With the rapid development of artificial intelligence, in the open communication environment, interference and network attacks on intelligent terminals are more frequent. Therefore, communication security and system security issues in the intelligent platoons become hot topics. Motivated by these, the concept of zero trust was proposed. Under this communication framework, all clients are not trusted by default, and the principle of “never trust, always verify” is followed. In order to maintain communication, they need to be authenticated and authorized. This work discusses the zero‐trust framework and mechanism aiming at communication security and gives the structure and module function in detail. Moreover, the impact of malicious cyber‐attacks on the network is parameterized, and the safety coefficients of the full‐trust and zero‐trust frameworks are compared to show the effect of the zero‐trust framework on information security. After that, the research studies range from information security to system security, where the effects of the zero‐trust framework on terminal distribution, controller design, and system stability are studied, taking an intelligent connected vehicle platoon as an example. Finally, three examples verify the positive impact of the zero‐trust framework on network information security.

Recent advancements in Intelligent Connected Vehicle (ICV) systems highlight the critical importance of cybersecurity within these complex networks, yet they still face challenges such as difficulties in precise modeling and poor adaptability to dynamic environments. This paper introduces an innovative control approach by integrating an event-triggered Intermittent Control (IC) strategy within a Zero-Trust Framework (ZTF). This methodology selectively triggers events for vehicle identities and data that meet a predefined trust threshold during each control interval, significantly enhancing the dynamic response capability of the platoon control system. By optimizing resource allocation, this strategy ensures secure and reliable signal transmission and effectively safeguards the platoon against potential malicious node attacks. Consequently, this research offers a novel solution for achieving both security and efficiency in ICV platoon control.

This paper proposes a trust-evaluation-based distributed model predictive control (DMPC) strategy for safe and scalable consensus of heterogeneous multi-agent systems (MASs) in a zero-trust environment. Dempster-Shafer theory (DST) is employed to quantify inter-agent trustworthiness and derive a global trust metric, thereby mitigating the impact of network threats and false testimonies during the trust evaluation process. Based on the computed trust, artificial reference trajectories are constructed to define the safe and scalable consensus state that each agent tracks, enabling adaptive regulation of reliance on neighbor information through real-time trust weights. The consensus problem is then reformulated as a trust-aware DMPC tracking problem that depends solely on locally received information, supporting distributed decision-making under zero-trust communication conditions. Sufficient conditions are derived to ensure recursive feasibility of the optimization problem, exponential stability of the closed-loop system, and the achievement of safe and scalable consensus. The effectiveness of the proposed strategy is validated via numerical simulations and vehicle platoon experiments.

The use of intelligent connected vehicles (ICVs) is an emerging concept in transportation systems, where the platoon leader processes various types of information from followers and roadside units to make accurate decisions. However, in complex network environments, the leader election process can become complicated and inefficient due to potential network attacks and abnormal node behaviours. To address this challenge, this paper presents a leader vehicle election mechanism based on dynamic trust assessment within a zero-trust framework. The proposed mechanism comprises two key components: dynamic trust assessment and adaptive leader election. The dynamic trust assessment method involves a trust evaluation mechanism composed of direct trust assessment and recommendation trust assessment based on the state information of vehicle nodes, where the dynamic trust assessment results are adjusted according to node behaviour information. The leader election mechanism involves periodic elections among the vehicle nodes on the basis of the results of the dynamic trust assessment, in which the vehicle node with the most votes is selected as the leader. Through comparative experiments against a full trust scenario, the proposed method demonstrates superior performance in resisting network attacks and identifying abnormal nodes, thereby significantly improving the safety, stability, and operational efficiency of the vehicle platoon.

In modern industrial scenarios, federated learning (FL) has been widely adopted due to its advantages in privacy preservation and distributed modeling. However, existing FL approaches rely on a single update paradigm, which significantly hampers communication efficiency. Moreover, the server preserves historical aggregation states to model causal propagation across training rounds, instead of performing memoryless per-round aggregation. For this end, we propose a self-adapting federated continual learning with gradient causal constraint (GCC-SFCL). First, the selection of synchronous or asynchronous communication across FL rounds is reformulated as continual learning task, where an experience accumulation mechanism records historical aggregation states to model long-term temporal dependencies among client updates. Based on the aggregated context, we further design a self-adapting scheduling strategy driven by multidimensional client state information, enabling the server to dynamically select optimal aggregation. Furthermore, a causality-steering gradient constraint is introduced to explicitly regulate gradient updates, improving the interpretability of the causal model and allowing the capture of fault propagation paths. Experimental results of a simulation case and the real-world case demonstrate that GCC-SFCL significantly improves not only communication efficiency but also the accuracy of fault propagation path identification.

