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.

中文翻译：

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一种基于低功耗蓝牙和云计算的机器学习新型室内定位系统

在本文中，我们使用云计算在多室内环境中提出了一种新颖的室内定位系统。之前的研究表明，在单一的室内环境中，如何避免信号遮挡和干扰一直是人们关注的问题。然而，我们发现了一些通用规则来支持我们的系统免受多室内环境中遮挡产生的干扰。一种方便的方法是部署蓝牙低功耗设备，主要收集大量信息以辅助定位。提出了一种基于神经网络的分类来提高定位精度，与几种算法进行比较，并讨论了它们的性能比较。我们还设计了分布式数据存储结构并建立了一个考虑存储负载的平台与Redis。