Processing sensitive data and deploying well-designed Intellectual Property (IP) cores on remote Field Programmable Gate Array (FPGA) are prone to private data leakage and IP theft. One effective solution is constructing Trusted Execution Environment (TEE) and its secure boot process on FPGA-SoC (FPGA System on Chip). This paper aims to establish Secure Runtime Customizable TEE (SrcTEE) on FPGA-SoC through the design of a novel secure boot scheme and the design of the following three components: 1) CrloadIP, which enforces access control on TEE applications deploying IP at runtime such that SrcTEE can alleviate threats from unauthorized TEE applications and then SrcTEE can be adjusted dynamically and securely; 2) CexecIP, which not only enables the execution of newly-installed IP cores without modifying the operating system of FPGA-SoC TEE, but also prevents insider attacks from executing IPs in SrcTEE; 3) CremoAT, which can provide the newly-measured SrcTEE state and establish a secure communication path between remote verifiers and SrcTEE. Our secure boot scheme supports refreshable root trust key, and assures the authenticity and integrity of boot codes during the SrcTEE booting process. We conduct a security analysis of SrcTEE and its performance evaluation on Xilinx Zynq UltraScale+ XCZU15EG 2FFVB1156 MPSoC.

Unmanned aerial vehicles (UAVs) are being broadly employed to assist in efficient data collection for IoT networks. Studies have been conducted to ensure the effectiveness and safety of UAVs in the data collection process. However, they only considered part of the challenges of energy consumption, collision avoidance, and mobility of IoT devices. In this paper, we study a UAV path planning optimization problem for UAV-assisted data collection to maximize the amount of collected data. Different from these existing works, this optimization problem not only considers all these challenges, but also considers the kinematic and communication constraints. Moreover, in this problem, the duration required for the UAV to complete the mission is unknown, makes it more challenging to solve this problem through traditional optimization methods. We thus formulate the problem as a partially observable Markov decision process (POMDP) with a continuous action space and propose a proximal policy optimization-based algorithm to address it. Experiment results demonstrate that our algorithm has significant advantages over other baseline algorithms in terms of success rate, data collection rate, and collision rate.

Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR architectures: (1) they need system nodes with enough computing and storage resources; (2) they have hardly considered the reliability of DHR architecture. To this end, this paper proposes a double-layer DHR (DDHR) architecture to ensure the reliability of RPSS. This architecture introduces a set of algorithms, which are optimized co-computation and ruling weight optimization algorithms for the data processing flow of the DDHR architecture. This set improves the reliability of the DDHR architecture. For the evaluation of the reliability of DDHR architecture, this paper also proposes two metrics: (1) Dynamic available similarity metric. This metric does not rely on the overall similarity of the double-layer redundant executor sets but evaluates the similarity of their performance under the specified interaction paths within a single scheduling cycle. The smaller its similarity, the higher its reliability. (2) Scheduling cycle under dual-layer similarity threshold. This metric evaluates the reliability of the RPSS under actual conditions by setting the schedulable similarity thresholds between the same and different layers of the dual-layer redundant executives in the scheduling process. Finally, analog simulation experiments and prototype system building experiments are carried out, whose numerical experimental results show that the DDHR architecture outperforms the traditional DHR architecture in terms of reliability and performance under different redundancy and dynamically available similarity thresholds, while the algorithmic complexity and multi-tasking concurrency performance are slightly weaker than that of the DHR architecture, but can be applied to the main operations of the RPSS in general.

The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning algorithm that integrates DRL with the large language model (LLM) reasoning to enable safe, compliant, and economically viable trajectory planning. Specifically, we model the trajectory planning task as a partially observable Markov decision process, explicitly incorporating obstacle avoidance, regulation awareness, and energy constraints. We design a hybrid optimization algorithm based on the soft actor-critic algorithm and LLM reasoning to enable adaptive decision-making in uncertain and dynamic environments. Experimental results demonstrate that our algorithm achieves the best overall performance, with the highest data collection rate (99.50%), almost zero collision avoidance rate and regulation violation rate, a successful landing rate of nearly 100%, and the lowest energy consumption rate (76.95%). These results validate the effectiveness of our algorithm in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.

Data provision, referring to data upload and data access, is one key phase in vehicular digital forensics. The unique features of Driverless Taxi (DT) bring new issues to this phase: I1) efficient verification of data integrity when diverse Data Providers (DPs) upload data; I2) DP privacy preservation during data upload; and I3) privacy preservation of both data and INvestigator (IN) under complex data ownership when accessing data. Considering that the existing works on digital forensics cannot address all these issues, we first propose a novel Lightweight and Privacy-preserving Data Provision (LPDP) approach consisting of three mechanisms: 1) Privacy-friendly Batch Verification Mechanism (PBVm), 2) Data Access Control Mechanism (DACm), and 3) Decentralized IN Warrant Issuance Mechanism (DIWIm). PBVm ensures scalable verification of data integrity to address I1. PBVm also ensures the DP privacy preservation in terms of the location privacy and unlinkability of data upload requests to address I2. Besides, DACm and DIWIm are combined to ensure data privacy preservation and the identity privacy of IN in terms of the anonymity and unlinkability of data access requests without sacrificing the traceability to address I3. Security analysis and performance evaluations validate LPDP’s capabilities in addressing the three issues.

Establishing and sustaining Metaverse service necessitates an unprecedented scale of resources. Some researchers consider the deployment of Metaverse service in a cloud-edge resource architecture, which can satisfy the escalating demand for Metaverse service resources while ensuring both high bandwidth and low latency. However, how to ensure the security and trustworthiness of resource nodes (RNs) in this architecture and thus ensure the reliability and security of Metaverse service is still a challenge. In this paper, we first propose a novel mechanism, named Reliable and Secure Metaverse Service (RSMS), to ensure Metaverse service reliability and security without sacrificing performance. RSMS consists of two protocols: (1) One is a blockchain-based lightweight mutual authentication protocol, which can assure the trustworthiness of heterogeneous Metaverse service RNs dynamically joining a Metaverse service resource pool and then guarantee the security of Metaverse service. (2) The other is a group authentication protocol used to form and maintain a stable and secure Metaverse service group composed of RNs, which ensures the reliability and enhances the security of Metaverse service. The reliability and security of Metaverse service under RSMS are thoroughly discussed, and also informal and formal security analysis are conducted. Additionally, we study the impact of RSMS on Metaverse service throughput, demonstrating its lightweight feature.