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.
Bibliographical noteFunding Information:
Acknowledgments This work was supported by the IT R&D program of MKE/KEIT [10033886, Core technology development of large-scale, intelligent and cooperative surveillance system] and the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment) (IITA-2009-C1090-0902-0038).
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Electrical and Electronic Engineering