With the popularization of intelligent connected vehicles (ICVs), traffic information sources are becoming ubiquitous and diverse. Given the inherent conflict between data value extraction and privacy protection, federated learning (FL) has emerged as a powerful tool for developing application models with certain generalization capability. Although FL ensures that data remains local, the parameters used for aggregation are still vulnerable to attacks, such as reverse engineering or membership inference. Methods based on homomorphic encryption or differential privacy can alleviate this issue to some extent; however, they also lead to a reduction in training performance. Furthermore, since the data collected by ICVs generally exhibit non-independent and identically distributed (non-IID) characteristics, ensuring model reliability becomes quite challenging. This paper presents a private-parameter-based federated learning method, FedPP, which integrates a Gaussian mechanism with multi-key homomorphic encryption to prevent parameter leakage while eliminating noise disturbance. By sorting and selecting the parameters to be aggregated, this approach not only demonstrates improved generalization capability under heterogeneous conditions but also effectively resists poisoning attacks. To evaluate the model, we constructed two non-IID traffic datasets using the Dirichlet distribution, which comprises a traffic sign dataset and a vehicle image dataset generated through the DALL-E model. Theoretical analysis and experimental results demonstrate that FedPP not only meets provable security under collaborative attacks but also exhibits higher model accuracy in heterogeneous vehicular network environments.

The Internet of Vehicles (IoV) has become a research hotspot owing to the continuous enrichment and expansion of the industrial ecology. IoV data is complex due to heterogeneity and dynamic topology, posing challenges for traditional processing methods and limited onboard device capabilities. To address this, cloud computing is essential for constructing a high-performance IoV network with accurate machine learning, extracting latent value from data. Despite cloud advances, privacy concerns in data transmission and processing within IoV persist. This paper proposed a lightweight fully homomorphic encryption algorithm to address privacy. Notably, the proposed encryption algorithm can be reduced to the conjugacy search problem (CSP) under the standard model. Based on the algorithm, a model for processing encrypted data is established and implemented on a neural network for traffic data classification. The approach is compared against conventional methods in terms of complexity, efficiency, and security. Results unequivocally demonstrate comparable accuracy with original neural networks. In contrast to traditional homomorphic encryption, the proposed approach provides equivalent security with a substantial 100-fold increase in efficiency.

The early trajectory prediction of micro unmanned aerial vehicles (micro-UAVs) with random behavior intentions facilitates the elimination of potential safety hazards. However, due to the property of a small radar cross Section (RCS), the backscattered radar signals from micro-UAVs may be submerged under strong background clutters, leading to distorted tracking and false prediction. To this end, this article presents a spatial–temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a 4-D multiple-input–multiple-output (MIMO) radar. Especially, to obtain accurate trajectories in low signal-to-noise ratio (SNR) conditions, the target detection and tracking are considered to be interdependent and addressed jointly in this work, rather than treating them as two separate processes in conventional methods. The advantage is that with the assistance of tracking, all consecutive spatial information encoded in raw radar streams can be incorporated to enhance the continuous detection performance, avoiding information loss using only one single scan. Subsequently, to accommodate high maneuvering scenarios, an intention-aware end-to-end transformer-based prediction framework is presented to simultaneously discover both spatial and temporal dependencies hiding in long-term estimated trajectories. Consequently, a 4-D frequency modulated continuous wave (FMCW) radar is utilized to evaluate the proposed system. Numerous simulation and experimental results indicate that STIF outperforms competing state-of-the-art methods and achieve superior prediction performance with the accuracy of 0.3851 m in low SNR conditions.

Robust pedestrian trajectory-tracking is an essential prerequisite to traffic accident prevention. However, it is a challenging task in urban autonomous driving, since the weak backscattered signals from pedestrians with small radar cross-section may be submerged in strong background clutters, especially under adverse weather conditions. On this account, this article presents an integration of detection and tracking (iDT) toward multipedestrian with a low signal-to-noise ratio (SNR). In particular, in contrast to conventional methods, in which the detection and tracking are treated as two separate processes, we address them jointly to ensure the accuracy of continuous detection and tracking in low SNR conditions. Another distinguishing element is that to accommodate the time-varying number of targets, the Bayesian framework is tailored by augmenting the state vector with a multipedestrian evolutional indicator. The advantage is that all targets can be tracked simultaneously by searching the global likelihood ratio of a spectrum once, rather than assigning an individual tracker to each target in conventional methods. Furthermore, through the proposed integrated framework, the data association problem is circumvented because there is no explicit measurement-target assignment process in our approach. In addition, a commercial automotive multiple-input-multiple-output millimeter-wave radar sensor is employed to validate the proposed method. Consequently, numerous simulation and experiment results turn out that iDT shows unique advantages in low-observable multipedestrian tracking compared with traditional methods.

