A novel indoor localization system using machine learning based on bluetooth low energy with cloud computing

In this paper, we propose a novel indoor localization system in a multi-indoor environment using cloud computing. Prior studies show that there are always concerns about how to avoid signal occlusion and interference in the single indoor environment. However, we find some general rules to support our system being immune to interference generated by occlusion in the multi-indoor environment. A convenient way is measured to deploy Bluetooth low energy devices, which mainly collect large information to assist localization. A neural network-based classification is proposed to improve localization accuracy, compared with several algorithms and their performance comparison is discussed. We also design a distributed data storage structure and establish a platform considering the storage load with Redis. Our real experimental validation shows that our system will meet the four aspects of performance requirements, which are higher accuracy, less power consumption, and increased levels of system magnitude and deployment efficiency.