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Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

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Abstract

Sensor node localization is an important feature of many applications, including wireless sensor networks and location-based services. The accurate localization of sensor nodes improves system performance and reliability. This research emphasizes the benefits of using hybrid machine learning and optimization strategies for sensor node localization. Machine Learning (ML) algorithms, such as neural networks and support vector machines, are used to simulate complex correlations between sensor readings and related locations. These models enable precise prediction of node placements based on received signal strength, time of arrival, or other sensory inputs. The survey conducted in this study aims to uncover the latest advancements in localization strategies within Wireless Sensor Networks through the utilization of ML and Optimization Techniques. By thoroughly examining the existing literature, research gaps have been identified when localization techniques are solely employed. To provide a comprehensive understanding, this survey offers a detailed classification of localization algorithms, covering various aspects. Furthermore, the paper elaborates on the implementation of Optimization and Machine Learning approaches, exploring potential combinations with localization techniques. Through the use of analytical tables, the survey presents a comprehensive overview of sensor node localization using ML and optimized approaches. Additionally, the study addresses the challenges encountered and identifies potential future directions for the integration of these techniques.

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Data Availability

Data sharing is not applicable to this article as this is a survey paper and no datasets were generated or analysed during the current study.

References

  1. Potdar, V., Sharif, A., & Chang, E. (2009). Wireless sensor networks: A survey. In Proceedings—International Conference on Advanced Information Networking and Applications, AINA (pp. 636–641). https://doi.org/10.1109/WAINA.2009.192.

  2. Zhou, Z., Xu, J., Zhang, Z., Lei, F., & Fang, W. (2017). Energy-efficient optimization for concurrent compositions of WSN services. IEEE Access, 5, 19994–20008. https://doi.org/10.1109/ACCESS.2017.2752756

    Article  Google Scholar 

  3. Yadav, P., Kumar, K., & Sharma, S.C. (2023). Machine learning based techniques for node localization in WSN: A survey. In Proceedings—IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023 (pp. 12–17).https://doi.org/10.1109/DICCT56244.2023.10110235.

  4. Alrajeh, N.A., Bashir, M., & Shams, B. (2013). Localization techniques in wireless sensor networks, vol. 2013. https://doi.org/10.1155/2013/304628.

  5. Kim, T., Vecchietti, L. F., Choi, K., Lee, S., & Har, D. (2021). Machine learning for advanced wireless sensor networks: A review. IEEE Sensors Journal, 21(11), 12379–12397. https://doi.org/10.1109/JSEN.2020.3035846

    Article  Google Scholar 

  6. Cheng, Y.K., Chang, R.Y., & Chen, L.J. (2017). A comparative study of machine-learning indoor localization using FM and DVB-T signals in real testbed environments. In IEEE Vehicular Technology Conference, vol. 2017. https://doi.org/10.1109/VTCSPRING.2017.8108573.

  7. Yang, Q. (2022). A new localization method based on improved particle swarm optimization for wireless sensor networks. IET Software, 16(3), 251–258. https://doi.org/10.1049/SFW2.12027

    Article  MathSciNet  Google Scholar 

  8. Sobehy, A., Renault, É., & Mühlethaler, P. (2022). Generalization aspect of accurate machine learning models for CSI-based localization. Annales des Telecommunications/Annals of Telecommunications, 77(5–6), 345–357. https://doi.org/10.1007/S12243-021-00853-Z/TABLES/3

    Article  Google Scholar 

  9. Turgut, Z., Ustebay, S., Ali Aydın, M., Gürkaş Aydın, G. Z., & Sertbaş, A. (2019). Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of Things environment. Transactions on Emerging Telecommunications Technologies, 30(9), e3705. https://doi.org/10.1002/ETT.3705

    Article  Google Scholar 

  10. Bai, J., Sun, Y., Meng, W., & Li, C. (2021). Wi-Fi fingerprint-based indoor mobile user localization using deep learning. Wireless Communications and Mobile Computing, 2021, 1–12. https://doi.org/10.1155/2021/6660990

    Article  Google Scholar 

  11. Eder, M., Reip, M., & Steinbauer, G. (2022). Creating a robot localization monitor using particle filter and machine learning approaches. Applied Intelligence, 52(6), 6955–6969. https://doi.org/10.1007/S10489-020-02157-6/TABLES/7

    Article  Google Scholar 

  12. Cottone, P., Gaglio, S., Lo Re, G., & Ortolani, M. (2016). A machine learning approach for user localization exploiting connectivity data. Engineering Applications of Artificial Intelligence, 50, 125–134. https://doi.org/10.1016/J.ENGAPPAI.2015.12.015

    Article  Google Scholar 

  13. Stanoev, A., Filiposka, S., In, V., & Kocarev, L. (2016). Cooperative method for wireless sensor network localization. Ad Hoc Networks, 40, 61–72. https://doi.org/10.1016/J.ADHOC.2016.01.003

    Article  Google Scholar 

  14. Ghari, P. M., Shahbazian, R., & Ghorashi, S. A. (2016). Wireless sensor network localization in harsh environments using SDP relaxation. IEEE Communications Letters, 20(1), 137–140. https://doi.org/10.1109/LCOMM.2015.2498179

    Article  Google Scholar 

  15. Miao, Y., Wu, H., & Zhang, L. (2018). The accurate location estimation of sensor node using received signal strength measurements in large-scale farmland. Journal of Sensors, 2018, 1–10. https://doi.org/10.1155/2018/2325863

    Article  Google Scholar 

  16. Sivasakthiselvan, S., & Nagarajan, V. (2020). Localization techniques of wireless sensor networks: A review. In Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020 (pp. 1643–1648). https://doi.org/10.1109/ICCSP48568.2020.9182290.

