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Two-stage weighted centroid localization for large-scale wireless sensor networks in ambient intelligence environment

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Abstract

Wireless sensor networks (WSNs) are a necessary technology for the development of ambient intelligence (AmI) applications. One of the major concerns of designing WSNs for AmI applications is the localization of the sensor nodes. This paper proposes a new range-free localization algorithm for large-scale WSNs using received signal strength (RSS) measurements. Different from most RSS-based range-free localization in WSNs, the proposed two-stage weighted centroid localization (TS-WCL) algorithm utilizes the RSS measurements between pairs of anchors, except for the RSS measurements between the unknown node and anchor nodes. In the first stage of TS-WCL, the unknown node constructs a virtual neighboring anchor list by doing half-symmetric lens presence tests. A half-symmetric lens presence test determines a residence area of the unknown node using the locations of two neighboring anchors and the corresponding RSS measurements. The centroid of the estimated residence area is then regarded as a virtual neighboring anchor of the unknown node. In the second stage, a weighted centroid formula is used to estimate the location of the unknown node based on the virtual neighboring anchor list. Simulation results show that the proposed method has better performance compared to other three typical range-free localization methods.

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References

  • Atmojo UD, Salcic Z, Wang IK, Park H (2015) System-level approach to the design of ambient intelligence systems based on wireless sensor and actuator networks. Journal of Ambient Intelligence Humanized Computing 6:1–17

    Article  Google Scholar 

  • Bahl P, Padmanabhan VN (2000) RADAR: An in-building RF-based user location and tracking system. In: Proceedings Joint Conference IEEE Computer and Communications Societies, pp 775–784.

  • Blumenthal J, Grossmann R, Golatowski F, Timmermann D (2007) Weighted centroid localization in Zigbee-based sensor networks. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing, pp 1–6

  • Bo C, Ren D, Tang S, Li XY, Mao X, Huang Q, Mo L, Jiang Z, Sun Y, Liu Y (2012) Locating sensors in the forest: A case study in Green Orbs. In: Proceedings of IEEE International Conference on Computer Communications, pp 1026–1034

  • Bruckner D, Picus C, Velik R, Herzner W, Zucker G (2012) Hierarchical semantic processing architecture for smart sensors in surveillance networks. IEEE Trans Industrial Informatics 8(2):291–301

    Article  Google Scholar 

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

  • Chang H, Tian J, Lai T, Chu H, and Huang P (2008) Spinning beacons for precise indoor localization. In: Proceedings ACM Conference on Embedded Network Sensor Systems, pp 127–140

  • He T, Huang C, Blum BM, Stankovic JA, Abdelzaher TF (2003) Range-free localization schemes for large scale sensor networks. In: Proceedings of International Conference on Mobile Computing and Networking, pp 81–95

  • Hofmann-Wellenhof B, Lichtenegger H, Collins J (1997) Global positioning system: theory and practice. Springer, New York

    Google Scholar 

  • Iqbal M, Lim HB, Wang W, Yao Y (2008) A sensor grid infrastructure for large-scale ambient intelligence. In: Proceedings of International Conference on Parallel and Distributed Computing, Applications and Technologies, pp 468–473

  • Kulkarni RV, Forster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. IEEE Communications Surveys Tutorials 13(1):68–96

    Article  Google Scholar 

  • Lasla N, Younis MF, Ouadjaout A, Badache N (2015) An effective area-based localization algorithm for wireless networks. IEEE Trans Computers 64(8):2103–2118

    Article  MathSciNet  MATH  Google Scholar 

  • Liu C, Scott T, Wu K, Hoffman D (2007) Range-free sensor localization with ring overlapping based on comparison of received signal strength indicator. Int J Sensor Networks 2(5):399–413

    Article  Google Scholar 

  • Mao G, Fidan B, Anderson B (2007) Wireless sensor network localization techniques. Comput Networks 51(10):2529–2553

    Article  MATH  Google Scholar 

  • Mikulecky P (2012) Ambient intelligence for outdoor activities support: possibilities for large-scale wireless sensor networks applications. In: Proceedings of International Conference on Systems and Networks Communications, pp 213–217

  • Niculescu D, Nath B (2003) DV based positioning in Ad Hoc networks. Telecommun Systems 22(1): 267–280

  • Peng P, Rajamani R (2012) Flexible micro-tactile sensor for normal and shear elasticity measurements. IEEE Trans Industrial Informatics 59(12):4907–4913

    Article  Google Scholar 

  • Priyantha NB, Chakraborty A, Balakrishnan H (2000) Cricket location-support system. In: Proceedings of International Conference on Mobile Computing and Networking, pp 32–43

  • Rappaport T (2001) Wireless communications: principles and practice. Prentice-Hall, Upper Saddle River, NJ

    MATH  Google Scholar 

  • Savvides A, Han CC, Strivastava MB (2001) Dynamic fine-grained localization in ad-hoc networks of sensors. In: Proceedings of International Conference on Mobile Computing and Networking, pp 166–179

  • Shang Y, Ruml W, Zhang Y, Fromherz MPJ (2003) Localization from Mere Connectivity. In: Proceedings of ACM International Symposium on Mobile Ad Hoc Networking & Computing, pp 201–212

  • Sheu JP, Chen PC, Hsu CS (2008) A distributed localization scheme for wireless sensor networks with improved grid-scan and vector-based refinement. IEEE Trans Mobile Computing 7(9):1110–1123

    Article  Google Scholar 

  • 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 

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

    Article  Google Scholar 

  • Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Networks 52(12):2292–2330

    Article  Google Scholar 

  • Zhong Z, He T (2011) RSD: A metric for achieving range-free localization beyond connectivity. IEEE Trans on Parallel Distributed Systems 22(11):1943–1951

    Article  MathSciNet  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical Engineering, Ocean University of China, Qingdao, China

    Hao Zhang & Zengfeng Wang

  2. School of Information and Electrical Engineering, Ludong University, Yantai, China

    Zengfeng Wang

  3. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

    Hao Zhang & T. Aaron Gulliver

Authors
  1. Hao Zhang
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  2. Zengfeng Wang
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  3. T. Aaron Gulliver
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Correspondence to Zengfeng Wang.

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Zhang, H., Wang, Z. & Gulliver, T.A. Two-stage weighted centroid localization for large-scale wireless sensor networks in ambient intelligence environment. J Ambient Intell Human Comput 9, 617–627 (2018). https://doi.org/10.1007/s12652-017-0458-8

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  • DOI: https://doi.org/10.1007/s12652-017-0458-8

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