This paper investigates the problem of distributed state monitoring for intelligent connected vehicle (ICV) fleets based on embedded trust levels within a zero-trust network architecture (ZTNA) while considering the impact of unknown input disturbances. The ZTNA significantly enhances the overall security performance of the vehicle fleet. To implement the “never trust, always verify” principle, we first analyze the effects of zero-trust architecture on the communication relationships among intelligent connected vehicles (ICVs). Drawing inspiration from human trust dynamics, rules for establishing and reducing trust levels between vehicles are formulated. A variable-directed graph is employed to represent the communication topology of the entire vehicle fleet. Next, a distributed unknown input monitor structure is proposed using known information and verified global state monitoring results from neighboring vehicles with embedded trust levels. By utilizing selectable matrices to process local and global control inputs, any individual vehicle can monitor the state of the entire fleet. Finally, a simulation analysis of a fleet of six vehicle nodes demonstrates the correctness and effectiveness of the proposed method.

This letter addresses distributed nonconvex constrained optimization problems, where both the local cost function and the inequality constraint function are nonconvex. Firstly, the global nonlinear equality constraint is added to the global cost function via a penalty-like function method. Then, based on the consensus technique of multiagent systems, the global nonlinear equality constraint is estimated through a distributed nonlinear consensus scheme within a finite time. Secondly, the local inequality constraint is managed with an adaptive penalty factor. Thirdly, the optimal outcome is attained by employing the gradient of the augmented Lagrangian function. The stability analysis is performed using the Lyapunov theory. Lastly, a simulation case on the economic dispatch problem in smart grids is presented to clarify the developed theoretical result.

Most of the past research on platooning control has not considered the impact of changes in the level of trust between vehicles in the platoon on the platoon control, and the communication process between connected vehicles is often subjected to malicious attacks such as time delays or interruptions, as well as tampering with status information, and the level of trust between vehicles changes, which affects the cooperative behaviors such as driving styles and inter-vehicle interval strategies. Changes in the level of trust between vehicles can affect the control process strategy of the vehicle, such as where the spacing strategy changes, thereby affecting the inputs to the controller, which in turn affects the outputs of the controller, and ultimately affects changes in the driving style of the vehicle, so considering the level of trust between vehicles in vehicle platoon control is critical to the safe operation of the vehicle platoon. To address this challenge, this paper proposes a dynamic splitting and merging control strategy for vehicle platoons based on the trust evaluation of vehicle nodes. Firstly, trust is evaluated using the Certainty Factor (C-F) uncertain reasoning model, which is a process that starts from initial evidence of uncertainty and derives reasonable conclusions with a certain degree of uncertainty by utilizing the uncertainty of evidence. An autoregressive model predicts short-term vehicle trajectories, and multi-source information from communication and perception is compared to determine vehicle node trust. Utilizing Bayesian reasoning methods, the trust level of vehicle nodes is updated. Based on traditional platoon control and trust evaluation, a software-level dynamic splitting and merging strategy is proposed to enhance resilience against unknown disturbances in a zero-trust environment. Finally, the system’s internal and string stability are analyzed, and the scheme’s effectiveness is validated through simulations. Note to Practitioners—The motivation of this paper is to solve the problem of vehicle platoon security control for connected vehicles in default distrust scenarios. Previous approaches were designed under the premise of considering mutual trust between vehicle nodes, ignoring the problems of inaccurate information interaction between connected vehicles and attacked interaction processes in real scenarios. To resist the risks of various types of attacks faced by intelligent networked vehicles in real traffic scenarios, this paper designs a vehicle platoon control strategy under zero-trust scenarios. Firstly, using the C-F uncertainty reasoning method, we propose a data-based node trust evaluation algorithm, and utilize its node trust evaluation results to re-establish the vehicle state equation under the zero-trust scenario. To make the vehicle platoon control under the zero-trust scenario scalable and resilient, this paper further proposes a vehicle platoon dynamic splitting and merging strategy based on the vehicle node trust evaluation scheme without changing the original communication topology of the vehicle platoon. The preliminary experimental results show that compared with the previous vehicle platoon control method under default vehicle node trust, the security of vehicle platoon operation is effectively improved. In the future, to better match real traffic scenarios, we will study how to dynamically adjust the trust threshold of vehicle nodes based on the vehicle operation environment, spacing strategy, sensor parameters, and other factors.