  17. More, A., & Raisinghani, V. (2017). A survey on energy efficient coverage protocols in wireless sensor networks. Journal of King Saud University—Computer and Information Sciences, 29(4), 428–448. https://doi.org/10.1016/J.JKSUCI.2016.08.001

    Article  Google Scholar 

  18. Jinning, Z., et al. (2019). A modified KNN Indoor WiFi localization method With K-median cluster. IOP Conference Series: Materials Science and Engineering, 608(1), 012008. https://doi.org/10.1088/1757-899X/608/1/012008

    Article  Google Scholar 

  19. Mao, G., & Fidan, B. (2009). Localization algorithms and strategies for wireless sensor networks. In Localization Algorithms and Strategies for Wireless Sensor Networks (pp. 1–510). https://doi.org/10.4018/978-1-60566-396-8.

  20. Cheng, L., Maple, C., Wu, C., & Meng, W. (2013). Localization in wireless sensor network. International Journal of Distributed Sensor Networks, 9, 457874. https://doi.org/10.1155/2013/457874

    Article  Google Scholar 

  21. Meng, W., Xiao, W., & Xie, L. (2011). An efficient EM algorithm for energy-based multisource localization in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 60(3), 1017–1027. https://doi.org/10.1109/TIM.2010.2047035

    Article  Google Scholar 

  22. Pal, A. (2010). Localization algorithms in wireless sensor networks: Current approaches and future challenges. Network Protocols and Algorithms, 2(1), 45–73. https://doi.org/10.5296/NPA.V2I1.279

    Article  Google Scholar 

  23. Tiwari, A., & Kumar, M. (2021). A review of range based localization techniques in wireless sensor networks. SSRG International Journal of Electronics and Communication Engineering, 8, 1–5. https://doi.org/10.14445/23488549/IJECE-V8I12P101

    Article  Google Scholar 

  24. Ristic, B., Morelande, M., Farina, A., & Dulman, S. (2006). On proximity-based range-free node localisation in wireless sensor networks. In 2006 9th International Conference on Information Fusion, 2006, Accessed: May 19, 2023. [Online]. Available: https://www.academia.edu/2755194/On_proximity_based_range_free_node_localisation_in_wireless_sensor_networks.

  25. Suo, H., Wan, J., Huang, L., & Zou, C. (2012). Issues and challenges of wireless sensor networks localization in emerging applications. In Proceedings—2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012, 3, 447–451. https://doi.org/10.1109/ICCSEE.2012.44.

  26. Jondhale, S. R., Deshpande, R. S., Walke, S. M., & Jondhale, A. S. (2017). Issues and challenges in RSSI based target localization and tracking in wireless sensor networks. International Conference on Automatic Control and Dynamic Optimization Techniques, ICACDOT, 2016, 594–598. https://doi.org/10.1109/ICACDOT.2016.7877655

    Article  Google Scholar 

  27. Majid, M., et al. (2022). Applications of wireless sensor networks and Internet of Things frameworks in the industry revolution 4.0: A systematic literature review. Sensors, 22(6), 2087. https://doi.org/10.3390/S22062087

    Article  Google Scholar 

  28. Chelouah, L., Semchedine, F., & Bouallouche-Medjkoune, L. (2018). Localization protocols for mobile wireless sensor networks: A survey. Computers & Electrical Engineering, 71, 733–751. https://doi.org/10.1016/J.COMPELECENG.2017.03.024

    Article  Google Scholar 

  29. Ullah, I., Liu, Y., Su, X., & Kim, P. (2019). Efficient and accurate target localization in underwater environment. IEEE Access, 7, 101415–101426. https://doi.org/10.1109/ACCESS.2019.2930735

    Article  Google Scholar 

  30. Kuo, S. P., Kuo, H. J., & Tseng, Y. C. (2009). The beacon movement detection problem in wireless sensor networks for localization applications. IEEE Transactions on Mobile Computing, 8(10), 1326–1338. https://doi.org/10.1109/TMC.2009.15

    Article  Google Scholar 

  31. Jiang, C., Li, T. S., Bin Liang, J., & Wu, H. (2017). Low-latency and energy-efficient data preservation mechanism in low-duty-cycle sensor networks. Sensors, 17(5), 1051. https://doi.org/10.3390/S17051051

    Article  Google Scholar 

  32. Liu, X., Gao, L., Lu, J., & Lioliou, E. (2016). Environmental risks, localization and the overseas subsidiary performance of MNEs from an emerging economy. Journal of World Business, 51(3), 356–368. https://doi.org/10.1016/J.JWB.2015.05.002

    Article  Google Scholar 

  33. Nemer, I., Sheltami, T., Shakshuki, E., Elkhail, A. A., & Adam, M. (2021). Performance evaluation of range-free localization algorithms for wireless sensor networks. Personal and Ubiquitous Computing, 25(1), 177–203. https://doi.org/10.1007/S00779-020-01370-X

    Article  Google Scholar 

  34. Yadav, P., Sharma, S. C., Singh, O., & Rishiwal, V. (2023). Optimized localization learning algorithm for indoor and outdoor localization system in WSNs. Wireless Personal Communications, 130(1), 651–672. https://doi.org/10.1007/S11277-023-10304-8/FIGURES/9

    Article  Google Scholar 

  35. Xie, N., Chen, Y., Li, Z., & Wu, D. O. (2021). Lightweight secure localization approach in wireless sensor networks. IEEE Transactions on Communications, 69(10), 6879–6893. https://doi.org/10.1109/TCOMM.2021.3098794

    Article  Google Scholar 

  36. Ou, C. H., & Ssu, K. F. (2008). Sensor position determination with flying anchors in three-dimensional wireless sensor networks. IEEE Transactions on Mobile Computing, 7(9), 1084–1097. https://doi.org/10.1109/TMC.2008.39

    Article  Google Scholar 

  37. Yadav, P., & Sharma, S.C. (2023). Q-Learning based optimized localization in WSN. In 2023 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1–5). https://doi.org/10.1109/ISCON57294.2023.10112130.

