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Wireless sensor network minimum beacon set selection algorithm based on tree model

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Abstract

Wireless sensor networks (WSNs) are highly attractive both in academia and in practice as a wholly new platform for information transmission. Localization technology is a key technology of WSNs. The structure of the beacon node set is very important to the positioning of the nodes. A method for constructing a minimum beacon set is proposed in this thesis based on the tree model, in which unimportant nodes are identified as early as possible and then pruned. Thus, we avoid unnecessary calculations when establishing the minimum beacon set. This method can provide a reliable guarantee for the unknown node localization. According to our experiment, this algorithm is rapid and stable.

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References

  1. Chaturvedi P, Daniel AK (2014) Wireless sensor networks-a survey. In: International conference on recent trends in information, telecommunication and computing, pp 450–457

  2. Hart JK, Martinez K (2006) Environmental sensor networks: a revolution in the earth system science? Earth Sci Rev 78(3):177–191

    Article  Google Scholar 

  3. Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks: technology, protocols, and applications. Wiley, New York

    Book  Google Scholar 

  4. Kemis H, Bruce N, Wang P, Antonio T (2012) Healthcare monitoring application in ubiquitous sensor network: design and implementation based on pulse sensor with arduino. In: 6th international conference on new trends in information science and service science and data mining (ISSDM), pp 34–38

  5. Rabaey JM, Ammer JM, Danny P, Shad R (2000) Pico radio supports Ad hoc ultra-low power wireless networking. IEEE Comput 33(7):42–48

    Article  Google Scholar 

  6. Kumar K, Liu J, Lu YH, Bhargava B (2013) A survey of computation offloading for mobile systems. Mob Netw Appl 18(1):129–140

    Article  Google Scholar 

  7. Kumarasiri R, Alshamaileh K, Tran NH, Devabhaktuni V (2015) An improved hybrid RSS/TDOA wireless ensors localization technique utilizing wi-fi networks. Mob Netw Appl 21(20):286–295

    Google Scholar 

  8. Intanagonwiwat C, Govindan R, Estrin D, Heidemann J (2003) Directed diffusion for wireless sensor networking. IEEE ACM Trans Netw 11(1):2–16

    Article  Google Scholar 

  9. He J, Geng YS, Wan YD, Li S, Pahlavan K (2013) A cyber physical test-bed for virtualization of RF access environment for body sensor network. IEEE Sens J 13(10):3826–3836

    Article  Google Scholar 

  10. Geng YS, Chen J, Fu RJ, Bao GQ, Pahlavan K (2016) Enlighten wearable physiological monitoring systems: on-body RF characteristics based human motion classification using a support vector machine. IEEE Trans Mob Comput 15(3):656–671

    Article  Google Scholar 

  11. Romer K, Mattern K (2004) The design space of wireless sensor networks. IEEE Wirel Commun 11(6):54–61

    Article  Google Scholar 

  12. Akyildiz IF, Su W, Sankarasubramaniam Y (2002) Wireless sensor network: a survey. Comput Netw 38(4):342–393

    Article  Google Scholar 

  13. Chang DC, Fang MW (2014) Bearing-only maneuvering mobile tracking with nonlinear filtering algorithms in wireless sensor networks. IEEE Syst J 8(1):160–170

    Article  Google Scholar 

  14. Kay S, Vankayalapati N (2013) Improvement of TDOA position fixing using the likelihood curvature. IEEE Trans Signal Process 61(8):1910–1914

    Article  MathSciNet  Google Scholar 

  15. Kottas A, Wang Z, Rodrguez A (2012) Spatial modeling for risk assessment of extreme values from environmental time series: a Bayesian non-parametric approach. Environ Metr 23(8):649–662

    Google Scholar 

  16. Storn R, Price K (1997) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. J Glo Optim 11:341–359

    Article  MATH  Google Scholar 

  17. Meng W, Xiao W, Xie L (2011) An efficient EM algorithm for energy-based multisource localization in wireless sensor networks. IEEE Trans Instrum Meas 60(3):1017–1027

    Article  Google Scholar 

  18. Ampeliotis D, Berberidis K (2010) Low complexity multiple acoustic source localization in sensor networks based on energy measurements. Signal Process 90(4):1300–1312

    Article  MATH  Google Scholar 

  19. Kumar S, Lobiyal DK (2014) Power efficient range-free localization algorithm for wireless sensor networks. Wirel Netw 20(4):681–694

    Article  Google Scholar 

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

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Acknowledgements

The research is supported by major scientific and technological projects of Fujian Province China (No. 2011H6027), National Natural Science Foundation of China (Nos. 61503316, 51404007).

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Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, 361005, China

    Bin Wu & Jian Luo

  2. School of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, China

    Chaoyu Yang

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  1. Bin Wu
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  2. Jian Luo
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  3. Chaoyu Yang
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Correspondence to Bin Wu.

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Wu, B., Luo, J. & Yang, C. Wireless sensor network minimum beacon set selection algorithm based on tree model. Neural Comput & Applic 30, 965–976 (2018). https://doi.org/10.1007/s00521-016-2734-5

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  • DOI: https://doi.org/10.1007/s00521-016-2734-5

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