UBiGTLoc: A Unified BiLSTM-Graph Transformer Localization Framework for IoT Sensor Networks
Ayesh Abu Lehyeh, Anastassia Gharib, Tian Xia, Dryver Huston, Safwan Wshah

TL;DR
UBiGTLoc is a novel framework combining BiLSTM and Graph Transformer models to improve sensor node localization in IoT networks, especially when anchor nodes are unavailable or unreliable, using only RSSI data.
Contribution
The paper introduces UBiGTLoc, a unified framework that effectively localizes IoT sensor nodes in both anchor-free and anchor-based scenarios using deep learning and graph modeling.
Findings
Outperforms existing localization methods in simulations.
Provides robust accuracy in dense and sparse networks.
Operates effectively with only RSSI data.
Abstract
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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Energy Efficient Wireless Sensor Networks · Sparse and Compressive Sensing Techniques