  38. Alsheikh, M.A., Lin, S., Niyato, D., & Tan, H.-P. (2015). Machine learning in wireless sensor networks: algorithms, strategies, and applications.

  39. Chen, Q., Chen, Y., Fan, C., Yang, F., & Wang, P. (2012). Research on node localization algorithm in WSN basing machine learning. https://doi.org/10.2991/ICCIA.2012.10.

  40. Wang, L., Er, M. J., & Zhang, S. (2020). A kernel extreme learning machines algorithm for node localization in wireless sensor networks. IEEE Communications Letters, 24(7), 1433–1436. https://doi.org/10.1109/LCOMM.2020.2986676

    Article  Google Scholar 

  41. Robinson, Y. H., Vimal, S., Julie, E. G., Lakshmi Narayanan, K., & Rho, S. (2022). 3-Dimensional manifold and machine learning based localization algorithm for wireless sensor networks. Wireless Personal Communications, 127(1), 523–541. https://doi.org/10.1007/S11277-021-08291-9/FIGURES/10

    Article  Google Scholar 

  42. Cheng, L., Wu, X., & Wang, Y. (2017). A non-line of sight localization method based on k-means clustering algorithm. In Proceedings of 2017 IEEE 7th International Conference on Electronics Information and Emergency Communication, ICEIEC 2017 (pp. 465–468). https://doi.org/10.1109/ICEIEC.2017.8076606.

  43. Chriki, A., Touati, H., & Snoussi, H. (2017). SVM-based indoor localization in wireless sensor networks. 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC, 2017, 1144–1149. https://doi.org/10.1109/IWCMC.2017.7986446

    Article  Google Scholar 

  44. Mohammed, S. K., Singh, S., Mizouni, R., & Otrok, H. (2023). A deep learning framework for target localization in error-prone environment. Internet of Things, 22, 100713. https://doi.org/10.1016/J.IOT.2023.100713

    Article  Google Scholar 

  45. Morelande, M.R., Moran, B., & Brazil, M. (2008). Bayesian node localisation in wireless sensor networks. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings (pp. 2545–2548). https://doi.org/10.1109/ICASSP.2008.4518167.

  46. (15) (PDF) NLOS Identification for Indoor Localization using Random Forest Algorithm. https://www.researchgate.net/publication/330901602_NLOS_Identification_for_Indoor_Localization_using_Random_Forest_Algorithm (Accessed May 19, 2023).

  47. Zhu, X. (2020). Indoor localization based on optimized KNN. Network and Communication Technologies, 5(2), 34. https://doi.org/10.5539/NCT.V5N2P34

    Article  MathSciNet  Google Scholar 

  48. Kumar, S., Tiwari, S. N., & Hegde, R. M. (2015). Sensor node tracking using semi-supervised hidden Markov models. Ad Hoc Networks, 33, 55–70. https://doi.org/10.1016/J.ADHOC.2015.04.004

    Article  Google Scholar 

  49. Yoo, J.H., Kim, W., & Kim, H.J. (2011). Event-driven Gaussian process for object localization in wireless sensor networks (pp. 2790–2795) https://doi.org/10.1109/IROS.2011.6094804.

  50. Poulose, A., & Han, D.S. (2021). Feature-based deep LSTM network for indoor localization using UWB measurements. In 3rd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2021 (pp. 298–301). https://doi.org/10.1109/ICAIIC51459.2021.9415277.

  51. Yan, W., Jin, D., Lin, Z., & Yin, F. (2021). Graph neural network for large-scale network localization,. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings, 2021, 5250–5254. https://doi.org/10.1109/ICASSP39728.2021.9414520.

  52. Tang, Z., et al. (2015). CTLL: A cell-based transfer learning method for localization in large scale wireless sensor networks. International Journal of Distributed Sensor Networks., 11, 252653. https://doi.org/10.1155/2015/252653

    Article  Google Scholar 

  53. Khelifi, M., & Moussaoui, S. (2021). ReLM: An efficient reinforcement learning-based localization algorithm for mobile wireless sensor networks. In 2021 International Conference on Innovations in Intelligent Systems and Applications, INISTA 2021: Proceedings. https://doi.org/10.1109/INISTA52262.2021.9548531.

  54. Tripathy, P., & Khilar, P. M. (2022). An ensemble approach for improving localization accuracy in wireless sensor network. Computer Networks, 219, 109427. https://doi.org/10.1016/J.COMNET.2022.109427

    Article  Google Scholar 

  55. Behera, A. P., Singh, A., Verma, S., & Kumar, M. (2020). Manifold learning with localized procrustes analysis based WSN localization. IEEE Sensors Letters, 4(10), 1–4. https://doi.org/10.1109/LSENS.2020.3025360

    Article  Google Scholar 

  56. Jain, N., Verma, S., & Kumar, M. (2017). Incremental LLE for localization in sensor networks. IEEE Sensors Journal, 17(19), 6483–6492. https://doi.org/10.1109/JSEN.2017.2738704

    Article  Google Scholar 

  57. Kar, A. K. (2016). Bio inspired computing: A review of algorithms and scope of applications. Expert Systems with Applications, 59, 20–32. https://doi.org/10.1016/J.ESWA.2016.04.018

    Article  Google Scholar 

  58. Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40(6), 663–675. https://doi.org/10.1109/TSMCC.2010.2049649

    Article  Google Scholar 

  59. Alomari, A., Phillips, W., Aslam, N., & Comeau, F. (2017). Swarm intelligence optimization techniques for obstacle-avoidance mobility-assisted localization in wireless sensor networks. IEEE Access, 6, 22368–22385. https://doi.org/10.1109/ACCESS.2017.2787140

    Article  Google Scholar 

  60. Liu, X., & You, X. (2021). Node localization algorithm based on improved DV-Hop wireless sensor network. In 2021 5th CAA International Conference on Vehicular Control and Intelligence, CVCI 2021. https://doi.org/10.1109/CVCI54083.2021.9661258.

