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An optimized sensor node localization approach for wireless sensor networks using RSSI

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Abstract

This article offers an efficient isosceles layout model for node deployment, and a parameter-less Jaya algorithm is also proposed as a solution to the sensor node localization issue in wireless sensor networks (WSNs). The proposed method bases its range measuring on the receive signal strength indicator (RSSI) method. The proposed layout ensures that target nodes are always within the anchor nodes’ transmission range, reducing the influence of RSSI interference and improving node localization accuracy. Compared to the earlier proposed square and equilateral layout models, the proposed model shows significant improvement. The performance of the earlier layout models and the proposed isosceles layout model for node localization is analyzed using a Jaya algorithm and compared with particle swarm optimization (PSO) and salp swarm algorithm (SSA)-based node localization algorithm. The proposed algorithm’s performance is evaluated using scalability and localization accuracy assessments. We investigate the influence of the degree of irregularity on localization accuracy for each layout. The proposed isosceles layout performs 14.16% and 8.56% better than the square and equilateral layout models for the proposed Jaya-based node localization algorithm. The proposed algorithm performs 7.16% and 4.24% better than the PSO- and SSA-based node localization algorithms for the isosceles layout model in WSNs for the same parameters. MATLAB simulation evaluates the proposed layout model and node localization algorithm.

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References

  1. Paul AK, Sato T (2017) Localization in wireless sensor networks: a survey on algorithms, measurement techniques, applications and challenges. J Sens Actuator Netw 6(4):24

    Article  Google Scholar 

  2. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114

    Article  Google Scholar 

  3. Saad E, Elhosseini M, Haikal AY (2018) Recent achievements in sensor localization algorithms. Alex Eng J 57(4):4219–4228

    Article  Google Scholar 

  4. Naureen A, Zhang N, Furber S, Shi Q (2020) A gps-less localization and mobility modelling (lmm) system for wildlife tracking. IEEE Access 8:102709–102732

    Article  Google Scholar 

  5. Han G, Jiang J, Zhang C, Duong TQ, Guizani M, Karagiannidis GK (2016) A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Commun Surv Tutor 18(3):2220–2243

    Article  Google Scholar 

  6. Khelifi F, Bradai A, Benslimane A, Rawat P, Atri M (2019) A survey of localization systems in internet of things. Mobile Netw Appl 24(3):761–785

    Article  Google Scholar 

  7. Sabale K, Mini S (2019) Anchor node path planning for localization in wireless sensor networks. Wirel Netw 25(1):49–61

    Article  Google Scholar 

  8. Kanwar V, Kumar A (2021) Dv-hop-based range-free localization algorithm for wireless sensor network using runner-root optimization. J Supercomput 77(3):3044–3061

    Article  Google Scholar 

  9. Zhang S, Liu X, Wang J, Cao J, Min G (2015) Accurate range-free localization for anisotropic wireless sensor networks. ACM Trans Sensor Netw (TOSN) 11(3):1–28

    Article  Google Scholar 

  10. Maruthi SP, Panigrahi T, Jagannath RPK (2020) Distributed version of hybrid swarm intelligence-nelder mead algorithm for doa estimation in wsn. Expert Syst Appl 144:113112

    Article  Google Scholar 

  11. Yu Z, Guo G (2017) Improvement of positioning technology based on rssi in zigbee networks. Wirel Pers Commun 95(3):1943–1962

    Article  Google Scholar 

  12. Amri S, Khelifi F, Bradai A, Rachedi A, Kaddachi ML, Atri M (2019) A new fuzzy logic based node localization mechanism for wireless sensor networks. Futur Gener Comput Syst 93:799–813

    Article  Google Scholar 

  13. Wang W, Liu X, Li M, Wang Z, Wang C (2019) Optimizing node localization in wireless sensor networks based on received signal strength indicator. IEEE Access 7:73880–73889

    Article  Google Scholar 

  14. Ullah I, Liu Y, Su X, Kim P (2019) Efficient and accurate target localization in underwater environment. IEEE Access 7:101415–101426

    Article  Google Scholar 

  15. Qi H, Mo L, Wu X (2020) Sdp relaxation methods for rss/aoa-based localization in sensor networks. IEEE Access 8:55113–55124

