In IoT-enabled V2X cooperative vehicle–infrastructure perception, roadside units must transmit informative representations to vehicles over bandwidth-limited and latency-sensitive links, making it challenging for single-modality transmission to reduce communication payload while preserving detection accuracy. This paper presents a communication-efficient V2X cooperative 3D object detection framework, termed QMDet (Quantization-and-Mask based Detector). In the proposed design, the roadside unit jointly transmits a low-bit compressed feature map and a semantic mask to the vehicle. The feature branch performs learnable joint channel–spatial compression followed by INT8 quantization. In parallel, the semantic branch conducts contextual modeling and generates a semantic mask, providing explicit semantic priors to compensate for information loss caused by compression and quantization. Furthermore, a gated fusion module is introduced to adaptively integrate vehicle-side features, decompressed roadside features, and semantic priors via dynamic weighting, thereby mitigating cross-view/domain discrepancies. Experiments on the DAIR-V2X-C dataset demonstrate that the proposed framework reduces the RSU-to-vehicle communication payload by 24% while maintaining detection accuracy, achieving gains of 0.11% on [email protected] and 0.13% on [email protected]. These results indicate that QMDet improves communication efficiency without sacrificing cooperative detection accuracy.

Cellular vehicle-to-everything (C-V2X) communication is essential for supporting diverse vehicle applications in cooperative vehicle-infrastructure systems (CVISs). However, various factors, such as building obstructions, signal blockages, and network conditions, severely affect the latency and reliability of V2V communication. To address this challenge, we propose a novel dynamic method for communication mode selection and resource allocation in C-V2X, termed MSRA, which enables vehicles to dynamically select and utilize different communication modes, such as V2V, Vehicle-to-Infrastructure (V2I), and Vehicle-to-Network (V2N) for direct, relay, or forwarding communication, based on real-time conditions. To this end, we formulate the joint optimization problem of mode selection and resource allocation as a Markov decision process (MDP) and propose a solution based on multiagent reinforcement learning (MARL) with centralized training and decentralized execution. Specifically, each vehicle acts as an agent, independently selecting communication modes and resources based on real-time network status and communication link quality, aiming to satisfy the latency and reliability requirements of V2V communication while maximizing the capacity of V2N communication based on heterogeneous Quality-of-Service (QoS) requirements. Simulation results indicate that the proposed algorithm significantly outperforms other decentralized baselines and demonstrates superior performance under various conditions.

The inefficient capture of equipment anomalies has impeded the effective training of models for detecting anomalies in various traffic equipment (TE). This paper proposes an unsupervised knowledge distillation for traffic equipment anomaly detection (UKD-TEAD), eliminating the need for numerous annotations and ensuring the applicability to various equipment anomalies. First, three specialized detection heads based on different object category aspect ratio distributions are designed, to detect multi-scale objects with high precision in complex traffic scenes. Second, a teacher-student model, grounded in hierarchical knowledge distillation, is developed to mitigate the critical feature loss associated with the small size of cropped regions of interest (ROIs). By performing knowledge distillation at different depths of the network, the student network effectively learns the representation capabilities of the teacher network on multiple scale feature layers, thereby improving the anomaly detection performance. Finally, to validate the proposed unsupervised anomaly detection framework, a target detection dataset and an unsupervised anomaly detection dataset were constructed based on traffic inspection data. Experimental results show that the proposed method achieves an [email protected] of 0.862 for TE detection, while the mean area under the curve (mAUC) for anomaly detection reaches 0.857.

Autonomous driving (AD) is critically dependent on intelligent decision-making technology, which is the crucial ingredient in driving safety and overall vehicle performance. And comprehensive consideration of driving heterogeneity, decision synergy, and game interaction is also the cornerstones. Accordingly, this paper constructs a cooperative decision-making framework for autonomous vehicles (AVs) that integrates driving styles within a hierarchical architecture based on deep reinforcement learning (DRL). The upper layer adopts the action shielding mechanism-based dueling-double deep Q-network (D3QN) algorithm incorporating the lane advantages into shared state space to complete the prompt lane-changing (LC) decision, the lower layer applies the soft actor-3-critic (SA3C) algorithm based on the clipped triple Q-learning to provide the continuous speed adaptive control. Three personalized collaborative decision strategies are formulated for particular driving styles in multi-objective optimization preference combined with style-incentive prioritized experience replay (SIPER). The experimental results confirm that the proposed framework can satisfy the personalized driving demands in complex traffic scenarios, effectively explore the prospective LC opportunities, and enhance the driving efficiency by 35.40% with aggressive strategy and the comfort by 56.46% with defensive strategy compared with normal strategy, while maintaining the safety.