  61. Hao, Z., Li, X., & Ding, Y. (2018). An improved PSO algorithm for node localization in indoor long-narrow confined space. In Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 (pp. 1841–1846). https://doi.org/10.1109/ICIEA.2018.8398008.

  62. Shen, S., Sun, L., Dang, Y., Zou, Z., & Wang, R. (2018). Node localization based on improved PSO and mobile nodes for environmental monitoring WSNs. International Journal of Wireless Information Networks, 25(4), 470–479. https://doi.org/10.1007/S10776-018-0414-3/FIGURES/9

    Article  Google Scholar 

  63. Saha, S., Saha, A., Roy, B., Sarkar, R., Bhardwaj, D., & Kundu, B. (2022). Integrating the particle swarm optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model. Earth Science Informatics, 15(4), 2637–2662. https://doi.org/10.1007/S12145-022-00878-5/TABLES/3

    Article  Google Scholar 

  64. Lee, S. H., et al. (2023). PSO-based target localization and tracking in wireless sensor networks. Electronics, 12(4), 905. https://doi.org/10.3390/ELECTRONICS12040905

    Article  Google Scholar 

  65. Nithya, B., & Jeyachidra, J. (2021). Hybrid ABC-BAT optimization algorithm for localization in HWSN. Microprocessors and Microsystems. https://doi.org/10.1016/J.MICPRO.2021.104024

    Article  Google Scholar 

  66. Goyal, S., & Patterh, M. S. (2016). Modified bat algorithm for localization of wireless sensor network. Wireless Personal Communications, 86(2), 657–670. https://doi.org/10.1007/S11277-015-2950-9/TABLES/5

    Article  Google Scholar 

  67. Nithya, B., & Jeyachidra, J. (2021). Optimized anchor based localization using bat optimization algorithm for heterogeneous WSN. In Proceedings of the 2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021. https://doi.org/10.1109/ICSES52305.2021.9633947.

  68. Yu, S., Zhu, J., & Lv, C. (2023). A quantum annealing bat algorithm for node localization in wireless sensor networks. Sensors (Basel), 23(2), 782. https://doi.org/10.3390/S23020782

    Article  Google Scholar 

  69. Mihoubi, M., Rahmoun, A., Lorenz, P., & Lasla, N. (2018). An effective Bat algorithm for node localization in distributed wireless sensor network. Security and Privacy, 1(1), e7.

    Article  Google Scholar 

  70. Yang, Y., Sun, L., & Xiang, M. (2015). Range-free localization algorithm based on mass spring model for wireless sensor networks. Chinese Journal of Sensors and Actuators, 28(6), 914–919. https://doi.org/10.3969/J.ISSN.1004-1699.2015.06.023

    Article  Google Scholar 

  71. Oliva, G., Setola, R., Panzieri, S., & Pascucci, F. (2016). Localization of networks with presence and distance constraints based on 1-hop and 2-hop mass–spring optimization. ICT Express, 2(1), 19–22. https://doi.org/10.1016/J.ICTE.2016.02.005

    Article  Google Scholar 

  72. Arora, S., & Singh, S. (2019). Butterfly optimization algorithm: A novel approach for global optimization. Soft Computing, 23(3), 715–734. https://doi.org/10.1007/S00500-018-3102-4/FIGURES/10

    Article  Google Scholar 

  73. Arora, S., & Anand, P. (2019). Learning automata-based butterfly optimization algorithm for engineering design problems. International Journal of Computational Materials Science and Engineering, 7(4), 1850021. https://doi.org/10.1142/S2047684118500215

    Article  Google Scholar 

  74. Stromberger, I., Tuba, E., Bacanin, N., Beko, M., & Tuba, M. (2018). Monarch butterfly optimization algorithm for localization in wireless sensor networks. In 2018 28th International Conference Radioelektronika, RADIOELEKTRONIKA (pp. 1–6). https://doi.org/10.1109/RADIOELEK.2018.8376387.

  75. Arora, S., & Singh, S. (2017). Node localization in wireless sensor networks using butterfly optimization algorithm. Arabian Journal for Science and Engineering, 42(8), 3325–3335. https://doi.org/10.1007/S13369-017-2471-9/METRICS

    Article  Google Scholar 

  76. Makhadmeh, S. N., et al. (2023). Recent advances in butterfly optimization algorithm, its versions and applications. Archives of Computational Methods in Engineering, 30(2), 1399–1420. https://doi.org/10.1007/S11831-022-09843-3/TABLES/2

    Article  Google Scholar 

  77. Kaur, S., et al. (2023). Node localization and data aggregation scheme using cuckoo search and neural network. Expert Systems, 40(4), e13033. https://doi.org/10.1111/EXSY.13033

    Article  Google Scholar 

  78. Kotiyal, V., Singh, A., Sharma, S., Nagar, J., & Lee, C. C. (2021). ECS-NL: An enhanced cuckoo search algorithm for node localisation in wireless sensor networks. Sensors., 21(11), 3576. https://doi.org/10.3390/S21113576

    Article  Google Scholar 

  79. Cheng, J., & Xia, L. (2016). An effective cuckoo search algorithm for node localization in wireless sensor network. Sensors (Switzerland), 16(9). https://doi.org/10.3390/S16091390.

  80. Cheng, J., & Xia, L. (2016). An effective cuckoo search algorithm for node localization in wireless sensor network. Sensors, 16(9), 1390. https://doi.org/10.3390/S16091390

    Article  Google Scholar 

  81. Shenkai, G., Li, C., Jing, W., & Xianglong, L. (2021). An improved approach for iterative nodes localization by using artificial bee colony, p. 109. https://doi.org/10.1117/12.2615329.

