Modeling and performance analysis of blockchain-aided secure TDOA localization under random internet-of-vehicle networks

Abstract

The recent fast growth of the internet-of-vehicle (IoV) market has sparked interest in accurate positioning using time-difference-of-arrival (TDOA) schemes. However, the rapid development of the IoV networks has been challenged by a significant increase in malicious attacks that can drastically degrade the localization performance. In this paper, we propose a blockchain-aided vehicular localization scheme as protection against malicious attacks. Specifically, a lightweight and robust trust evaluation process is developed to identify malicious nodes by using target location estimates and node energy consumption behaviors. We provide a theoretical framework to characterize the impact of blockchain and computational delays on TDOA-based localization. Furthermore, a tractable Crame´r-Rao lower bound (CRLB) is derived using stochastic geometry to quantify the localization performance with random network configurations. Simulation results demonstrate that the proposed scheme efficiently protects the localization system against various malicious attacks and accurately estimates the target position even under a high proportion of malicious nodes. The devised benchmark precisely measures the blockchain-aided TDOA-based localization performance in IoV networks and provides insights into localization optimization without lengthy and complicated simulations.