This article develops a cooperative-critic learning-based secure tracking control (CLSTC) method for unknown nonlinear systems in the presence of multisensor faults. By introducing a low-pass filter, the sensor faults are transformed into "pseudo" actuator faults, and an augmented system that integrates the system state and the filter output is constructed. To reduce design costs, a joint neural network Luenberger observer (NNLO) structure is established by using neural network and input/output data of the system to identify unknown system dynamics and sensor faults online. To achieve the optimal secure tracking control, an augmented tracking system is formed by integrating the dynamics of tracking error, reference trajectory, and filter output. Then, a novel cost function is designed for the augmented tracking system, which employs the fault estimation and the discount factor. The Hamilton-Jacobi-Bellman equation is solved to obtain the CLSTC strategy through an adaptive critic structure with cooperative tuning laws. Besides, the Lyapunov stability theorem is utilized to prove that all signals of the closed-loop system converge to a small neighborhood of the equilibrium point. Simulation results demonstrate that the proposed control method has good fault tolerance performance and is suitable for solving secure control problems of nonlinear systems with various sensor faults.

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.

Extended object detection and tracking (EODT) is becoming a promising alternative for autonomous perception, which provides not only common motion states but also accurate spatial extent information, such as shape and size estimations. However, due to uncoordinated radar transmissions in zero-trust autonomous driving scenarios, radar-based EODT systems suffer from mutual radio frequency (RF) interference launched by attackers, leading to ghost targets and increased noise. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this paper. In contrast to pioneering works that treat object detection and tracking as two separate steps, the proposed method handles them jointly by integrating a continuous detection process into tracking, improving the detectability of weak targets. More specifically, to accommodate the time-varying number and extended size of radar reflections, an adaptive spatial distribution model representing the deformable extents is incorporated to capture the contour evolution over time. The key insight is that by accumulating the reflected power, all backscattered points are regarded as one entity to match the real target so that the intractable data association problem can be circumvented in the proposed method. Unlike the prominent random matrix model-based approaches that split motion and extent states into independent parts, this study explores the interdependencies between the states and updates them simultaneously. In addition, the proposed system has been deployed on a low-cost automotive radar platform. Experimental results confirm that the proposed approach can achieve accurate and resilient EODT against RF interference attacks, especially in occlusion, dynamic motion switching, and complex multiple extended target tracking scenarios. A demonstration video with EODT results is available in the supplementary materials.

Due to the reduction of imaging accuracy caused by the approximations of signal models, traditional synthetic aperture radar(SAR) moving target motion parameter estimation methods based on frequency domain imaging algorithms may suffer from the problem of accuracy reduction. To solve this problem, a novel moving target motion parameter estimation scheme is proposed for dual-channel SAR based on the time domain back projection(BP) algorithm. First, the BP imaging model of a moving target is constructed for the dual-channel SAR, and the focus position and the phase response of the moving target are analyzed. We show that the radial velocity of the moving target is proportional to the center frequency of the azimuth wavenumber spectrum, which can be used to estimate the radial velocity. Afterwards, the displaced phase center antenna based on BP is deduced for the clutter suppression, and the constant false alarm rate detector is used to detect moving targets. Then, the azimuth offset of the moving target between the two sub-aperture images is used to estimate the azimuth velocity, since it is proportional to the azimuth velocity. Meanwhile, a modified refocussing method is applied for a more accurate azimuth velocity estimation. Furthermore, the slant-range velocity is calculated by the geometric relationship among the radial velocity, azimuth velocity, and the slant-range velocity. The simulation and semi-physical simulation experiments verify that the proposed scheme can achieve higher accuracy in motion parameter estimation than the method based on the frequency domain imaging algorithm in both the side-looking and squint-looking dual-channel SAR.

