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Link to original content: https://doi.org/10.1007/s10776-023-00604-y
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Cooperative DE-Based Localization Algorithm for Wireless Communication Network

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Abstract

Although the conventional differential evolution (DE) localization algorithm provides reasonable localization results, its improvement in localization performance is limited. Considering the distance information between the tags being an effective way to improve localization performance, this paper proposes a novel cooperative DE-based localization algorithm by presenting the localization process and the fitness function, which contains the distance information between the tags. Specifically, our method first utilizes the conventional DE algorithm to provide the initial values of the tags. Then, according to the initial values, the distance information between the tags is fully utilized to improve the localization performance. The simulation results demonstrate our algorithm’s effectiveness in improving the localization performance and simultaneously realizing the localization of all tags.

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Some data generated or used during the study are available from the corresponding author by request.

References

  1. W. Li, L. Zhang, Y. Y. Wu, et al., Integrated Inter-tower wireless communications network for terrestrial broadcasting and multicasting systems, IEEE Transactions on Broadcasting, Vol. 67, No. 3, pp. 570–581, 2021.

    Article  Google Scholar 

  2. N. Singh and A. A. Shukla, Review on progress and future trends for wireless network for communication system, Lecture Notes in Electrical Engineering, Vol. 776, pp. 445–453, 2022.

    Article  Google Scholar 

  3. X. L. Tang, Research on Smart Logistics Model Based on Internet of Things Technology, IEEE Access, Vol. 8, pp. 151150–151159, 2020.

    Article  Google Scholar 

  4. K. Zhao, M. H. Zhu, B. Xiao, X. G. Yang, C. L. Gong and J. Y. Wu, Joint RFID and UWB technologies in intelligent warehousing management system, IEEE Internet of Things Journal, Vol. 7, No. 12, pp. 11640–11655, 2020.

    Article  Google Scholar 

  5. L. Q. Gui, F. Xiao, Y. Zhou, F. Shu and T. Val, Connectivity based DV-Hop localization for internet of things, IEEE Transactions on Vehicular Technology, Vol. 69, No. 8, pp. 8949–8958, 2020.

    Article  Google Scholar 

  6. L. Q. Gui, X. Y. Huang, F. Xiao, et al., DV-Hop localization with protocol sequence based access, IEEE Transactions on Vehicular Technology, Vol. 67, No. 10, pp. 9972–9982, 2018.

    Article  Google Scholar 

  7. C. H. Geng, X. Yuan and H. Huang, Exploiting channel correlations for NLOS TOA localization with multivariate gaussian mixture models, IEEE Wireless Communications Letters, Vol. 9, No. 1, pp. 70–73, 2020.

    Article  Google Scholar 

  8. T. Wang, H. Xiong, H. Ding and L. H. Zheng, TDOA-Based joint synchronization and localization algorithm for asynchronous wireless sensor networks, IEEE Transactions on Communications, Vol. 68, No. 5, pp. 3107–3124, 2020.

    Article  Google Scholar 

  9. Y. Zheng, M. Sheng, J. Y. Liu and J. D. Li, Exploiting AOA estimation accuracy for indoor localization: a weighted AOA-based approach, IEEE Wireless Communications Letters, Vol. 8, No. 1, pp. 65–68, 2019.

    Article  Google Scholar 

  10. V. Bianchi, P. Ciampolini and I. . De. . Munari, RSSI-based indoor localization and identification for ZigBee wireless sensor networks in smart homes, IEEE Transactions on Instrumentation and Measurement, Vol. 68, No. 2, pp. 566–575, 2019.

    Article  Google Scholar 

  11. X. X. Han, Y. C. Dong, L. Yue and Q. X. Xu, State transition simulated annealing algorithm for discrete-continuous optimization problems, IEEE Access, Vol. 7, pp. 44391–44403, 2019.

