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.

翻译：物联网（IoT）作为一项持续演进的技术范式，正深刻重塑全球产业与社会。由定位解决方案支撑的实时数据采集、分析与决策，构成了基于位置服务的基石，使其能够支持多样化物联网生态系统中的关键功能。然而，现有定位研究多聚焦于单一环境，导致需要开发多个模型以适配不同场景。在智慧城市背景下，环境动态性进一步加剧了成本与复杂性。为应对这些挑战，本文提出一种统一室内外定位解决方案，利用迁移学习（TL）机制构建单一深度学习模型，可在多样化环境中精准预测物联网设备的定位。性能评估表明，采用基于编码器的迁移学习方案后，室内环境基线模型性能提升约17.18%，室外环境提升约9.79%。