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.

Due to the small radar cross section (RCS), pedestrians are typical low-observable traffic participants for radar-based automotive perception systems. The early detection and understanding of pedestrians’ activities are of great significance to automotive safety. To this end, this paper presents an end-to-end joint tracking and activity estimation (RETA) system based on 4D automotive radar, which deals in particular with pedestrian activity identification under cluttered real-world scenes. Firstly, a novel integrated detection and tracking algorithm is proposed to guarantee positioning accuracy, in which all unthresholded 4D radar measurements are incorporated to explore the spatial coherent information across multiple frames, avoiding weak target information loss. After that, to discriminate continuous activities with varying durations in sequential trajectories, this paper innovatively presents a decomposed connectionist recurrent convolutional neural network, which facilitates fused temporal-spatial motion feature extraction. Especially, the labor-consuming activity pre-segmentation problem is circumvented with the help of a connectionist temporal classification algorithm in the proposed neural network. At last, RETA can be implemented for real end-to-end perception applications. Extensive experiment results highlight its superiority and effectiveness by attaining a continuous recognition accuracy of 94.8%. To the best of our knowledge, this is the first end-to-end activity recognition system specific for low-observable pedestrians. A demonstration video recorded in challenging practical traffic scenarios has been uploaded in the supplementary materials.

In real networks, communication constraints often prevent the full exchange of information between nodes, which is inevitable. This brief investigates the problem of time delay and randomly missing data in Boolean networks (BNs). A Bernoulli random variable is assigned to each node to characterize the probability of data packet dropout. Time delay and missing data are modeled by independent random variables. A novel data-sending rule that incorporates both communication constraints is proposed. An augmented system, comprising current states, delayed information, and successfully transmitted data, is established for theoretical analysis. Using the semitensor product (STP), the necessary and sufficient condition for asymptotic stability of delayed BNs with random data dropouts is derived. The convergence rate is also obtained.

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.

Due to the weak radar echoes and strong background clutters in low-altitude airspace, the detection and tracking for multiple micro-unmanned aerial vehicles (UAVs) have posed formidable challenges in radar surveillance field. Consequently, this paper proposes an end-to-end detection and tracking framework (E2DTF) for multiple micro-UAVs by utilizing the frequency modulated continuous wave-multiple input multiple output (FMCW-MIMO) radar. To address the low signal-to-noise ratio (SNR) problem, E2DTF presents a frame-range-Doppler-azimuth information fusion filter to integrate the target energy by exploiting the spatio-temporal dependence of positions within a sequence of unthresholded frames. Additionally, considering that a target may enter/leave the radar field-of-view (FOV), E2DTF introduces a target model state, updated by an extended Markov state transition matrix sequentially, to realize an unknown, time-varying number of micro-UAVs tracking. Another nice feature of E2DTF is that it avoids the complex data association procedure thanks to removing the threshold-decision operation. Finally, both numerical simulations and experiments with five different scenarios, i.e., horizontal line, cross-trajectory, circular loop, rainy condition and 3D trajectory tracking are presented to verify the effectiveness of the proposed method. The results show that E2DTF can obtain superior detection and tracking performance for multiple micro-UAVs in contrast to the state-of-the-art methods considering detection and tracking processes independently, especially under 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.

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

To deal with inconsistencies coming from consideration of criteria, that is, attributes with preference-ordered domains (scales), such as product quality, market share, and debt ratio, the dominance-based rough set approach has been proposed. However, it is not much useful for analyzing incomplete information. In this paper, we attempt to present research focusing on incomplete ordered decision systems where all attributes are considered as criteria and some objects’ criterion values are unknown. Under the assumption that all unknown values are the “do not care” semantics, the concept of tolerance dominance relation is introduced. Then, the rough approximations induced by it are obtained. And their properties are investigated. Especially, the important properties of rough approximations, such as rough inclusion, complementarity, identity, and monotonicity, are preserved well-known in the presented methodology. Moreover, an example is employed to illustrate these results. We expand the potential applications of dominance-based rough set approach.

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.