A New Receiver Placement Scheme Using Delaunay Refinement-based Triangulation

被引:0
|
作者
Basheer, Mohammed Rana [1 ]
Jagannathan, S. [1 ]
机构
[1] Missouri Univ Sci & Technol, Dept Elect & Comp Engn, Rolla, MO 65409 USA
关键词
Delaunay refinement; Constrained Weighted Least Squares; Received Signal Strenght Indicator; Optimal placement;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, a sub-optimal solution to the receiver placement and number of receiver determination problem is introduced. To achieve this overall goal, first, localization error for a received signal strength indicator (RSSI)-based N-receiver system localizing a transmitter is estimated. Subsequently, this estimator error along with the 2D-tessellation techniques such as Delaunay refinement are used to position candidate receivers not only to minimize their number needed to meet the location error threshold but also to reduce the dilution of localization accuracy due to the layout of receivers. Rigorous mathematical analysis indicates that the receiver count generated by our Delaunay refinement-based suboptimal solution using triangular tiles is indeed bounded from the optimal count by a constant which in turn depends upon the workspace layout. Finally, the sub-optimal scheme is demonstrated by using experimental data.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Chew's Second Delaunay Triangulation Refinement Scheme for Optimal RSUs Deployment to Ensure Maximum Connectivity in Vehicle to Infrastructure Communication
    Selvakumari, P.
    Sheela, D.
    Chinnasamy, A.
    WIRELESS PERSONAL COMMUNICATIONS, 2022, 123 (01) : 375 - 405
  • [32] Chew’s Second Delaunay Triangulation Refinement Scheme for Optimal RSUs Deployment to Ensure Maximum Connectivity in Vehicle to Infrastructure Communication
    P. Selvakumari
    D. Sheela
    A. Chinnasamy
    Wireless Personal Communications, 2022, 123 : 375 - 405
  • [33] Recognizing Linear Building Patterns in Topographic Data by Using Two New Indices based on Delaunay Triangulation
    He, Xianjin
    Deng, Min
    Luo, Guowei
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (04)
  • [34] Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks
    Qin, Junping
    Sun, Shiwen
    Deng, Qingxu
    Liu, Limin
    Tian, Yonghong
    SENSORS, 2017, 17 (06)
  • [35] Novel parallel algorithm for constructing Delaunay triangulation based on a twofold-divide-and-conquer scheme
    Wu, Wenzhou
    Rui, Yikang
    Su, Fenzhen
    Cheng, Liang
    Wang, Jiechen
    GISCIENCE & REMOTE SENSING, 2014, 51 (05) : 537 - 554
  • [36] A new approach for automating land partitioning using binary search and Delaunay triangulation
    Hakli, Huseyin
    Uguz, Harun
    Cay, Tayfun
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2016, 125 : 129 - 136
  • [37] A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method
    Nasirahmadi, A.
    Hensel, O.
    Edwards, S. A.
    Sturm, B.
    ANIMAL, 2017, 11 (01) : 131 - 139
  • [38] Classification of Endoscopic Images Using Delaunay Triangulation-Based Edge Features
    Haefner, M.
    Gangl, A.
    Liedlgruber, M.
    Uhl, A.
    Vecsei, A.
    Wrba, F.
    IMAGE ANALYSIS AND RECOGNITION, 2010, PT II, PROCEEDINGS, 2010, 6112 : 131 - +
  • [39] Clothing segmentation using foreground and background estimation based on the constrained Delaunay triangulation
    Hu, Zhilan
    Yan, Hong
    Lin, Xinggang
    PATTERN RECOGNITION, 2008, 41 (05) : 1581 - 1592
  • [40] Using Duality and Hopfield Neural Network for Delaunay Triangulation based Fingerprint Matching
    Ahmadian, Kushan
    Gavrilova, Marina
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTING, ENGINEERING AND INFORMATION, 2009, : 225 - 230