Issue No. 05 - May (2014 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.85
Xiaoping Wang , School of Computer Science, National University of Defense Technology, Section 642, Changsha, China
Yunhao Liu , School of Software and TNLIST, Tsinghua University, Beijing, China
Zheng Yang , School of Software and TNLIST, Tsinghua University, Beijing, China
Kai Lu , School of Computer Science, National University of Defense Technology, Section 642, Changsha, China
Jun Luo , School of Computer Science, National University of Defense Technology, Section 642, Changsha, China
Accurate localization is crucial for wireless ad-hoc and sensor networks. Among the localization schemes, component-based approaches specialize in localization performance. By grouping nodes into increasingly large rigid components, component-based localization algorithms can properly conquer network sparseness and anchor sparseness. However, such design is sensitive to measurement errors. Existing robust localization methods focus on eliminating the positioning error of a single node. Indeed, a single node has two dimensions of freedom in 2D space and only suffers from one type of transformation: translation. As a rigid 2D structure, a component suffers from three possible transformations: translation, rotation, and reflection. A high degree of freedom brings about complicated cases of error productions and difficulties on error controlling. This study is the first work addressing how to deal with ranging noises for component-based methods. By exploiting a set of robust patterns, we present an Error-TOlerant Component-based algorithm (ETOC) that not only inherits the high-performance characteristic of component-based methods, but also achieves robustness of the result. We evaluate ETOC through a real-world sensor network consisting of 120 TelosB motes as well as extensive large-scale simulations. Experiment results show that, comparing with the-state-of-the-art designs, ETOC can work properly in sparse networks and provide more accurate localization results.
Robustness, Distance measurement, Noise measurement, Algorithm design and analysis, Measurement errors, Educational institutions, Reflection
X. Wang, Y. Liu, Z. Yang, K. Lu and J. Luo, "Robust Component-Based Localizationin Sparse Networks," in IEEE Transactions on Parallel & Distributed Systems, vol. 25, no. 5, pp. 1317-1327, 2014.