Sensor nodes localization in wireless Internet of Things (IoT) sensor networks is crucial for the effective operation of diverse applications, such as smart cities and smart agriculture. Existing sensor nodes localization approaches heavily rely on anchor nodes within wireless sensor networks (WSNs). Anchor nodes are sensor nodes equipped with global positioning system (GPS) receivers and thus, have known locations. These anchor nodes operate as references to localize other sensor nodes. However, the presence of anchor nodes may not always be feasible in real-world IoT scenarios. Additionally, localization accuracy can be compromised by fluctuations in Received Signal Strength Indicator (RSSI), particularly under non-line-of-sight (NLOS) conditions. To address these challenges, we propose UBiGTLoc, a Unified Bidirectional Long Short-Term Memory (BiLSTM)-Graph Transformer Localization framework. The proposed UBiGTLoc framework effectively localizes sensor nodes in both anchor-free and anchor-presence WSNs. The framework leverages BiLSTM networks to capture temporal variations in RSSI data and employs Graph Transformer layers to model spatial relationships between sensor nodes. Extensive simulations demonstrate that UBiGTLoc consistently outperforms existing methods and provides robust localization across both dense and sparse WSNs while relying solely on cost-effective RSSI data.

翻译：无线物联网传感器网络中传感器节点的定位对于智慧城市与智慧农业等多样化应用的有效运行至关重要。现有传感器节点定位方法高度依赖无线传感器网络内的锚节点。锚节点指配备全球定位系统接收器、因而位置已知的传感器节点，其作为参考节点用于定位其他传感器节点。然而，在实际物联网场景中，锚节点的部署并非总是可行。此外，接收信号强度指示的波动，特别是在非视距条件下，可能损害定位精度。为应对这些挑战，本文提出UBiGTLoc，一种统一的双向长短期记忆-图Transformer定位框架。所提出的UBiGTLoc框架能够有效实现无锚节点与含锚节点无线传感器网络中的传感器节点定位。该框架利用BiLSTM网络捕捉RSSI数据中的时序变化，并采用图Transformer层建模传感器节点间的空间关系。大量仿真实验表明，UBiGTLoc在仅依赖低成本RSSI数据的前提下，持续优于现有方法，并在密集与稀疏无线传感器网络中均能提供鲁棒的定位性能。