Extending Beacon Lifetime by Predicting User Occupancy Using Deep Neural Networks

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications. However, the BLE beacon network usually suffers from poor reliability and high maintenance costs due to the short-lived battery lifetime. A few recent works tackled the challenge by adjusting the operating configuration subject to the nearby user occupancy in a reactive manner. However, previous works failed to adapt to dynamically changing user occupancy behaviors due to the lack of a prediction mechanism. Such shortcomings lead to a shorter lifetime and longer packet arrival delays. To overcome this limitation, a novel neural network architecture that makes accurate and timely user occupancy prediction is introduced. The proposed network learns partial correlation of the time-series data and attention score for robust prediction. The predictions are then utilized to adaptively change the operation configurations to maximize the lifetime and minimize the packet arrival delay. To the best of our knowledge, this is the first work to leverage time-series prediction to optimize the performance of a BLE beacon. The effectiveness of the proposed learning methods is verified by comprehensive simulations with real-life data. The results demonstrate that the proposed method can extend a beacon lifetime by 50% more than the existing reactive approach. Moreover, the packet arrival delays are also reduced by up to 40%.