A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments

arXiv:2405.09960v1 Announce Type: cross
Abstract: 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 …

abstract analysis arxiv collection cs.lg cs.ni data data collection decision diverse ecosystems eess.sp enabling environments ever form foundation functions industries internet internet of things iot localization location making paradigm real-time services solutions support them time data transfer transfer learning type