Abstract

Bluetooth Low Energy (BLE) beacon networks are a popular infrastructure for IoT and smart city applications due to their scalability and affordability, as well as the proliferation of Bluetooth-enabled devices. However, BLE beacon networks suffer from short battery lifetime, inducing additional maintenance costs. Previous works have tackled this problem by proposing a more energy-efficient BLE beacon firmware that will change its operating configuration based on user existence information. However, previous efforts could not adapt to varying user traffic conditions and therefore was impractical. To address this issue, this paper proposes a novel neural network-driven framework, User-P, that extends beacon lifetime by changing its operating configuration by predicting user traffic conditions. Furthermore, the paper also presents a novel machine learning method tailored for user traffic prediction. Last but not least, the effectiveness of the proposed framework and methods are proven through a set of simulations. The simulation results show that the proposed framework can extend the beacon lifetime by 180% in comparison to that of the state-of-the-art techniques.