Report
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments
| العنوان: | A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments |
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| المؤلفون: | Ahmed, Abdullahi Isa, Etiabi, Yaya, Azim, Ali Waqar, Amhoud, El Mehdi |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture |
| الوصف: | Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about 17.18% in indoor environments and 9.79% in outdoor environments. Comment: 6 pages, 8 figures, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.09960 |
| رقم الانضمام: | edsarx.2405.09960 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |