SenLoRa: Integrated Sensing and Communication with Ambient LoRa

Lu Wang, Hao Wang, Boliang Guo, Xiaoshen Li, Yongzhi Huang, Junmei Yao, Kaishun Wu

Abstract: LoRa is widely adopted for IoT connectivity. While integrating sensing and communication (ISAC) in LoRa is crucial for future development, existing approaches fail to implement ISAC with ambient LoRa traffic due to low data rates. Relying solely on the limited preamble for sensing proves insufficient, as it often requires dedicated sensing devices or protocol modifications. To address these challenges, we theoretically analyze the trade-off between communication and sensing ambient LoRa traffic. By introducing the Sensing-Communication Ratio (SCR) and per-symbol sensing entropy, we provide new metrics to quantify sensing capacity. Building on these insights, we propose SenLoRa, the first practical LoRa ISAC system that integrates seamlessly with existing communication infrastructure, enabling a range of sensing applications with optimized SCR. The core of SenLoRa lies in confidence symbol extraction, which uses a likelihood probability ratio to identify high-confidence symbols from payload for both communication and sensing entropy enhancement. We also design an incentive strategy to obtain additional sensing entropy. Real-world case studies demonstrate that SenLoRa achieves a respiration detection error as low as 0.2 bpm and over 90% accuracy in walking detection, while maintaining standard LoRa communication. These results validate SenLoRa as a practical and efficient ISAC solution for LoRa-based IoT systems.