PACE: Physically-Assisted Channel Estimation

Abstract

Radio link quality is highly influenced by changes in the physical environment. To sustain reliable and efficient data delivery, link quality estimation is essential for Cyber-Physical Systems (CPSs) or Internet of Things (IoT). Network-based link quality estimation methods estimate the link quality by monitoring data transmissions. In a dynamic environment, the accuracy of link quality so estimated may become degraded because the accuracy must be balanced against the overhead of data transmissions. In this work, we propose to incorporate sensor readings available in a CPS/IoT system to augment existing link quality estimation. We call this a Physical-Assisted Channel Estimator (PACE). By analyzing sensor readings that are highly correlated to the link quality, PACE may detect the change of link quality in real-time. Evaluation conducted on a real intelligent parking system shows that compared to existing network-based methods, PACE reacts to persistent disturbances much more quickly without sacrificing robustness to transient fluctuations, and achieves higher accuracy even under a low data transmission rate. With PACE, the data delivery performance of routing protocols can be significantly improved. We expect PACE to be the first milestone towards Physical-Assisted Cyber Systems (PACSs) for fulfilling the vision of environment-aware computing and communication.