Enhanced data acquisition in dense RFID measurement systems using Dynamic Query Tree

Abstract Passive RFID sensor networks are increasingly deployed as measurement systems for distributed environmental monitoring applications. However, in high-density sensing scenarios, the transmission of large sensor data packets leads to severe signal collisions, causing high measurement latency and compromising measurement integrity. To address the bottleneck of low data throughput in existing sensing systems, this paper proposes a high-throughput RFID sensor measurement system based on a novel parallel decoding method. Unlike traditional serial identification protocols, a Dynamic Collision-Detection Query Tree (DCDQT) strategy is integrated into the system to optimize the real-time collection of sensor measurement data. By employing a segment-based processing structure and a Synchronous Probe Code mechanism, the system achieves parallel decoding of multiple sensor tags in a single timeslot based on Hamming distance analysis. Simulations with 200-2000 tags demonstrate that the proposed method achieves 66.5% slot efficiency, reducing measurement acquisition time by 16.8% and communication overhead by 8.85%. Furthermore, a hardware prototype based on Software-Defined Radio (SDR) and FPGA is built for validation. Field measurement experiments with temperature and humidity sensor tags confirm the system's robustness and its ability to maintain high data integrity in dense measurement environments.