BLE Mesh Reliability Optimization using Neural Networks

Abstract

The Bluetooth Low Energy (BLE) Mesh network technology is one of the newest technologies in the wireless communication domain. Due to low cost and low power consumption, it has already become widespread and has the potential for a wide range of applications. However, the flooding algorithm on which based BLE Mesh data transmission process impacts strongly on networks reliability. Because improper network setup can be critical to ensuring sufficient network reliability, it is necessary to be able to predict the network reliability in order to be able to reconfigure the network to improve its reliability. In this master thesis, we propose neural network approaches that predict the reliability of both the entire network and its individual nodes. Presented results demonstrate that trained neural networks are scalable by providing high accuracy of predictions on networks of different sizes.