Journal
IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2019)
Volume -, Issue -, Pages 883-891Publisher
IEEE
DOI: 10.1109/infocom.2019.8737621
Keywords
-
Categories
Funding
- National Key R&D Program of China [2017YFB1003000]
- National Natural Science Foundation of China [61772306]
- State Grid of China Research Fund
Ask authors/readers for more resources
Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial loT system. In average, TwinLeak achieves a TP11 higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available