A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments
This addresses the cost and complexity issues for smart cities by providing a single model for diverse environments, though it is incremental as it builds on existing transfer learning methods.
The paper tackles the problem of IoT localization requiring multiple models for different environments by proposing a unified deep transfer learning model, achieving improvements of 17.18% in indoor and 9.79% in outdoor environments over a baseline.
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.