Summary Based on the relationship between the Internet of Things (IoT) and mobile payment, this article analyzed the security characteristics of IoT mobile payment. The security system of IoT mobile payment was constructed according to the security demands of mobile payment. Finally, the mobile payment security system was utilized to grade the current level of mobile payment security in China, which verified the urgent problems in China's mobile payment security. The security system of mobile payment based on IoT is a set of macro theoretical systems, whose establishment can lay a theoretical foundation for its branch theoretical research in the future. Meanwhile, it is applicable to the quantitative evaluation of mobile payment security level in China, and has certain guiding significance in theory and practice.

The communication topologies between vehicles in a platoon substantially impact the platoon’s stability. This study provides a distributed linear feedback control law that considers time-varying delay with guaranteed internal and string stability of the platoon system under various communication topologies. Firstly, the vehicle dynamics linearized model was derived using the precise feedback linearization technology. Different types of communication topologies, such as vehicle-to-vehicle communication and sensor-based communication, were described using directed graphs. Secondly, the linear feedback control law was designed to establish the stable zone of the linear controller gain under the effect of different communication topologies using directed graphs and the Routh-Hurwitz stability theorem. The Lyapunov-Razumikhin theorem determines the upper bound of the time-varying delay of various communication topologies. Additionally, the string stability of leader-predecessor following topology was studied, and the results were combined with the internal stability to determine the upper bound of time-varying delay. Finally, the results were verified by conducting two numerical simulations.

Directed against the shortcoming of the vulnerability assessment based on complex network theory for urban road network traffic systems, a state vulnerability assessment method, considering the influence of congestion, is constructed by using a cloud model to describe the randomness and uncertainty characteristics of risks. First, based on complex network theory, the primary index assessment system of road network vulnerability is introduced. Second, to describe the congestion states of roads, the cloud model theory is introduced to characterize the congestion features of road sections. After that, a state vulnerability identification method based on congestion cloud charts is constructed. Finally, on the basis of topological mapping of road network in Nan’an District in Chongqing, experiments and analyses are carried out in light of the actual congestion delay index dataset provided by AutoNavi Maps to verify the effectiveness and rationality of our proposed scheme. The experimental results show that the assessment method of state vulnerability can better describe the overall operational state and vulnerable road sections for road network.

In order to more effectively eliminate the disturbance of vibration signal to ensure the security monitoring of stacker be more accurate in Industrial Internet of Things (IIoT), a cooperative denoising algorithm with interactive dynamic adjustment function was constructed and proposed. First, some basic theories such as EMD, EEMD, LMS, and VSLMS were introduced in detail according the characteristics of stacker in IIoT. Meanwhile, the advantages and disadvantages of varieties of algorithms have been analyzed. Secondly, based on the traditional VSLMS-EEMD, an improved VSLMS-EEMD was proposed. Thirdly, to guarantee the denoising effect of security monitoring in IIoT, a cooperative denosing model and framework named as IDVSLMS-EEMD was designed and constructed based on the advantages of LMS, VSLMS, and improved VSLMS-EEMD. In addition, the assignment rules and models of the corresponding weight coefficients were also set up according to the features of the error signal of denoising process in IIoT. At the same time, we have designed a cooperative denoising algorithm with interactive dynamic adjustment function. And some evaluated indexes such as NSR and SDR were selected and introduced to evaluate the effectiveness of the different algorithms. Thirdly, some simulation examples and real experiment examples of stacker running signals under abnormal condition, which has been developed and applied in Power Grid of China, was used to verify and simulate the effectiveness of our presented algorithm. The experiment comparison results have shown that our algorithm can improve the denosing effect. Finally, some conclusions were discussed and the directions for future engineering application were also pointed out.

The key for location‐based service popularization in vehicular environment is security and efficiency. However, due to the constrained resources in vehicle‐mounted system and the distributed structure of fog computation, disposing of the conflicts between real‐time implementation and user’s privacy remains an open problem. Aiming at synchronously preserving the position information for users as well as the data proprietorship of service provider, an efficient location‐based querying scheme is proposed in this paper. We argue that a recent scheme proposed by Jannati and Bahrak is time‐consuming and vulnerable against active adaptive corruptions. Thus accordingly, a postquantum secure oblivious transfer protocol is devised based on efficient NTRU cryptosystem, which then serves as the understructure of a complete location‐based querying scheme in ad hoc manner. The security of our scheme is proved under universal composability frame, while performance analysis is also carried out to testify its efficiency.