Federated Kalman Filter for Secure IoT-based Device Monitoring Services
This addresses privacy concerns for IoT device users, but it appears incremental as it builds on existing federated learning and blockchain methods.
The paper tackled privacy issues in IoT device monitoring services by introducing a platform combining Federated Kalman Filter, federated learning, and private blockchain, and found it has significant potential for improved data estimation in RSSI-based localization compared to a standard Kalman Filter.
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.