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Node Distribution-Based Localization for Large-Scale Wireless Sensor Networks
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- Authors
- Sang Jin Han, Sung Jin Lee, Sang Hoon Lee, Jong Jun Park, Sang Joon Park
- Issue Date
- 2010-07
- Citation
- Wireless Networks, v.16, no.5, pp.1389-1406
- ISSN
- 1022-0038
- Publisher
- Springer
- Language
- English
- Type
- Journal Article
- Abstract
- Distributed localization algorithms are required for large-scale wireless sensor network applications. In this paper, we introduce an efficient algorithm, termed node distribution-based localization (NDBL), which emphasizes simple refinement and low system-load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighboring nodes, updates its position estimate by minimizing a local cost-function, and then passes this updated position to neighboring nodes. This update process uses a node distribution that has the same density per unit area as large-scale networks. Neighbor nodes are selected from the range in which the strength of received signals is greater than an experimentally based threshold. Based on results of a MATLAB simulation, the proposed algorithm was more accurate than trilateration and less complex than multi-dimensional scaling. Numerically, the mean distance error of the NDBL algorithm is 1.08-5.51 less than that of distributed weighted multi-dimensional scaling (dwMDS). Implementation of the algorithm using MicaZ with TinyOS-2.x confirmed the practicality of the proposed algorithm. © 2009 Springer Science+Business Media, LLC.
- KSP Keywords
- Cost Function, Distance error, Distributed localization, Efficient algorithms, Low-cost, Low-rate, Matlab Simulation, Mean distance, Multi-dimensional scaling(MDS), Position estimate, TinyOS-2.x