Secure and Privacy-Preserving Multi-Source Optimization for UAV-Assisted Intelligent Transportation Systems

Chen, Chao and Xu, Xiaolong and Cui, Guangming and Xiang, Haolong and Wu, Jiale and Bilal, Muhammad (2025) Secure and Privacy-Preserving Multi-Source Optimization for UAV-Assisted Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050

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Abstract

Uncrewed Aerial Vehicles (UAVs) are increasingly integrated into Intelligent Transportation Systems (ITS) to process multi-source data, thereby enhancing overall efficiency. As ITS relies increasingly on multi-source data for decision-making, new challenges arise in terms of secure data integration, user privacy protection, and communication latency. Most current UAV optimization methods only look at certain things, including planning the flight path or scheduling resources. However, these solutions don’t have a way to optimize all of these important parameters at the same time, which leads to higher latency and energy use. We suggest a secure and privacy-preserving multi-UAV optimization technique based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) with Prioritized Experience Replay (PER) to solve these problems. This algorithm jointly optimizes UAV trajectories, resource scheduling, and task allocation in a multi-UAV and multi-edge servers system to reduce energy consumption and system latency while preserving user data privacy. A blockchain-based mechanism is added to make UAV data even more secure and open by recording task execution records in a way that can’t be changed. The proposed approach works, as shown by the outcomes of the experiments. The proposed solution cuts down on system latency and energy use by a lot compared to current optimization methods, even when the system is under a lot of stress or needs to keep data private.