    Article  Google Scholar 

  16. Sabbella VR, Edla DR, Lipare A, Parne SR (2020) An efficient localization approach in wireless sensor networks using krill herd optimization algorithm. IEEE Syst J 15(2):2432–2442

    Article  Google Scholar 

  17. Sekhar P, Lydia EL, Elhoseny M, Al-Akaidi M, Selim MM, Shankar K (2021) An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication. Phys Commun 48:101411

    Article  Google Scholar 

  18. Khanna R, Kumar A et al (2022) Artificial intelligence applications for target node positions in wireless sensor networks using single mobile anchor node. Comput Ind Eng 167:107998

    Article  Google Scholar 

  19. Nain M, Goyal N, Awasthi LK, Malik A (2022) A range based node localization scheme with hybrid optimization for underwater wireless sensor network. Int J Commun Syst 35:5147

    Google Scholar 

  20. Rani S, Babbar H, Kaur P, Alshehri MD, Shah SHA (2022) An optimized approach of dynamic target nodes in wireless sensor network using bio inspired algorithms for maritime rescue. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2021.3129914

    Article  Google Scholar 

  21. Rao R (2016) Jaya : a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    MathSciNet  Google Scholar 

  22. Bianchi V, Ciampolini P, De Munari I (2018) Rssi-based indoor localization and identification for zigbee wireless sensor networks in smart homes. IEEE Trans Instrum Meas 68(2):566–575

    Article  Google Scholar 

  23. Booranawong A, Sengchuai K, Buranapanichkit D, Jindapetch N, Saito H (2021) Rssi-based indoor localization using multi-lateration with zone selection and virtual position-based compensation methods. IEEE Access 9:46223–46239

    Article  Google Scholar 

  24. Qin Q, Tian Y, Wang X (2021) Three-dimensional uwsn positioning algorithm based on modified rssi values. Mobile Inf Syst. https://doi.org/10.1155/2021/5554791

    Article  Google Scholar 

  25. Yang B, Guo L, Guo R, Zhao M, Zhao T (2020) A novel trilateration algorithm for rssi-based indoor localization. IEEE Sens J 20(14):8164–8172

    Article  Google Scholar 

  26. Jin R, Che Z, Xu H, Wang Z, Wang L (2015) An RSSI-based localization algorithm for outliers suppression in wireless sensor networks. Wirel Netw 21(8):2561–2569

    Article  Google Scholar 

  27. Livinsa ZM, Jayashri S (2013) Performance analysis of diverse environment based on rssi localization algorithms in wsns. In: 2013 IEEE Conference on Information and Communication Technologies. IEEE, pp. 572–576

  28. 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 

  29. Naguib A (2020) Multilateration localization for wireless sensor networks. Indian J Sci Technol 13(10):1213–1223

    Article  Google Scholar 

  30. Wadood A, Farkoush SG, Khurshaid T, Yu J-T, Kim C-H, Rhee S-B (2019) Application of the jaya algorithm in solving the problem of the optimal coordination of overcurrent relays in single-and multi-loop distribution systems. Complexity. https://doi.org/10.1155/2019/5876318

    Article  Google Scholar 

  31. Goudos SK, Yioultsis TV, Boursianis AD, Psannis KE, Siakavara K (2019) Application of new hybrid jaya grey wolf optimizer to antenna design for 5g communications systems. IEEE Access 7:71061–71071

    Article  Google Scholar 

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Acknowledgements

Thank you to all the anonymous reviewers for providing valuable suggestions in order to create a better version of the proposed article.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Shilpi, Prateek Raj Gautam & Arvind Kumar

  2. Department of Electronics and Communication Engineering, KL University, Vijayawada, India

    Sunil Kumar

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  1. Shilpi
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  2. Prateek Raj Gautam
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  3. Sunil Kumar
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  4. Arvind Kumar
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Correspondence to Shilpi.

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Shilpi, Gautam, P.R., Kumar, S. et al. An optimized sensor node localization approach for wireless sensor networks using RSSI. J Supercomput 79, 7692–7716 (2023). https://doi.org/10.1007/s11227-022-04971-w

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