A Secure ZUPT-Aided Indoor Navigation System Using Blockchain in GNSS-Denied Environments

Abstract

This paper proposes a novel Blockchain-based indoor navigation system that combines a foot-mounted dual-inertial measurement unit (IMU) setup and a zero-velocity update (ZUPT) algorithm for secure and accurate indoor navigation in GNSS-denied environments. The system estimates the user’s position and orientation by fusing the data from two IMUs using an extended Kalman filter (EKF). The ZUPT algorithm is employed to detect and correct the error introduced by sensor drift during zero-velocity intervals, thus enhancing the accuracy of the position estimate. The proposed Low SWaP-C blockchain-based decentralized architecture ensures the security and trustworthiness of the system by providing an immutable and distributed ledger to store and verify the sensor data and navigation solutions. The proposed system is suitable for various indoor navigation applications, including autonomous vehicles, robots, and human tracking. The experimental results provide clear and compelling evidence of the effectiveness of the proposed system in ensuring the integrity, privacy, and security of navigation data through the utilization of blockchain technology. The system exhibits an impressive ability to process more than 680 transactions per second within the Hyperledger-Fabric framework. Furthermore, it demonstrates exceptional accuracy and robustness, with a mean RMSE error of 1.2 m and a peak RMSE of 3.2 during a 20 min test. By eliminating the reliance on external signals or infrastructure, the system offers an innovative, practical, and secure solution for indoor navigation in environments where GNSS signals are unavailable.

A Novel Efficient Dynamic Throttling Strategy for Blockchain-Based Intrusion Detection Systems in 6G-Enabled VSNs

“…The framework gathers information from four main layers, namely, the IoT layer, network layer, fog layer, and cloud layer to monitor and analyse the network traffic among IoT devices. The authors in [ 23 , 24 ] proposed blockchain-based solutions to detect malicious vehicles and IoT devices. The main difference between our work and the work proposed in [ 23 ] is that in the latter case, the malicious behaviour of the vehicles is detected using machine learning (neural networks), while in our case we focus on the lateral movement among the endpoints installed inside the vehicles.…”

“…The main difference between our work and the work proposed in [ 23 ] is that in the latter case, the malicious behaviour of the vehicles is detected using machine learning (neural networks), while in our case we focus on the lateral movement among the endpoints installed inside the vehicles. The authors in [ 24 ] addressed the problem of indoor navigation and proposed a new secure communication approach based on blockchain, which is different from the objective of our paper.…”