Federated Kalman Filter for Secure IoT-based Device Monitoring Services

TL;DR

This paper proposes a privacy-preserving platform combining Federated Kalman Filter, federated learning, and blockchain technology to enhance IoT device localization accuracy while addressing privacy concerns.

Contribution

It introduces a novel integration of Federated Kalman Filter with blockchain for secure and accurate IoT device localization.

Findings

Significant improvement in RSSI-based localization accuracy.

Effective privacy preservation through blockchain technology.

Potential for enhanced data estimation in IoT device monitoring.

Abstract

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.