Accurate and Ubiquitous Floor Identification at the Edge using a Single Cell Tower

Abstract

All available cellular-based floor identification systems require information from multiple cell towers simultaneously, a feature absent in almost all phones, thus constraining their practicality. To address this limitation, we propose CellFloor, the first floor identification system to achieve high accuracy using only the serving cell tower while being regulatory-compliant. Based on recent advances in NLP, CellFloor builds a domain-specific edge-deployed large language model to identify the floor given a sequence of serving tower signal measurements. Our novel NLP-inspired approach allows CellFloor to extract rich contextual patterns from the signals, overcoming the limited information available when only the serving tower is used. Moreover, CellFloor employs recent advances in deep generative models to improve robustness against serving tower signal variations, enhancing floor identification accuracy. Our extensive evaluation of CellFloor shows consistently re-markable accuracy on multiple real testbeds, where it accurately estimates the exact floor at least 99.49% of the time using only the serving tower. This accuracy is superior to state-of-the-art (SOTA) systems, even when they use all available towers. Furthermore, unlike CellFloor, most SOTA systems fail to meet regulatory requirements when restricted to using just the serving tower, which is the only information available from the majority of current phones in the market. CellFloor also maintains regulatory compliance under different challenging conditions, including using only 20% of the available training data.