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Proposing a measurement criterion to evaluate the border problem in localization algorithms in WSNs

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Abstract

Localization algorithms are one of the most important protocols that may be used in many different fields of wireless sensor networks. The border problem is a considerable challenge for localization algorithms. In this regard, the sensor nodes that are placed on the boundary of a deployment area have a larger localization error as compared to the other sensor nodes. In this article, a new criterion is proposed for measuring the impact of the border problem on the performance of localization algorithms in isotropic networks. The performance of some range-free localization algorithms is studied by simulation. The results show that, contrary to LSVM and NN algorithms, the impacts of the border problem are reduced by a reduction in the dimensions of the deployment environment in the DV-hop algorithm. Besides, the effect of the border problem on the performance of the localization algorithms can be reduced by an increase in the number of anchor nodes. On the other hand, the number of sensor nodes does not have a significant impact on reducing the effect of the border problem in localization algorithms. Finally, a solution is proposed to reduce the negative impact of this issue on the performance of localization methods.

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References

  1. Bhuiyan MZA, Wang G, Cao J, Wu J (2014) Sensor placement with multiple objectives for structural health monitoring. ACM Trans Sensor Netw 10(4):68

    Article  Google Scholar 

  2. Navarro M, Davis TW, Liang Y, Liang X (2013) ASWP: a long-term WSN deployment for environmental monitoring. In: 12th international conference on Information processing in sensor networks, pp 351–352

  3. Blumrosen G, Hod B, Anker T, Dolev D, Rubinsky B (2013) Enhancing RSSI-based tracking accuracy in wireless sensor networks. ACM Trans Sensor Netw 9(3):29

    Article  Google Scholar 

  4. Misra S, Singh S (2012) Localized policy-based target tracking using wireless sensor networks. ACM Trans Sensor Netw (TOSN) 8(3):27

    Google Scholar 

  5. Liao Z, Wang J, Zhang S, Cao J, Min G (2015) Minimizing movement for target coverage and network connectivity in mobile sensor networks. IEEE Trans Parallel Distrib Syst 26(7):1971–1983

    Article  Google Scholar 

  6. Zhu Y, Huang M, Chen S, Wang Y (2012) Energy-efficient topology control in cooperative ad hoc networks. IEEE Trans Parallel Distrib Syst 23(8):1480–1491

    Article  Google Scholar 

  7. He S, Chen J, Sun Y (2012) Coverage and connectivity in duty-cycled wireless sensor networks for event monitoring. IEEE Trans Parallel Distrib Syst 23(3):475–482

    Article  Google Scholar 

  8. Halder S, Ghosal A (2016) A survey on mobile anchor assisted localization techniques in wireless sensor networks. Wirel Netw 22(7):2317–2336

    Article  Google Scholar 

  9. Bulusu N, Heidemann J, Estrin D (2000) GPS-less low-cost outdoor localization for very small devices. IEEE Pers Commun 7(5):28–34

    Article  Google Scholar 

  10. Cheung KW, So H-C, Ma W-K, Chan Y-T (2004) Least squares algorithms for time-of-arrival-based mobile location. IEEE Trans Signal Process 52(4):1121–1130

    Article  MathSciNet  Google Scholar 

  11. Rong P, Sichitiu ML (2006) Angle of arrival localization for wireless sensor networks. In: 3rd annual IEEE communications society on sensor and ad hoc communications and networks, pp 374–382

  12. Tran DA, Nguyen T (2008) Localization in wireless sensor networks based on support vector machines. IEEE Trans Parallel Distrib Syst 19(7):981–994

    Article  Google Scholar 

  13. Zhao C, Xu Y, Huang H (2014) Weighted centroid localization based on compressive sensing. Wirel Netw 20(6):1527–1540

    Article  Google Scholar 

  14. Afzal S, Beigy H (2014) A localization algorithm for large scale mobile wireless sensor networks: a learning approach. J Supercomput 69(1):98–120