  82. Shan, H. M. (2021). A node localisation method for wireless sensor networks based on an improved bee colony algorithm. International Journal of Sensor Networks, 36(2), 108–114. https://doi.org/10.1504/IJSNET.2021.115922

    Article  Google Scholar 

  83. Annepu, V., & Rajesh, A. (2020). Implementation of an efficient artificial bee colony algorithm for node localization in unmanned aerial vehicle assisted wireless sensor networks. Wireless Personal Communications, 114(3), 2663–2680. https://doi.org/10.1007/S11277-020-07496-8/FIGURES/11

    Article  Google Scholar 

  84. Krishnamoorthy, V. K., Duraisamy, U. N., Jondhale, A. S., Lloret, J., & Ramasamy, B. V. (2023). Energy-constrained target localization scheme for wireless sensor networks using radial basis function neural network. International Journal of Distributed Sensor Networks, 2023, 1–12. https://doi.org/10.1155/2023/1426430

    Article  Google Scholar 

  85. Mohar, S. S., Goyal, S., & Kaur, R. (2022). Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics. Journal of Supercomputing, 78(9), 11975–12023. https://doi.org/10.1007/S11227-022-04320-X/TABLES/8

    Article  Google Scholar 

  86. Zazali, A. A., Subramaniam, S. K., & Zukarnain, Z. A. (2020). Flood control distance vector-hop (FCDV-Hop) localization in wireless sensor networks. IEEE Access, 8, 206592–206613. https://doi.org/10.1109/ACCESS.2020.3038047

    Article  Google Scholar 

  87. Han, G., Zhang, C., Jiang, J., Yang, X., & Guizani, M. (2017). Mobile anchor nodes path planning algorithms using network-density-based clustering in wireless sensor networks. Journal of Network and Computer Applications, 85, 64–75. https://doi.org/10.1016/J.JNCA.2016.12.016

    Article  Google Scholar 

  88. Kouroshnezhad, S., Peiravi, A., Sayad Haghighi, M., & 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. https://doi.org/10.1016/J.ADHOC.2018.12.014

    Article  Google Scholar 

  89. Karimi Alavijeh, A., Ramezani, M. H., & Karimi Alavijeh, A. (2018). Localization improvement in wireless sensor networks using a new statistical channel model. Sensors and Actuators A: Physical, 271, 283–289. https://doi.org/10.1016/J.SNA.2018.01.015

    Article  Google Scholar 

  90. Chen, Y., Lu, S., Chen, J., & Ren, T. (2017). Node localization algorithm of wireless sensor networks with mobile beacon node. Peer-to-Peer Networking and Applications, 10(3), 795–807. https://doi.org/10.1007/S12083-016-0522-8

    Article  Google Scholar 

  91. Zhang, S., Er, M. J., Zhang, B., & Naderahmadian, Y. (2017). A novel heuristic algorithm for node localization in anisotropic wireless sensor networks with holes. Signal Processing, 138, 27–34. https://doi.org/10.1016/J.SIGPRO.2017.03.010

    Article  Google Scholar 

  92. Shahzad, F., Sheltami, T. R., & Shakshuki, E. M. (2017). DV-maxHop: A fast and accurate range-free localization algorithm for anisotropic wireless networks. IEEE Transactions on Mobile Computing, 16(9), 2494–2505. https://doi.org/10.1109/TMC.2016.2632715

    Article  Google Scholar 

  93. Tomic, S., Beko, M., & Dinis, R. (2017). 3-D target localization in wireless sensor networks using RSS and AoA measurements. IEEE Transactions on Vehicular Technology, 66(4), 3197–3210. https://doi.org/10.1109/TVT.2016.2589923

    Article  Google Scholar 

  94. Lv, T., Gao, H., Li, X., Yang, S., & Hanzo, L. (2016). Space-time hierarchical-graph based cooperative localization in wireless sensor networks. IEEE Transactions on Signal Processing, 64(2), 322–334. https://doi.org/10.1109/TSP.2015.2480038

    Article  MathSciNet  MATH  Google Scholar 

  95. Li, S., Wang, X., Zhao, S., Wang, J., & Li, L. (2013). Local semidefinite programming-based node localization system for wireless sensor network applications. IEEE Systems Journal, 8(3), 879–888. https://doi.org/10.1109/JSYST.2013.2260625

    Article  Google Scholar 

  96. Ou, C. H., & He, W. L. (2013). Path planning algorithm for mobile anchor-based localization in wireless sensor networks. IEEE Sensors Journal, 13(2), 466–475. https://doi.org/10.1109/JSEN.2012.2218100

    Article  Google Scholar 

  97. Zhao, J., et al. (2013). Localization of wireless sensor networks in the wild: Pursuit of ranging quality. IEEE/ACM Transactions on Networking, 21(1), 311–323. https://doi.org/10.1109/TNET.2012.2200906

    Article  Google Scholar 

  98. Chang, C. Y., Lin, C. Y., & Chang, C. T. (2012). Tone-based localization for distinguishing relative locations in wireless sensor networks. IEEE Sensors Journal, 12(5), 1058–1070. https://doi.org/10.1109/JSEN.2011.2163503

    Article  Google Scholar 

  99. Abdelhakim, A. Machine learning for localization of radioactive sources via a distributed sensor network. https://doi.org/10.1007/s00500-023-08447-8.

  100. Asif, R., Farooq-i-Azam, M., Chaudary, M. H., Husen, A., & Hassan, S. R. (2023). A distance vector hop-based secure and robust localization algorithm for wireless sensor networks. Electronics, 12(10), 2237. https://doi.org/10.3390/ELECTRONICS12102237

    Article  Google Scholar 

  101. Abdullah, O.A., Al-Hraishawi, H., & Chatzinotas S. (2023). Deep learning-based device-free localization in wireless sensor networks. In 2023 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1–6). https://doi.org/10.1109/WCNC55385.2023.10118744.

