Environment-aware Testing for DNN-based Smart-home WiFi Sensing Systems

WiFi-based human activity recognition is a promising sensing application in smart homes due to the low cost, wide availability, and privacy preservation of WiFi devices. However, pushing WiFi sensing technology to industry-scale deployment is difficult due to its poor robustness against environment differences. How to systematically test such sensing system is crucial to improve its practicality, and is also challenging because the sensing performance is significantly influenced by the underlying physical environments. In this paper, we introduce the problem of testing environment-dependent sensing systems, including how to measure test coverage and how to effectively generate data to improve the coverage. We describe our initial attempts on examining test sufficiency with environment-neuron joint coverage and improving the coverage through targeted environment variations and signal transformations. Our experiments have demonstrated the higher effectiveness of using environment-neuron coverage to represent test sufficiency, as compared with using the conventional neuron coverage. Meanwhile, the coverage-guided sensing data generation can lead to higher accuracy of the sensing system under changing environments.