• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Zhou Yong, Xia Shixiong, Ding Shifei, Zhang Lei, Ao Xin. An Improved APIT Node Self-Localization Algorithm in WSN Based on Triangle-Center Scan[J]. Journal of Computer Research and Development, 2009, 46(4): 566-574.
Citation: Zhou Yong, Xia Shixiong, Ding Shifei, Zhang Lei, Ao Xin. An Improved APIT Node Self-Localization Algorithm in WSN Based on Triangle-Center Scan[J]. Journal of Computer Research and Development, 2009, 46(4): 566-574.

An Improved APIT Node Self-Localization Algorithm in WSN Based on Triangle-Center Scan

More Information
  • Published Date: April 14, 2009
  • Node self-localization is one of the important research topics in WSN. APIT is a major range-free localization algorithm. Compared with other range-free algorithms, APIT can achieve higher precision position estimation with small communication cost. However, APIT requires high anchor node density. Besides, in the process of APIT test, boundary effect and low neighbor node density can easily increase InToOut error and OutToIn error. Otherwise, the grid scan algorithm is inefficient and has a lower fault-tolerance to OutToIn error. In allusion to the problems mentioned above, an improved APIT algorithm based on triangle-center scan is proposed. Firstly, the reason for InToOut error and OutToIn error is analyzed and two improvements of APIT are introduced. Then, the effect of grid scan algorithm on the precision of position estimation and the algorithms efficiency are analyzed, and a triangle-center scan algorithm is presented. Finally, simulation results show that the improved algorithm not only can reduce the IntoOut error and OutToIn error effectively and improve the precision of position estimation, but also has a higher fault-tolerance to OutToIn error and can enhance the algorithms efficiency.
  • Related Articles

    [1]Ji Zhong, Nie Linhong. Texture Image Classification with Noise-Tolerant Local Binary Pattern[J]. Journal of Computer Research and Development, 2016, 53(5): 1128-1135. DOI: 10.7544/issn1000-1239.2016.20148320
    [2]Lu Daying, Zhu Dengming, Wang Zhaoqi. Texture-Based Multiresolution Flow Visualization[J]. Journal of Computer Research and Development, 2015, 52(8): 1910-1920. DOI: 10.7544/issn1000-1239.2015.20140417
    [3]Wang Huafeng, Wang Yuting, Chai Hua. State-of-the-Art on Texture-Based Well Logging Image Classification[J]. Journal of Computer Research and Development, 2013, 50(6): 1335-1348.
    [4]Zhong Hua,Yang Xiaoming, and Jiao Licheng. Texture Classification Based on Multiresolution Co-occurrence Matrix[J]. Journal of Computer Research and Development, 2011, 48(11): 1991-1999.
    [5]Xiong Changzhen, Huang Jing, Qi Dongxu. Irregular Patch for Texture Synthesis[J]. Journal of Computer Research and Development, 2007, 44(4): 701-706.
    [6]Li Jie, Zhu Weile, Wang Lei. Texture Recognition Using the Wold Model and Support Vector Machines[J]. Journal of Computer Research and Development, 2007, 44(3).
    [7]Xu Cunlu, Chen Yanqiu, Lu Hanqing. Statistical Landscape Features for Texture Retrieval[J]. Journal of Computer Research and Development, 2006, 43(4): 702-707.
    [8]Yang Gang, Wang Wencheng, Wu Enhua. Texture Synthesis by the Border Image[J]. Journal of Computer Research and Development, 2005, 42(12): 2118-2125.
    [9]Shang Zhaowei, Zhang Mingxin, Zhao Ping, Shen Junyi. Different Complex Wavelet Transforms for Texture Retrieval and Similarity Measure[J]. Journal of Computer Research and Development, 2005, 42(10): 1746-1751.
    [10]Zhang Yan, Li Wenhui, Meng Yu, and Pang Yunjie. Fast Texture Synthesis Algorithm Using PSO[J]. Journal of Computer Research and Development, 2005, 42(3).

Catalog

    Article views (904) PDF downloads (583) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return