  102. Kagi, S., & Mathapati, B. S. (2022). Localization in wireless sensor network using machine learning optimal trained deep neural network by parametric analysis. Measurement: Sensors, 24, 100427. https://doi.org/10.1016/J.MEASEN.2022.100427

    Article  MATH  Google Scholar 

  103. Gang, Q., Muhammad, A., Khan, Z. U., Khan, M. S., Ahmed, F., & Ahmad, J. (2022). Machine learning-based prediction of node localization accuracy in IIoT-based MI-UWSNs and design of a TD coil for omnidirectional communication. Sustainability, 14(15), 1–23.

    Article  Google Scholar 

  104. Liouane, O., Femmam, S., Bakir, T., & Ben Abdelali, A. (2021). Improved two hidden layers extreme learning machines for node localization in range free wireless sensor networks. Journal of Communications., 16(12), 528–534. https://doi.org/10.12720/JCM.16.12.528-534

    Article  Google Scholar 

  105. Hu, Q., Wu, F., Wong, R. K., Millham, R. C., & Fiaidhi, J. (2023). A novel indoor localization system using machine learning based on bluetooth low energy with cloud computing. Computing, 105(3), 689–715. https://doi.org/10.1007/S00607-020-00897-4/TABLES/3

    Article  Google Scholar 

  106. Al-Rashdan, W. Y., & Tahat, A. (2020). A comparative performance evaluation of machine learning algorithms for fingerprinting based localization in DM-MIMO wireless systems relying on big data techniques. IEEE Access, 8, 109522–109534. https://doi.org/10.1109/ACCESS.2020.3001912

    Article  Google Scholar 

  107. Kangyong, Y., Guo, W., Peng, T., Liu, Y., Zuo, P., & Wang, W. Parametric sparse bayesian dictionary learning for multiple sources localization with propagation parameters uncertainty and nonuniform noise.

  108. Maghdid, H.S., Ghafoor, K.Z., Al-Talabani, A., Sadiq, A.S., Singh, P.K., & Rawat, D.B. (2022). Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm. Internet Technology Letters, 5(1). https://doi.org/10.1002/ITL2.200.

  109. Bhatti, M. A., Riaz, R., Rizvi, S. S., Shokat, S., Riaz, F., & Kwon, S. J. (2020). Outlier detection in indoor localization and Internet of Things (IoT) using machine learning. Journal of Communications and Networks, 22(3), 236–243. https://doi.org/10.1109/JCN.2020.000018

    Article  Google Scholar 

  110. Kim, M., Han, D., & Kevin Rhee, J. K. (2020). Machine learning for practical localization system using multiview CSI. IEEE Access, 8, 184575–184584. https://doi.org/10.1109/ACCESS.2020.3029598

    Article  Google Scholar 

  111. Li, W., Chen, P., Wang, B., & Xie, C. (2019). Automatic localization and count of agricultural crop pests based on an improved deep learning pipeline. Scientific Reports 9(1). https://doi.org/10.1038/S41598-019-43171-0.

  112. Fan, J., & Awan, A. S. (2019). Non-line-of-sight identification based on unsupervised machine learning in ultra wideband systems. IEEE Access, 7, 32464–32471. https://doi.org/10.1109/ACCESS.2019.2903236

    Article  Google Scholar 

  113. Srinivasan, S. M., Truong-Huu, T., & Gurusamy, M. (2019). Machine learning-based link fault identification and localization in complex networks. IEEE Internet of Things Journal, 6(4), 6556–6566. https://doi.org/10.1109/JIOT.2019.2908019

    Article  Google Scholar 

  114. Yan, J., Xu, Z., Luo, X., Chen, C., & Guan, X. (2019). Feedback-based target localization in underwater sensor networks: A multisensor fusion approach. IEEE Transactions on Signal and Information Processing over Networks, 5(1), 168–180. https://doi.org/10.1109/TSIPN.2018.2866335

    Article  Google Scholar 

  115. Panayiotou, T., Chatzis, S. P., & Ellinas, G. (2018). Leveraging statistical machine learning to address failure localization in optical networks. Journal of Optical Communications and Networking, 10(3), 162–173. https://doi.org/10.1364/JOCN.10.000162

    Article  Google Scholar 

  116. Berz, E. L., Tesch, D. A., & Hessel, F. P. (2018). Machine-learning-based system for multi-sensor 3D localisation of stationary objects. IET Cyber-Physical Systems: Theory & Applications, 3(2), 81–88. https://doi.org/10.1049/IET-CPS.2017.0067

    Article  Google Scholar 

  117. Prasad, K. N. R. S. V., Hossain, E., & Bhargava, V. K. (2018). Machine learning methods for RSS-based user positioning in distributed massive MIMO. IEEE Transactions on Wireless Communications, 17(12), 8402–8417. https://doi.org/10.1109/TWC.2018.2876832

    Article  Google Scholar 

  118. Silva Almeida, J., Bezerra Marinho, L., Mendes Souza, J. W., Assis, E. A., & Reboucas Filho, P. P. (2018). Localization system for autonomous mobile robots using machine learning methods and omnidirectional sonar. IEEE Latin America Transactions, 16(2), 368–374. https://doi.org/10.1109/TLA.2018.8327388

    Article  Google Scholar 

  119. Amri, S., Khelifi, F., Bradai, A., Rachedi, A., Kaddachi, M. L., & Atri, M. (2019). A new fuzzy logic based node localization mechanism for wireless sensor networks. Future Generation Computer Systems, 93, 799–813. https://doi.org/10.1016/J.FUTURE.2017.10.023

    Article  Google Scholar 

  120. Khatab, Z. E., Hajihoseini, A., & Ghorashi, S. A. (2018). A fingerprint method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sensors Letters, 2(1), 1–4. https://doi.org/10.1109/LSENS.2017.2787651

    Article  Google Scholar 

  121. Bin Tariq, O., Lazarescu, M. T., Iqbal, J., & Lavagno, L. (2017). Performance of machine learning classifiers for indoor person localization with capacitive sensors. IEEE Access, 5, 12913–12926. https://doi.org/10.1109/ACCESS.2017.2721538

