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RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks

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Abstract

The fact that sensor nodes are situated in specific places is most obvious in wireless sensor networks. A wireless sensor network has been designed to address node localization through several solutions. Environmental monitoring, healthcare, and industrial automation are just some of the fields where wireless sensor networks (WSNs) find extensive application. This sort of WSN application is fundamentally dependent on the accurate localization of sensor nodes. The purpose of this paper is to propose an enhanced received signal strength indicator (RSSI) technique for determining unlocalized nodes using anchor nodes. An optimal communication range, aligning with the application needs, can be determined by assessing the dynamic correlation between communication distance and the RSSI throughout the localization procedure. The advanced RSSI-based node localization method proposed in this study involves two distinct stages: the distance determination phase and the computation phase. In this work, we assess the outcome using the Castalia simulation. The precision of wireless sensor network node location is greatly improved by integrating mobile anchor nodes with RSSI-based dynamic node localization. The results of the simulations and the subsequent analysis attest to the excellent accuracy and low energy consumption of the suggested method during the localization procedure.

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References

  1. Lorincz, K., Malan, D. J., Fulford-Jones, T. R., Nawoj, A., Clavel, A., Shnayder, V., Mainland, G., Welsh, M., & Moulton, S. (2004). Sensor networks for emergency response: Challenges and opportunities. IEEE Pervasive Computing, 3(4), 16–23.

    Article  Google Scholar 

  2. Kanoosh, H. M., Houssein, E. H., & Selim, M. M. (2019). Salp swarm algorithm for node localization in wireless sensor networks. Journal of Computer Networks and Communications, 2019, 1–12.

    Article  Google Scholar 

  3. Kumar, A., & Prashar, D. (2018). A novel approach for node localization in wireless sensor networks. In Intelligent communication, control and devices (pp. 419–428). Springer.

  4. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). September. Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th annual international conference on Mobile computing and networking (pp. 81–95).

  5. Kulkarni, V. R., Desai, V., & Kulkarni, R. V. (2019). A comparative investigation of deterministic and metaheuristic algorithms for node localization in wireless sensor networks. Wireless Networks, 25(5), 2789–2803.

    Article  Google Scholar 

  6. Rout, S., Mohapatra, P., Rath, A., & Sahu, B. (2022). Node localization in wireless sensor networks using dynamic genetic algorithm. Journal of Applied Research and Technology, 20(5), 520–528.

    Article  Google Scholar 

  7. Phoemphon, S., So-In, C., & Leelathakul, N. (2020). A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks. Expert Systems with Applications, 143, 113044.

    Article  Google Scholar 

  8. Rout, S. K., Rath, A. K., & Rout, B. R. (2016). Efficient energy utilization and node localization in dynamic DV-Hop algorithm for wireless sensor networks. Indian Journal of Science and Technology, 9, 30.

    Article  Google Scholar 

  9. Lin, Y., Tao, H., Tu, Y., & Liu, T. (2019). A node self-localization algorithm with a mobile anchor node in underwater acoustic sensor networks. IEEE Access, 7, 43773–43780.

    Article  Google Scholar 

  10. Alomari, A., Comeau, F., Phillips, W., & Aslam, N. (2018). New path planning model for mobile anchor-assisted localization in wireless sensor networks. Wireless Networks, 24(7), 2589–2607.

    Article  Google Scholar 

  11. Kumar, A., Khosla, A., Saini, J. S., & Singh, S. (2012). September. Computational intelligence based algorithm for node localization in wireless sensor networks. In 2012 6th IEEE international conference intelligent systems (pp. 431–438). IEEE.

  12. Chang, S., Li, Y., Wang, H., Hu, W., & Wu, Y. (2018). RSS-based cooperative localization in wireless sensor networks via second-order cone relaxation. IEEE Access, 6, 54097–54105.

    Article  Google Scholar 

  13. Patwari, N., Hero III, A. O. (2003). Using proximity and quantized RSS for sensor localization in wireless networks, WSNA’03, September 19, 2003, San Diego, California, USA.

  14. Wang, Y., Goddard, S., & Perez, L. C. (2007). A study on the cricket location-support system communication protocols. In 2007 IEEE international conference on electro/information technology (pp. 257–262). IEEE.

  15. Yedavalli, K., Krishnamachari, B., Ravula, S., & Srinivasan, B. (2005). Ecolocation: a sequence based technique for RF localization in wireless sensor networks. In IPSN 2005. Fourth international symposium on information processing in sensor networks, 2005 (pp. 285–292). IEEE.

  16. Yang, J., Cai, Y., Tang, D., & Liu, Z. (2019). A novel centralized range-free static node localization algorithm with memetic algorithm and Lévy flight. Sensors, 19(14), 3242.

    Article  Google Scholar 

  17. Niu, R., Vempaty, A., & Varshney, P. K. (2018). Received-signal-strength-based localization in wireless sensor networks. Proceedings of the IEEE, 106(7), 1166–1182.

    Article  Google Scholar 

  18. Chen, H., Gao, F., Martins, M., Huang, P., & Liang, J. (2013). Accurate and efficient node localization for mobile sensor networks. Mobile Networks and Applications, 18, 141–147.