A hybrid active-passive wireless sensor network (HWSN) is a cost-effective and energy-efficient way for localization systems. The active sensor nodes, which are targets, can locate themselves by sending wireless power signals to power up the passive sensors, or via cooperative localization among targets. The common used power allocation approaches e.g., semi-definite programming (SDP), are aiming to minimize the overall localization error with energy constraints. However, the global optimum may lead to issues such as decreased individual performance and unbalanced network resources. To address the above challenges, we propose a game theory power allocation framework for the cooperative localization of HWSNs in this paper. We present the Fisher information matrix (FIM) and the corresponding squared position error bound (SPEB) for two types of HWSNs, which are the general cooperation network and tree topology network. For the general cooperation network, we derive the closed-form solution of optimal power allocation using the general bargaining equilibrium. In the tree topology network, we propose a Stackelberg equilibrium approach that effectively adapts the power allocation scheme based on the obtained hierarchical information structure. Extensive simulation results demonstrate that the proposed method outperforms semidefinite programming (SDP) and equal power allocation scheme (EQ) in position estimation error. Specifically, compared to other schemes, our proposed scheme has improved the positioning accuracy by 75% in the general cooperation network and by 70% in the tree topology network.

For future 6G systems, reconfigurable intelligent surface (RIS) controls phase shift of the reflective unit to improve the channel, which affects the received signal strength (RSS) of the microwave. In this paper, we introduce the RIS as anchors into the cooperative localization wireless sensor network (RACLN) to locate the sensor nodes. The RIS in RACLN can be a base station that provides multiple passive antennas for highly accurate localization. We derive the Cramér-Rao lower bound (CRLB) and the related squared position error bound (SPEB) for RACLN. The formulations indicate that the phase shift control of the RIS can improve the localization accuracy effectively. However, determining the appropriate phase shifts poses a nonlinear, non-convex integer programming problem. Thus, we propose a semi-definite programming (SDP) based phase optimization algorithm (SDP-PO) by relaxing the objective and achieving the minimum SPEB. Further, we also develop a low-complexity phase optimization (LC-PO) algorithm to reduce the dimension of the phase shift vector of SDP-PO. The simulation results demonstrate that the SPEB of RACLN is 94:68% smaller than the WSN using sensor nodes as anchors. In addition, The proposed SDP-PO algorithms outperform the genetic algorithm (GA) and the alternate optimization (AO) with 17.12% and 37.49%.

The security of data throughout its entire life cycle has become the most urgent and fundamental security issue in the digital economy era. With the continuous expansion and deepening of vehicle-infrastructure-cloud integration (VICI), data among the vehicle, infrastructure, and cloud are interconnected, leading to a constant extension in the scope of automotive data. Focusing solely on vehicle-end data is no longer sufficient to meet the developmental needs of intelligent connected vehicles. Considering data security from the perspective of VICI holds significant importance for ensuring the safety of the entire life cycle of intelligent connected vehicle data. In this context, this paper focuses on data security for VICI, analyzes the scope and characteristics of the data, explores the key challenges, and elaborates on the application of data security technology for VICI. Building upon this, the paper proposes DSA-VICI and conducts an analysis of the development trends in data security for VICI.

In recent years, in-vehicle applications based on vehicular ad hoc networks (VANETs) have been continuously expanded. Many applications not only focus on delay and effective forwarding rate but also pay more attention to routing path multiplexing and throughput. However, in VANETs, it is challenging to establish real-time and robust multihop forwarding paths due to volatile topological information, disconnected network, churn rate, etc. In order to adapt to the new development trend of VANETs, a self-healing routing strategy (SR) with the ant colony optimization (ACO) is proposed in this article. SR introduces the ACO algorithm to establish routing paths to ensure connectivity and immediacy. The routing-build-ability (RBA) is defined to measure the forwarding capability of a vehicle. The RBA is derived from the delay and packet delivery ratio (PDR) using the fuzzy logic system, which can reduce the computational complexity. To reduce the overhead of path reconstruction performed due to path disconnection, in-road-repairing and intersection-repairing methods are proposed in this article, which prolong the duration of the optimal path and improve throughput. The simulation results and mathematical analyses demonstrate that the feasible SR can reduce the delay by 30%, shorten the time overhead to one sixth, promote the routing duration by three times, and enhance the throughput by three times.

C-V2X, as an end-to-end wireless communication network for intelligent transportation system, may lead to degradation of its communication quality in the case of high vehicle density, and the large-scale field test provides a test environment for its possible situation. whether the communication performance of C-V2X system in the large-scale field test can meet the technical requirements is the premise of whether C-V2X system can be applied and promoted in China. At this stage, the data after the large-scale test is only analyzed in terms of time delay and packet error rate(PER), and only the analysis of these two indicators is not able to judge whether the equipment meets the technical indicators in the large-scale scenario. In this paper, we propose an analysis and evaluation scheme for the data after the communication performance of vehicle devices in large-scale tests, add the evaluation indexes of interval delay jitter and throughput, and propose some improvement schemes for the existing test data saving format.

