arXiv:2407.13303v2 Announce Type: replace Abstract: Conventional large-scale indoor localization based on Wi-Fi RSSI fingerprinting faces issues of time-consuming and labor-intensive labeled data collection, limited generalization of a model trained under a supervised learning (SL) framework due to its inability to leverage unlabeled data, and model performance degradation in dynamic scenarios with environmental variations. To address those challenging issues, we propose a comprehensive semi-supervised learning (SSL) framework for a deep neural network (DNN) localization model based on the Mea
The increasing demand for precise indoor localization in various applications motivates research into more efficient and robust methods, particularly as AI advances make semi-supervised learning more viable.
Improved indoor localization technology can enhance logistical efficiency, public safety, and autonomous systems in complex environments, addressing challenges where GPS is unavailable.
This research outlines a method to significantly reduce the dependency on costly, labor-intensive data collection for indoor positioning systems, making Wi-Fi RSSI fingerprinting more practical and scalable.
- · Indoor location service providers
- · Logistics and warehousing sectors
- · Smart building developers
- · AI/ML framework developers
- · Companies reliant on purely supervised learning for indoor localization
- · Systems with high manual data collection overhead
More widespread adoption of accurate and cost-effective indoor localization systems becomes feasible.
New applications emerge in areas like retail analytics, healthcare, and industrial automation due to reliable indoor tracking.
The reduced barrier to entry for indoor localization could spur innovation in location-aware services and potentially influence urban planning and infrastructure development.
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