    Article  Google Scholar 

  19. Wang, Z., Zhang, H., Lu, T., & Gulliver, T. A. (2018). Cooperative RSS-based localization in wireless sensor networks using relative error estimation and semidefinite programming. IEEE Transactions on Vehicular Technology, 68(1), 483–497.

    Article  Google Scholar 

  20. Hehdly, K., Laaraiedh, M., Abdelkefi, F., & Siala, M. (2019). Cooperative localization and tracking in wireless sensor networks. International Journal of Communication Systems, 32(1), e3842.

    Article  Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. Qi, Q., Li, Y., Wu, Y., Wang, Y., Yue, Y., & Wang, X. (2019). RSS-AOA-based localization via mixed semi-definite and second-order cone relaxation in 3-D Wireless Sensor Networks. IEEE Access, 7, 117768–117779.

    Article  Google Scholar 

  23. Sneha, V., & Nagarajan, M. (2020). Localization in wireless sensor networks: A review. Cybernetics and Information Technologies, 20(4), 3–26.

    Article  Google Scholar 

  24. Miloud, M., Abdellatif, R., & Lorenz, P. (2019). Moth flame optimization algorithm range-based for node localization challenge in decentralized wireless sensor network. International Journal of Distributed Systems and Technologies (IJDST), 10(1), 82–109.

    Article  Google Scholar 

  25. Sun, Y., Yuan, Y., Xu, Q., Hua, C., & Guan, X. (2019). A mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks. Sensors, 19(20), 4369.

    Article  Google Scholar 

  26. Nguyen, T. L. N., Vy, T. D., & Shin, Y. (2019). An efficient hybrid RSS-AoA localization for 3D wireless sensor networks. Sensors, 19(9), 2121.

    Article  Google Scholar 

  27. Javadi, S. H., Moosaei, H., & Ciuonzo, D. (2019). Learning wireless sensor networks for source localization. Sensors, 19(3), 635.

    Article  Google Scholar 

  28. Madhumathi, K., & Suresh, T. (2020). Node localization in wireless sensor networks using multi-output random forest regression. In Soft computing for problem solving (pp. 177–186). Springer.

  29. Dolha, S., Negirla, P., Alexa, F., & Silea, I. (2019). Considerations about the signal level measurement in wireless sensor networks for node position estimation. Sensors, 19(19), 4179.

    Article  Google Scholar 

  30. Yu, L., Haipeng, W., You, H., & Jian, S. (2013). A novel hybrid node localization algorithm for wireless sensor networks. Journal of Computer Science, 9, 1747–1760.

    Article  Google Scholar 

  31. Tuba, E., Tuba, M., & Beko, M., 2018. Two stage wireless sensor node localization using firefly algorithm. In Smart trends in systems, security and sustainability (pp. 113–120). Springer.

  32. Rout, S. K., Rath, A. K., Bhagabati, C., & Mohapatra, P. K. (2016). Node localization by using fuzzy optimization technique in wireless sensor networks. In 2016 International conference on information technology (InCITe)-the next generation it summit on the theme-internet of things: connect your worlds (pp. 176–181). IEEE.

  33. Luomala, J., & Hakala, I. (2019). Analysis and evaluation of adaptive RSSI-based ranging in outdoor wireless sensor networks. Ad Hoc Networks, 87, 100–112.

    Article  Google Scholar 

  34. Jondhale, S. R., Wakchaure, M. A., Agarkar, B. S., & Tambe, S. B. (2022). Improved generalized regression neural network for target localization. Wireless Personal Communications, 125(2), 1677–1693.

    Article  Google Scholar 

  35. Jondhale, S. R., Jondhale, A. S., Deshpande, P. S., & Lloret, J. (2021). Improved trilateration for indoor localization: Neural network and centroid-based approach. International Journal of Distributed Sensor Networks, 17(11), 15501477211053996.

    Article  Google Scholar 

  36. Bhat, S. J., & Santhosh, K. V. (2020). Is localization of wireless sensor networks in irregular fields a challenge? WirelessPersonal Communications, 114, 2017–2042.

    Google Scholar 

  37. Rout, S. K., Rath, A. K., & Bhagabati, C. (2016). Energy efficient and cost effective secure node localization with key management in wireless sensor networks. In 2016 5th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO) (pp. 515–520). IEEE.

  38. Yan, Z., Mukherjee, A., Yang, L., Routray, S., & Palai, G. (2019). Energy-efficient node positioning in optical wireless sensor networks. Optik, 178, 461–466.

    Article  Google Scholar 

  39. Thilagavathi, P., & Manickam, J. M. L. (2021). ERTC: An enhanced RSSI based tree climbing mechanism for well-planned path localization in WSN using the virtual force of mobile anchor node. Journal of Ambient Intelligence and Humanized Computing, 12, 6665–6676.

    Article  Google Scholar 

  40. Tseng, C. L., Cheng, C. S., Ruan, Z. Y., Lee, R. G., & Lee, C. Y. (2020). An improved EKF localization method with rssi aid for mobile wireless sensor networks. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3364–3369). IEEE.

  41. Strumberger, 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). IEEE.

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Correspondence to M. Ijaz Khan.

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Kumar, K.S., Rout, S.K., Panda, S.K. et al. RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks. Telecommun Syst 87, 137–149 (2024). https://doi.org/10.1007/s11235-024-01183-w

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