Towards Sub-Room Level Occupancy Detection with Denoising-Contractive Autoencoder

Lately, there are many works exploited the radio frequency (RF) fingerprint for occupancy detection. However, most works suffer severe performance variations owing to the unreliable received signal strength (RSS). In this paper, we propose a deep learning approach to occupancy detection: 1) an unsupervised denoising-contractive autoencoder (DCAE) is built to learn a robust fingerprint representation from the raw RSS measurements, and 2) a supervised softmax function is added at the last layer for classification. A real testbed with Bluetooth Low Energy (BLE) beacons was built such that we can collect real-world RSS data for experiments. The data were collected via different devices at different times to better reflect environmental variations. The experimental results show that our proposed approach achieves a substantial performance gain in comparison to the conventional machine learning approaches. Specifically, our proposed DCAE is able to reconstruct the noisy and always changing data with less than 0.047 mean square error. Overall, our occupancy detection combining DCAE and softmax classifier achieves sub-room level accuracy for at least 99.3% of the time.