Floor identification has gained much attention due to the increasing demand for indoor location-based services, especially prompt emergency response services. Leveraging cellular signals for floor identification has recently been of interest due to the pervasiveness of cellular technology. However, all current systems require information from multiple cell towers concurrently, which is inaccessible in most phones and thus severely limits their deployability. We propose UniCellular , a ubiquitous and deployable floor identification system. UniCellular is the first system to meet the regulatory agencies’ accuracy requirements using received signal strength information from only the serving cell tower. UniCellular relies on a sequence of signal measurements to overcome the limited information available when using only the serving tower. Our system handles multiple challenges affecting accuracy and deployability, including noisy cellular data, overfitting, data collection overhead, and suitability for mobile device deployment. Moreover, UniCellular employs recent advances in deep generative models to improve the system’s robustness to unseen noisy data and reduce data collection overhead. Our extensive experiments verify UniCellular ’s effectiveness in multiple real testbeds, where it correctly estimates the user’s exact floor up to 98.7% of the time using only the serving tower, an improvement up to 310% compared to the state-of-the-art cellular-based system. Furthermore, UniCellular can remain regulatory-compliant with up to 70% reduction in available training data. UniCellular achieves these improvements with over an order of magnitude reduction in the model size compared to the state-of-the-art. This shows UniCellular ’s superior performance while providing a ubiquitous, efficient, and practical solution that meets regulatory requirements.

Sherif Mostafa

Moustafa Youssef

Khaled A. Harras

ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.

ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes high-quality papers on all aspects of spatial algorithms and systems, as well as closely related disciplines. It adopts a multidisciplinary perspective, covering a wide range of areas where spatial data is manipulated or visualized. These areas include geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, along with related visual computing fields such as computer graphics, computer vision, geometric modeling, and visualization, where spatial, geospatial, and spatiotemporal data are central.

《ACM Transactions on Spatial Algorithms and Systems》是一本学术期刊，致力于发表关于空间算法和系统及相关学科的高质量论文。该期刊具有多学科的视角，涵盖了多个领域，其中空间数据的处理或可视化是核心内容。这些领域包括地理学、地理信息系统（GIS）、地理空间和时空数据库、空间和度量索引、基于位置的服务、基于网络的空间应用、地理信息检索（GIR）、空间推理与挖掘、安全与隐私，以及与计算机图形学、计算机视觉、几何建模和可视化等相关的视觉计算领域。

Ubiquitous and Low-Overhead Floor Identification with Limited Cellular Information

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