    Article  Google Scholar 

  15. Niculescu D, Nath B (2003) DV based positioning in ad hoc networks. Telecommun Syst 22(1–4):267–280

    Article  Google Scholar 

  16. Zhang Z, Gou X, Li Y, Huang S (2009) DV-hop based self-adaptive positioning in wireless sensor networks. In: 5th international conference on wireless communications, networking and mobile computing, pp 1–4

  17. Hou S, Zhou X, Liu X (2010) A novel DV-hop localization algorithm for asymmetry distributed wireless sensor networks. In: 3rd IEEE international conference on computer science and information technology, pp 243–248

  18. Lee J, Chung W, Kim E, Hong IW (2010) Robust DV-hop algorithm for localization in wireless sensor network. In: International conference on control automation and systems, pp 2506–2509

  19. Liu X, Zhang S, Bu K (2016) A locality-based range-free localization algorithm for anisotropic wireless sensor networks. Telecommun Syst 62(1):3–13

    Article  Google Scholar 

  20. Kumar S, Lobiyal DK (2016) Novel DV-Hop localization algorithm for wireless sensor networks. Telecommun Syst 64(3):509–524

    Article  Google Scholar 

  21. Yang X, Kong Q, Dai X (2010) An improved weighted centroid location algorithm. J Xi’an Jiaotong Univ 8:002

    Google Scholar 

  22. Wang J, Urriza P, Han Y, Cabric D (2011) Weighted centroid localization algorithm: theoretical analysis and distributed implementation. IEEE Trans Wirel Commun 10(10):3403–3413

    Article  Google Scholar 

  23. Chatterjee A (2010) A fletcher–reeves conjugate gradient neural-network-based localization algorithm for wireless sensor networks. IEEE Trans Veh Technol 59(2):823–830

    Article  Google Scholar 

  24. So-In C, Permpol S, Rujirakul K (2016) Soft computing-based localizations in wireless sensor networks. Pervasive Mob Comput 29:17–37

    Article  Google Scholar 

  25. Yan X, Yang Z, Song A, Yang W, Liu Y, Zhu R (2016) A novel multihop range-free localization based on kernel learning approach for the internet of things. Wirel Pers Commun 87(1):269–292

    Article  Google Scholar 

  26. V-s Feng, Chang SY (2012) Determination of wireless networks parameters through parallel hierarchical support vector machines. IEEE Trans Parallel Distrib Syst 23(3):505–512

    Article  Google Scholar 

  27. Lee J, Choi B, Kim E (2013) Novel range-free localization based on multidimensional support vector regression trained in the primal space. IEEE Trans Neural Netw Learn Syst 24(7):1099–1113

    Article  Google Scholar 

  28. Lee J, Chung W, Kim E (2013) A new kernelized approach to wireless sensor network localization. Inf Sci 243:20–38

    Article  MathSciNet  Google Scholar 

  29. Lee S, Jin M, Koo B, Sin C, Kim S (2016) Pascal’s triangle-based range-free localization for anisotropic wireless networks. Wirel Netw 22(7):2221–2238

    Article  Google Scholar 

  30. Banihashemian SS, Adibnia F, Sarram MA (2018) A new range-free and storage-efficient localization algorithm using neural networks in wireless sensor networks. Wirel Pers Commun 98:1547–1568

    Article  Google Scholar 

  31. Xiao Q, Xiao B, Cao J, Wang J (2010) Multihop range-free localization in anisotropic wireless sensor networks: a pattern-driven scheme. IEEE Trans Mob Comput 9(11):1592–1607

    Article  Google Scholar 

  32. Qian Q, Shen X, Chen H (2011) An improved node localization algorithm based on DV-Hop for wireless sensor networks. Comput Sci Inf Syst 8(4):953–972

    Article  Google Scholar 

Download references

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Correspondence to Fazlollah Adibnia.

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Banihashemian, S.S., Adibnia, F. & Sarram, M.A. Proposing a measurement criterion to evaluate the border problem in localization algorithms in WSNs. Computing 100, 1251–1272 (2018). https://doi.org/10.1007/s00607-018-0612-y

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  • DOI: https://doi.org/10.1007/s00607-018-0612-y

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