Sense Me on the Ride: Accurate Mobile Sensing over a LoRa Backscatter Channel

Wireless sensing has great significance in Internet of Things (IoT) applications and has attracted substantial research interests in academia. In this study, we propose Palantir, a first-of-its-kind long-range sensing system based on the LoRa backscatter technology. By utilizing the ON-OFF-Keying modulated backscatter signals, Palantir can perform fine-grained long-range cyclist sensing. Our findings show that sensing is more susceptible to channel quality than communication. Hence, the design of Palantir particularly addresses the critical challenges of signal processing, such as amplitude instability, frequency offset, clock drift, spectrum leakage, and multiplicative noise. We implement Palantir and evaluate its performance by conducting comprehensive benchmark experiments. A prototype is also built and a case study of respiration monitoring in the real world is implemented. Results demonstrate that Palantir can perform accurate sensing at a range up to 100 m, which is twice that of state-of-the-art approaches. The median deviation of the detected motion period is as low as 0.2%.