2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2015)
Dec. 12, 2015 to Dec. 14, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2015.18
Multi-hop localization is a common method which is suitable for large-scale application. However, it is usually influenced by the network anisotropy, leading to the instability of the localization performance. In order to reduce the influence of the network anisotropy on the localization accuracy, this paper regards the localization as a regression forecasting process by constructing the mapping relationship between hop-counts and Euclidean distances among nodes. The method can effectively avoid the influence of anisotropy on localization, and it has low computation overhead and high localization accuracy without setting complex parameters. The simulation experimental results show that compared with the previous similar algorithms, the proposed algorithm can obtain a faster localization speed and a higher localization accuracy.
Wireless sensor networks, Euclidean distance, Training, Anisotropic magnetoresistance, Support vector machines, Handheld computers, Network topology
X. Yan, A. Song, Y. Liu and X. Gu, "An Efficient Tolerant-Anisotropic Localization for Large-Scale Wireless Sensor Network," 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), Nanjing, China, 2015, pp. 41-44.