    Article  Google Scholar 

  12. K. R. Opara, R. Karol, Arabas and A. Jaroslaw, Differential evolution: a survey of theoretical analyses, Swarm and Evolutionary Computation, Vol. 44, pp. 546–558, 2018.

    Article  Google Scholar 

  13. S. Das and P. N. Suganthan, Differential Evolution: a survey of the State-of-the-Art, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 4–31, 2011.

    Article  Google Scholar 

  14. A. K. Qin, V. L. Huang and P. N. Suganthan, Differential Evolution algorithm with strategy adaptation for global numerical optimization, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 2, pp. 398–417, 2009.

    Article  Google Scholar 

  15. R. Harikrishnan, V. J. S. Kumar and P. S. Ponmalar, Differential evolution approach for localization in wireless sensor networks, in: IEEE International Conference on Computational Intelligence and Computing Research. pp. 1–4, 2014.

  16. D. Hernandez, R. Rodriguez, E. Merchan and A. Santiago, Optimal design of a drive shaft with composite materials through particle swarm optimization, IEEE Latin America Transactions, Vol. 18, No. 6, pp. 1008–1016, 2020.

    Article  Google Scholar 

  17. K. S. Yildirim, Gradient Descent algorithm inspired adaptive time synchronization in wireless sensor networks, IEEE Sensors Journal, Vol. 16, No. 13, pp. 5463–5470, 2016.

    Article  Google Scholar 

  18. V. Annepu and A. Rajesh, Implementation of self adaptive mutation factor and cross-over probability based differential evolution algorithm for node localization in wireless sensor networks, Evolutionary Intelligence, 2019.

  19. D. Qiao and G. K. H. Pang, A modified differential evolution with heuristic algorithm for nonconvex optimization on sensor network localization, IEEE Transactions on Vehicular Technology, Vol. 65, No. 3, pp. 1676–1689, 2016.

    Article  Google Scholar 

  20. C. Tian, Y. Ma and B. Wang, Cooperative localization for passive RFID backscatter networks and theoretical analysis of performance limit, IEEE Transactions on Wireless Communications, Vol. 22, No. 2, pp. 1388–1402, 2023.

    Article  Google Scholar 

  21. S. Kim and M. Kojima, Semidefinite programming relaxations for sensor network localization, Proceedings of the IEEE International Symposium on Computer-Aided Control System Design, Vol. 2021, pp. 19–23, 2010.

    Google Scholar 

  22. Y. Hu, S. Zhuo and R. Fan, A semidefinite programming algorithm for improving noisy sensor positions using accurate inter-sensor range measurements, in: Proceedings of 2020 2nd International Conference on Image Processing and Machine Vision. pp. 124–129, 2020.

  23. M. NaraghiPour and G. C. RojasSensor, Network localization via distributed randomized gradient descent, in: IEEE Military Communications Conference. pp. 1714–1719, 2013.

  24. B. Xia, N. Xie, W. H. Yuan and C. H. Li, A novel statistical manifold algorithm for position estimation, IEEE/CAA Journal of Automatica Sinica, Vol. 6, No. 6, pp. 1513–1518, 2019.

    Article  Google Scholar 

  25. J. Cheng and L. Xia, An effective Cuckoo search algorithm for node localization in wireless sensor network, Sensors (Switzerland), Vol. 16, No. 9, pp. 1–17, 2016.

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62001272.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, Shandong, China

    Ying Gao, Bin Xia & Liye Zhang

  2. College of Architecture and Information Engineering, Shandong Vocational College of Industry, Zibo, China

    Ying Gao

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  1. Ying Gao
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  2. Bin Xia
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  3. Liye Zhang
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Correspondence to Bin Xia.

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Gao, Y., Xia, B. & Zhang, L. Cooperative DE-Based Localization Algorithm for Wireless Communication Network. Int J Wireless Inf Networks 30, 306–315 (2023). https://doi.org/10.1007/s10776-023-00604-y

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