.1392.2.2.55.1 [In Persian].
[4] C.G. Lopes and A.H. Seyed, “Diffusion Least-Mean Squares over Adaptive Networks : Formulation and Performance Analysis,” IEEE Trans. Signal Process, vol. 56, no. 7-2, pp. 3122-3136, 2008, doi: 10.1109/TSP.2008.917383.
[5] R. Abdolee, S. Saur, B. Champagne, and A. H. Sayed, “Diffusion LMS localization and tracking algorithm for wireless cellular networks,” in IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Vancouver, BC, Canada, 2013, pp. 4598-4602, doi: 10.1109/ICASSP.2013.6638531.
[6] S. Ashkezari-Toussi and H. Sadoghi-Yazdi, “Robust diffusion LMS over adaptive networks,” Signal Process., vol. 158, pp. 201-209, 2019, doi: 10.1016/j.sigpro.2019.01.004.
[7] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Comput. Networks, vol. 38, no. 4, pp. 393-422, 2002, doi: 10.1016/S1389-1286(01)00302-4.
[8] S. Tabatabaei, “An Energy Efficient Clustering Method using Bat Algorithm and Mobile Sink in Wireless Sensor Networks,” Soft Comput. J., vol. 8, no. 2, pp. 102-115, 2020, doi: 10.22052/8.2.102 [In Persian].
[9] M. Khalily-Dermany, “A multiple criteria algorithm for planning the itinerary of mobile sink in wireless sensor networks,” Soft Comput. J., vol. 7, no. 2, pp. 74-83, 2019, dor: 20.1001.1.23223707.1397.7.2.6.7 [In Persian].
[10] F. Zafari, A. Gkelias, and K.K. Leung, “A survey of indoor localization systems and technologies,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, pp. 2568-2599, 2019, doi: 10.1109/COMST.2019.2911558.
[11] J. He, Y. Geng, and K. Pahlavan, “Toward accurate human tracking: Modeling time-of-arrival for wireless wearable sensors in multipath environment,” IEEE Sens. J., vol. 14, no. 11, pp. 3996-4006, 2014, doi: 10.1109/JSEN.2014.2356857.
[12] J. Cota-Ruiz, J.-G. Rosiles, P. Rivas-Perea, and E. Sifuentes, “A Distributed Localization Algorithm for Wireless Sensor Networks Based on the Solutions of Spatially-Constrained Local Problems,” in IEEE Sens. J., vol. 13, no. 6, pp. 2181-2191, June 2013, doi: 10.1109/JSEN.2013.2249660. 
[13] X. Qu and L. Xie, “An efficient convex constrained weighted least squares source localization algorithm based on TDOA measurements,” Signal Process., vol. 119, pp. 142-152, 2016, doi: 10.1016/j.sigpro.2015.08.001. 
[14] T. Jia, H. Wang, X. Shen, Z. Jiang, and K. He, “Target localization based on structured total least squares with hybrid TDOA-AOA measurements,” Signal Process., vol. 143, pp. 211-221, 2018, doi: 10.1016/j.sigpro.2017.09.011. 
[15] S. Tomic, M. Beko, and R. Dinis, “3-D target localization in wireless sensor networks using RSS and AoA measurements,” IEEE Trans. Veh. Technol., vol. 66, no. 4, pp. 3197-3210, 2017, doi: 10.1109/TVT.2016.2589923.
[16]  J. Shi, G. Wang, and L. Jin, “Least squared relative error estimator for RSS based localization with unknown transmit power,” IEEE Signal Process. Lett., vol. 27, pp. 1165-1169, 2020, doi: 10.1109/LSP.2020.3005298.
[17] W. Ding, Q. Zhong, Y. Wang, C. Guan, and B. Fang, “Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation,” Sensors, vol. 22, no. 3, p. 733, 2022, doi: 10.3390/s22030733. 
[18] A.H. Sayed, A. Tarighat, and N. Khajehnouri, “Network-based wireless location: challenges faced in developing techniques for accurate wireless location information,” IEEE Signal Process. Mag., vol. 22, no. 4, pp. 24-40, 2005, doi: 10.1109/MSP.2005.1458275.
[19] L.L. de Oliveira, G.H. Eisenkraemer, E.A. Carara, J.B. Martins, and J. Monteiro, “Mobile Localization Techniques for Wireless Sensor Networks: Survey and Recommendations,” ACM Trans. Sens. Networks, vol. 19, no. 2, pp. 1-39, 2023, doi: 10.1145/3561512.
[20] S. Tomic, M. Beko, L.M. Camarinha-Matos, and L.B. Oliveira, “Distributed localization with complemented RSS and AOA measurements: Theory and Methods,” Appl. Sci., vol. 10, no. 1, p. 272, 2020, doi: 10.3390/app10010272.
[21] P. Yadav and S.C. Sharma, “A Systematic Review of Localization in WSN: Machine Learning and Optimization-Based approaches,” Int. J. Commun. Syst., vol. 36, no. 4, 2023, doi: 10.1002/dac.5397.
[22] X. Zhu, W.-P. Zhu, and B. Champagne, “Spectrum sensing based on fractional lower order moments for cognitive radios in α-stable distributed noise,” Signal Process., vol. 111, pp. 94-105, 2015, doi: 10.1016/j.sigpro.2014.12.022.
[23] C. Soares and J. Gomes, “STRONG: Synchronous and asynchronous robust network localization, under non-Gaussian noise,” Signal Process., vol. 185, p. 108066, 2021, doi: 10.1016/j.sigpro.2021.108066.
[24] S.A. Kassam, Signal detection in non-Gaussian noise, Springer Science and Business Media, 1988, doi: 10.1007/978-1-4612-3834-8.
[25] I.D. Schizas, G. Mateos, and G.B. Giannakis, “Distributed LMS for consensus-based in-network adaptive processing,” IEEE Trans. Signal Process., vol. 57, no. 6, pp. 2365-2382, 2009, doi: 10.1109/TSP.2009.2016226.
Soft Computing Journal
Volume 13, Issue 1 - Serial Number 25
September 2024
Pages 58-75

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History

  • Receive Date: 04 April 2023
  • Revise Date: 27 August 2023
  • Accept Date: 04 September 2023

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