    Article  Google Scholar 

  122. Wang, J., Zhang, X., Gao, Q., Yue, H., & Wang, H. (2017). Device-free wireless localization and activity recognition: A deep learning approach. IEEE Transactions on Vehicular Technology, 66(7), 6258–6267. https://doi.org/10.1109/TVT.2016.2635161

    Article  Google Scholar 

  123. Zheng, K., et al. (2017). Energy-efficient localization and tracking of mobile devices in wireless sensor networks. IEEE Transactions on Vehicular Technology, 66(3), 2714–2726. https://doi.org/10.1109/TVT.2016.2584104

    Article  Google Scholar 

  124. Jiang, M., Lu, S., Sui, Q., Dong, H., Sai, Y., & Jia, L. (2015). Low velocity impact localization on CFRP based on FBG sensors and ELM algorithm. IEEE Sensors Journal, 15(8), 4451–4456. https://doi.org/10.1109/JSEN.2015.2422851

    Article  Google Scholar 

  125. Kim, W., Park, J., & Kim, H. J. (2010). Target localization using ensemble support vector regression in wireless sensor networks. IEEE Wireless Communications and Networking Conference, WCNC. https://doi.org/10.1109/WCNC.2010.5506589

    Article  Google Scholar 

  126. Rahman, M. S., Park, Y., & Kim, K. D. (2012). RSS-based indoor localization algorithm for wireless sensor network using generalized regression neural network. Arabian Journal for Science and Engineering, 37(4), 1043–1053. https://doi.org/10.1007/S13369-012-0218-1/METRICS

    Article  Google Scholar 

  127. Wymeersch, H., Maranò, S., Gifford, W. M., & Win, M. Z. (2012). A machine learning approach to ranging error mitigation for UWB localization. IEEE Transactions on Communications, 60(6), 1719–1728. https://doi.org/10.1109/TCOMM.2012.042712.110035

    Article  Google Scholar 

  128. Chadha, J., Jain, A., & Kumar, Y. (2023). Satellite imagery-based Airbus ship localization and detection using deep learning-based approaches. Peer-to-Peer Networking and Applications. https://doi.org/10.1007/S12083-023-01493-X

    Article  Google Scholar 

  129. Klein, L. C., et al. (2023). A machine learning approach to robot localization using fiducial markers in RobotAtFactory 4.0 competition. Sensors, 23(6), 3128. https://doi.org/10.3390/S23063128

    Article  Google Scholar 

  130. Al-Habashna, A., Wainer, G., & Aloqaily, M. (2022). Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks. Simulation Modelling Practice and Theory, 118, 102543. https://doi.org/10.1016/J.SIMPAT.2022.102543

    Article  Google Scholar 

  131. Mohanta, T. K., & Das, D. K. (2023). Improved wireless sensor network localization algorithm based on selective opposition class topper optimization (SOCTO). Wireless Personal Communications, 128(4), 2847–2868. https://doi.org/10.1007/S11277-022-10075-8/TABLES/9

    Article  Google Scholar 

  132. Yadav, P., Sharma, S.C., & Rishiwal, V. (2022). Hybrid localization scheme using K-fold optimization with machine learning in WSN. International Journal of Communication Systems, 35(12). https://doi.org/10.1002/dac.5206.

  133. Chen, J., Sackey, S. H., Anajemba, J. H., Zhang, X., & He, Y. (2021). Energy-efficient clustering and localization technique using genetic algorithm in wireless sensor networks. Complexity, 2021, 1–12. https://doi.org/10.1155/2021/5541449

    Article  Google Scholar 

  134. Shen, Z., Zhang, T., Tagami, A., & Jin, J. (2021). When RSSI encounters deep learning: An area localization scheme for pervasive sensing systems. Journal of Network and Computer Applications., 173, 102852. https://doi.org/10.1016/J.JNCA.2020.102852

    Article  Google Scholar 

  135. Guo, R., Qin, D., Zhao, M., & Xu, G. (2020). Mobile target localization based on iterative tracing for underwater wireless sensor networks. International Journal of Distributed Sensor Networks, 16(7). https://doi.org/10.1177/1550147720940634.

  136. Singh, A., Kotiyal, V., Sharma, S., Nagar, J., & Lee, C. C. (2020). A machine learning approach to predict the average localization error with applications to wireless sensor networks. IEEE Access, 8, 208253–208263. https://doi.org/10.1109/ACCESS.2020.3038645

    Article  Google Scholar 

  137. Ren, Q., Zhang, Y., Nikolaidis, I., Li, J., & Pan, Y. (2020). RSSI quantization and genetic algorithm based localization in wireless sensor networks. Ad Hoc Networks, 107, 102255. https://doi.org/10.1016/J.ADHOC.2020.102255

    Article  Google Scholar 

  138. Cai, X., Wang, P., Cui, Z., Zhang, W., & Chen, J. (2020). Weight convergence analysis of DV-hop localization algorithm with GA. Soft Computing, 24(23), 18249–18258. https://doi.org/10.1007/S00500-020-05088-Z

    Article  MATH  Google Scholar 

  139. Anusha, K. S., Ramanathan, R., & Jayakumar, M. (2020). Link distance-support vector regression (LD-SVR) based device free localization technique in indoor environment. Engineering Science and Technology, an International Journal, 23(3), 483–493. https://doi.org/10.1016/J.JESTCH.2019.09.004

    Article  Google Scholar 

  140. Rauchenstein, L.T., Vishnu, A., Li, X., & Deng, Z.D. (2018). Improving underwater localization accuracy with machine learning. Review of Scientific Instruments, 89(7). https://doi.org/10.1063/1.5012687.

