Merging Strategy on Cluster-based MDS Algorithm for Nodes Localization in Wireless Sensor Networks
- Authors
- Kang, Insung; Nam, Haewoon
- Issue Date
- Dec-2020
- Publisher
- IEEE
- Keywords
- Cluster-based; global map; least squares method (LSM); localization; map merging; map weight; multidimensional scaling (MDS); procrustes transform
- Citation
- 2020 International Conference on Information and Communication Technology Convergence (ICTC), v.2020-October, pp 1132 - 1135
- Pages
- 4
- Indexed
- SCI
SCOPUS
- Journal Title
- 2020 International Conference on Information and Communication Technology Convergence (ICTC)
- Volume
- 2020-October
- Start Page
- 1132
- End Page
- 1135
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/116310
- DOI
- 10.1109/ICTC49870.2020.9289570
- ISSN
- 2162-1233
- Abstract
- In this paper, we introduce a new map merging method based on least squares method (LSM) in cluster-based multidimensional scaling (MDS) localization systems. Most map merging algorithms used in cluster-based MDS localization systems cannot avoid the accumulation of errors in the map merging process in common. Unfortunately, these errors accumulated in the map merging process lead to a decrease in the performance of position estimation of the whole nodes in a network. Through the proposed merging algorithm based on LSM, the errors accumulated in the map merging process can be avoided. The proposed algorithm can be applied not only to the MDS-based localization system but also to other coordinate-based localization systems. The proposed map merging algorithm shows better performance of position estimation accuracy than the simple merging method used in many cluster-based MDS localization systems in all simulations which are not considering map weight factors. As an example of the highest improvement, a simulation result shows 29% improvement in position estimation accuracy after using each corresponding merging method.
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