RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering
| Main Authors: | Klus, Lucie, Quezada-Gaibor, Darwin, Torres-Sospedra, Joaquın, Lohan, Elena Simona, Granell, Carlos, Nurmi, Jari |
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| Format: | Proceeding Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4091706 |
Daftar Isi:
- Abstract—Modern IoT devices, that include smartphones and wearables, usually have limited resources. They require efficient methods to optimize the use of internal storage, provide computational efficiency, and reduce energy consumption. Device resources should be used appropriately, especially when employed for time-consuming and energy-intensive computations such as positioning or localization. However, reducing computational costs usually degrades the positioning methods. Therefore, the goal of this article is to propose and compare compression mechanisms of the fingerprinting datasets for energy-saving without losing relevant information, by using adaptive k-means clustering. As a result, we achieved a compression ratio of up to 15.97 with a small decrease (1%) in position error.
- Supplementary materials are available at https://doi.org/10.5281/zenodo.4026370