  141. Wen, W., Wen, X., Yuan, L., & Xu, H. (2018). Range-free localization using expected hop progress in anisotropic wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2018(1), 1–13. https://doi.org/10.1186/S13638-018-1326-8/FIGURES/11

    Article  Google Scholar 

  142. Sun, Y., Zhang, X., Wang, X., & Zhang, X. (2018). Device-free wireless localization using artificial neural networks in wireless sensor networks. Wireless Communications and Mobile Computing, 2018, 1–8. https://doi.org/10.1155/2018/4201367

    Article  Google Scholar 

  143. Fang, X., Jiang, Z., Nan, L., & Chen, L. (2018). Optimal weighted K-nearest neighbour algorithm for wireless sensor network fingerprint localisation in noisy environment. IET Communications, 12(10), 1171–1177. https://doi.org/10.1049/IET-COM.2017.0515

    Article  Google Scholar 

  144. Phoemphon, S., So-In, C., & Tao Niyato, D. (2018). A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization. Applied Soft Computing, 65, 101–120. https://doi.org/10.1016/J.ASOC.2018.01.004

    Article  Google Scholar 

  145. Wang, Z., Zhang, H., Lu, T., Sun, Y., & Liu, X. (2017). A new range-free localisation in wireless sensor networks using support vector machine. International Journal of Electronics, 105(2), 244–261. https://doi.org/10.1080/00207217.2017.1357198

    Article  Google Scholar 

  146. Sharma, G., & Kumar, A. (2018). Modified energy-efficient range-free localization using teaching–learning-based optimization for wireless sensor networks. IETE Journal of Research, 64(1), 124–138. https://doi.org/10.1080/03772063.2017.1333467

    Article  Google Scholar 

  147. Banihashemian, S. S., Adibnia, F., & Sarram, M. A. (2018). A new range-free and storage-efficient localization algorithm using neural networks in wireless sensor networks. Wireless Personal Communications, 98(1), 1547–1568. https://doi.org/10.1007/S11277-017-4934-4/FIGURES/11

    Article  Google Scholar 

  148. Kang, J., Park, Y. J., Lee, J., Wang, S. H., & Eom, D. S. (2018). Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Transactions on Industrial Electronics, 65(5), 4279–4289. https://doi.org/10.1109/TIE.2017.2764861

    Article  Google Scholar 

  149. Sun, B., Guo, Y., Li, N., & Fang, D. (2017). Multiple target counting and localization using variational bayesian EM algorithm in wireless sensor networks. IEEE Transactions on Communications, 65(7), 2985–2998. https://doi.org/10.1109/TCOMM.2017.2695198

    Article  Google Scholar 

  150. Z. Wang, H. Liu, S. Xu, X. Bu, and J. An, “Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network,” Sensors 2017, Vol. 17, Page 969, vol. 17, no. 5, p. 969, Apr. 2017, doi: https://doi.org/10.3390/S17050969.

  151. Li, X., Ding, S., & Li, Y. (2017). Outlier suppression via non-convex robust PCA for efficient localization in wireless sensor networks. IEEE Sensors Journal, 17(21), 7053–7063. https://doi.org/10.1109/JSEN.2017.2754502

    Article  Google Scholar 

  152. Correa, A., Llado, M. B., Morell, A., & Vicario, J. L. (2016). Indoor pedestrian tracking by on-body multiple receivers. IEEE Sensors Journal, 16(8), 2545–2553. https://doi.org/10.1109/JSEN.2016.2518872

    Article  Google Scholar 

  153. Janapati, R., Balaswamy, C., Soundararajan, K., & Venkanna, U. (2016). Indoor localization of cooperative WSN using PSO assisted AKF with optimum references. Procedia Computer Science, 92, 282–291. https://doi.org/10.1016/J.PROCS.2016.07.357

    Article  Google Scholar 

  154. El Assaf, A., Zaidi, S., Affes, S., & Kandil, N. (2016). Robust ANNs-based WSN localization in the presence of anisotropic signal attenuation. IEEE Wireless Communications Letters, 5(5), 504–507. https://doi.org/10.1109/LWC.2016.2595576

    Article  Google Scholar 

  155. Zhu, F., & Wei, J. (2017). Localization algorithm for large scale wireless sensor networks based on fast-SVM. Wireless Personal Communications, 95(3), 1859–1875. https://doi.org/10.1007/S11277-016-3665-2

    Article  Google Scholar 

  156. Gharghan, S. K., Nordin, R., Ismail, M., & Ali, J. A. (2016). Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling. IEEE Sensors Journal, 16(2), 529–541. https://doi.org/10.1109/JSEN.2015.2483745

    Article  Google Scholar 

  157. So-In, C., Permpol, S., & Rujirakul, K. (2016). Soft computing-based localizations in wireless sensor networks. Pervasive and Mobile Computing, 29, 17–37. https://doi.org/10.1016/J.PMCJ.2015.06.010

    Article  Google Scholar 

  158. Bernas, M., & Płaczek, B. (2015). Fully connected neural networks ensemble with signal strength clustering for indoor localization in wireless sensor networks, vol. 2015. https://doi.org/10.1155/2015/403242.

  159. Payal, A., Rai, C.S., & Reddy, B.V.R. (2014). Artificial neural networks for developing localization framework in wireless sensor networks. In 2014 International Conference on Data Mining and Intelligent Computing, ICDMIC 2014. https://doi.org/10.1109/ICDMIC.2014.6954228.

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Acknowledgements

I am currently pursuing my doctoral studies at IIT Roorkee in India, under the QIP fellowship program sponsored by M.J.P. Rohilkhand University, Bareilly. The research presented in this paper is an integral part of my doctoral work. I would like to express my sincere gratitude to my supervisor, Prof. Subhash Chander Sharma, from IIT Roorkee, for his invaluable support and guidance. Throughout my academic journey, Prof. Sharma’s unwavering enthusiasm, extensive knowledge, and meticulous attention to detail have been a constant source of inspiration. From my initial exploration of numerous books to the finalization of this research paper, his mentorship has played a pivotal role in shaping the current form of this work.

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Yadav, P., Sharma, S.C. Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches. Wireless Pers Commun 132, 2293–2362 (2023). https://doi.org/10.1007/s11277-023-10630-x

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