Modeling BLE Propagation above the Ceiling for Smart HVAC Systems

We have been working on automatic location estimation of HVACs utilizing the RSSI of BLE (Bluetooth Low Energy) to improve the time-consuming process in the HVAC system network setting where technicians need to identify the location of each HVAC one by one to assign a network address to the HVAC. This short paper particularly reports an initial attempt of training a BLE propagation prediction model adaptive to "above-ceiling" environment, which is a key enabler for automation of the assignment process. Based on the 1.3 million RSSI sample data collected in a real building for one week, we trained the model with different parameters and discovered that RMSE of RSSI prediction has the following trend: 1) it decreases with the introduction of the obstacle features - the number of beams and the number of HVAC machines, and 2) it decreases with the increase of polynomial term degree. The best model so far has an RMSE of 4.832 dBm tested by 10-fold cross-validation. The methodology and quantified result of this initial attempt provide reference not only for the network assignment process but also for building BLE-mesh networks in the above-the-ceiling environment, which can be exploited for HVACs management or sensor data aggregation.