A distributed sensor nodes localization algorithm based on supernodes

JIANG Jun-zheng1 ZHAO Hai-bing1

(1.School of Information and Communication, Guilin University of Electronic Technology, Guilin, Guangxi Autonomous Region, China 541004)
【Knowledge Link】self-organizing network

【Abstract】A distributed algorithm based on supernodes is proposed to iteratively locate the large number of sensor nodes in wireless sensor networks. In terms of the overlapped decomposition of the entire network, two steps are involved at each iteration of the algorithm: one is the node localization method within each subgraph and the other is the local consensus among neighbor subgraphs. To be specific, the conjugate gradient method is employed to determine the position of nodes in subgraphs. Then, the position of each node will be adjusted by a neighboring consensus strategy. These two steps are repeated until the iterative termination condition is satisfied. Simulation results show that the proposed algorithm is an order of magnitude lower positioning error than the existing distributed algorithms and can efficiently locate nodes in large-size wireless sensor networks.

【Keywords】 wireless sensor network; localization; distributed; iteration; supernodes; conjugate gradient method; fusion between subgraphs;

【DOI】

【Funds】 National Natural Science Foundation of China (61761011) Natural Science Foundation of Guangxi (2017GXNSFAA198173)

Download this article

    References

    [1] Yu X S, Wang Y, Meng Y N, et al. Non-line of sight node localization method based on IMM-IKF for wireless sensor networks [J]. Control and Decision, 2018, 33 (6): 1069–1074 (in Chinese).

    [2] Yu X W, Zhou L X, Yu Q H, et al. Localization algorithm for mine wireless sensor network based on rigid cluster and chicken swarm optimization [J]. Journal of Southwest Jiaotong University, 2019, 54 (4): 870–878 (in Chinese).

    [3] Mofarreh-Bonab M, Ghorashi S A. A low complexity and high speed gradient descent based secure localization in wireless sensor networks [C]. International Conference on Computer and Knowledge Engineering. Mashhad, 2013: 300–303.

    [4] Keller Y, Gur Y. A diffusion approach to network localization [J]. IEEE Transactions on Signal Processing, 2011, 59 (6): 2642–2654.

    [5] Patwari N, Ash J N, Kyperountas S, et al. Locating the nodes: Cooperative localization in wireless sensor networks [J]. IEEE Signal Processing Magazine, 2005, 22 (4): 54–69.

    [6] He J, Wu D Y, Li X F, et al. A 3-D indoor localization algorithm using distance optimization [J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40 (3): 37–42 (in Chinese).

    [7] Liu H, Han Y B, Zhang S B, et al. WSNs localization algorithm based on self-adaptive penalty function optimization particle swarm optimization [J]. Chinese Journal of Sensors and Actuators, 2018, 31 (8): 1253–1257 (in Chinese).

    [8] Tomic S, Beko M, Rui D. RSS-based localization in wireless sensor networks using convex relaxation: Noncooperative and cooperative schemes [J]. IEEETransactions on Vehicular Technology, 2014, 64 (5): 2037–2050.

    [9] Biswa P, Liang T C, Toh K C, et al. Semidefinite programming approaches for sensor network localization with noisy distance measurements [J]. IEEE Transactions on Automation Science & Engineering, 2006, 3 (4): 360–371.

    [10] Jiang J Z, Zhao H B, Li Y J, et al. A method for sensor nodes localization based on second-order Taylor approximation [P]. China: 201810438409. X, 2018–10–12 (in Chinese).

    [11] Srirangarajan S, Tewfik A, Luo Z Q. Distributed sensor network localization using SOCP relaxation [J]. IEEETransactions on Wireless Communications, 2008, 7 (12): 4886–4895.

    [12] Soares C, Xavier J, Gomes J. Simple and fast convex relaxation method for cooperative localization in sensor networks using range measurements [J]. IEEETransactions on Signal Processing, 2015, 63 (17): 4532–4543.

    [13] Luo Y Q Z, Liao W. RSSI range optimization newton localization algorithm based on particle filter [J]. Instrument Technique and Sensor, 2017 (6): 116–119 (in Chinese).

    [14] Wang F. RF RSS-based clustering and multi-sensor integration location algorithm [J]. Computer Engineering and Design, 2018, 39 (6): 1553–1558 (in Chinese).

    [15] Chen B L. Convex optimization theory and algorithm [M]. Beijing: Tsinghua University Press, 2005: 291–306 (in Chinese).

    [16] Jiang J Z. Design algorithms of DFT modulated filter banks [D]. Xi’an: National Lab of Radar Signal Processing, Xidian University, 2011: 21–28 (in Chinese).

This Article

ISSN:1001-0920

CN: 21-1124/TP

Vol 35, No. 12, Pages 2898-2906

December 2020

Downloads:0

Share
Article Outline

Knowledge

Abstract

  • 0 Introduction
  • 1 Description of the location problem
  • 2 Distributed localization algorithm
  • 3 Simulation results and analysis
  • 4 Conclusions
  • References