Denoising-Contractive Autoencoder for Robust Device-Free Occupancy Detection

Device-free occupancy detection is very important for certain Internet of Things applications that do not require the user to carry a receiver. This paper achieves the device-free occupancy detection with RF fingerprinting, which labels each zone with a 2M-dimensional fingerprint vector. Specifically, the fingerprint vector consists of received signal strength (RSS) values measured from M Bluetooth low energy (BLE) beacons and also their corresponding temporal RSS variations. However, the unreliable RSS values caused two common issues with the fingerprint vector: 1) noise and 2) sparsity. To this end, we propose denoising-contractive autoencoder (DCAE) to jointly deal with these two issues, by learning a robust fingerprint prior to device-free occupancy detection. We validate the performance of our proposed DCAE with large-scale real-world datasets. The experimental results indicate the substantial performance gain of our proposed DCAE in comparison with state-of-the-art autoencoders. In particular, the classifier trained using the fingerprints learned by our proposed DCAE is able to maintain at least 90% accuracy when the noise factor or sparsity ratio increases to 0.6 and 0.5, respectively.