Device monitoring services have increased in popularity with the evolution of recent technology and the continuously increased number of Internet of Things (IoT) devices. Among the popular services are the ones that use device location information. However, these services run into privacy issues due to the nature of data collection and transmission. In this work, we introduce a platform incorporating Federated Kalman Filter (FKF) with a federated learning approach and private blockchain technology for privacy preservation. We analyze the accuracy of the proposed design against a standard Kalman Filter (KF) implementation of localization based on the Received Signal Strength Indicator (RSSI). The experimental results reveal significant potential for improved data estimation for RSSI-based localization in device monitoring.
翻译:物联网设备监测服务随着最近技术的进化和不断增加的设备数量而变得越来越受欢迎。最受欢迎的服务之一是使用设备位置信息的服务。然而,由于数据收集和传输的性质,这些服务遇到了隐私问题。在本研究中,我们引入了一种采用联邦学习方法和私有区块链技术保护隐私的联邦卡尔曼滤波器(FKF)平台。我们分析了所提出的设计对基于接收信号强度指示器(RSSI)的定位的标准卡尔曼滤波器(KF)实现的估计精度。实验结果表明,在设备监测中,基于RSSI的定位的数据估计具有显著的改进潜力。