Intelligent connected vehicles exchange real-time driving information via wireless communication. To ensure corresponding application performance, field test is needed. Obviously, V2V communication performance is influenced by several factors, such as vehicle density, road factor, weather, etc., and bring great challenge for test scheme design, which is considered as a serious hinder of V2X market process. In this paper, a density-oriented test scheme is proposed. Both communication performance and run-time application performance tests are considered, and both software and hardware architectures are designed. Moreover, the interfaces and a control procedure are given. Then the test system is deployed in test field while tests are done to verify the effectiveness of proposed test scheme design.

The complex traffic environment of the intersection region makes traffic accidents occur frequently in this section. Intelligent network vehicles can give early warning of possible dangers through communication between vehicles and other traffic participants, so as to reduce the occurrence of traffic accidents. At present, many automobile enterprises are studying the landing application of vehicle to everything (V2X). This paper focuses on the application requirements of intersection collision warning (ICW) to V2X. Testing is an integral part of vehicle internet and can ensure safe and effective use of vehicle internet.In this paper, we describe in detail the testing process and results of communications performance of vehicle-to-vehicle (V2V) at an obstructed intersection in a closed test field where there are a lot of cars as a background. In addition, we have proposed a message forwarding mechanism based on vehicle to vehicle to infrastructure to vehicle (V2I2V), and the test results show that V2I2V performs better than V2V at an intersection with obstacles.

In the communication of devices based on C-V2X, packet error rate (PER) is an important metric to measure the communication performance of a device. As for the packet loss phenomenon, we usually focus on why the receiver did not successfully receive the message, and rarely focus on whether the transmitter actually sent the message.We usually consider the messages sending situation recorded by the application layer as the messages that should be received by the receiver (packets that are known to be not sent by the application layer due to application layer congestion control, etc., are not in the scope of this paper). However, in the actual communication process, there are some discrepancies between the real packets sent from the bottom layer and the application layer's records. In the 2020 C-V2X Large-scale Pilot Demonstration, when we analyzed the results and calculated the received PER of the devices, we were confused whether some of the devices did not send all the packets successfully. Based on this confusion, we defined the concept of transmitter traffic to represent the actual packet sending situation of the device. We designed a method to calculate transmitter traffic by using the "large-scale" data available, and conducted statistics on the transmitter traffic of more than 40 terminal companies, more than 10 chip module companies, and more than 50 participating devices. We analyzed the statistical results, and analyzed the possible reasons for the unsuccessful transmitter traffic.

Protocol conformance test plays a necessary and important role in guaranteeing that Long Term Evolution-Vehicle to Everything (LTE-V2X) devices to meet the standards and achieve interoperability. Based on in-depth analyzing 3rd Generation Partnership Project (3GPP) release 14 and China LTE-V2X standards, in this paper, a LTE-V2X Conformance Testing Scheme is designed, while corresponding test process and message sets are defined. Test suite are designed according to message layer and network layer requirements. Finally, a test tool, VeTest, is proposed to realize conformance test. The work is significance for promoting the LTE-V2X standardization and interoperability.

In this paper, we propose a cooperative motion planning method for a group of connected and automated vehicles (CAVs) crossing a lane-free intersection without using explicit traffic signaling. This multi-vehicle motion planning task is formulated as a centralized optimal control problem. However, the solution to this optimal control problem is numerically intractable due to the high dimensionality of the collision-avoidance constraints and the nonlinearity of the vehicle kinematics. A two-stage strategy is proposed for generating online solutions: at Stage 1, the CAVs are requested to reach a standard formation before entering the intersection; at Stage 2, the vehicles cross the intersection. As the motion planning sub-problem at Stage 2 begins with a standard configuration, the optimal solution to this standard sub-problem can be computed offline in advance and applied online directly. On the other hand, the formation reconfiguration sub-problem at Stage 1 is easy to solve online. Through dividing the entire dynamic process into two periods, the difficulties in the original optimal control problem are significantly reduced so that the real-time performance is achieved.

This work formulates the multi-vehicle lane change motion planning task as a centralized optimal control problem, which is beneficial in being generic and complete. However, a direct solution to this optimal control problem is numerically intractable due to the dimensionality of the collision-avoidance constraints and nonlinearity of the vehicle kinematics. A progressively constrained dynamic optimization (PCDO) method is proposed to facilitate the numerical solving process of this complicated problem. PCDO guarantees to efficiently obtain an optimum to the original optimal control problem via solving a sequence of simplified problems which gradually judge and reserve only the active collision-avoidance constraints. A first-regularization-then-action strategy, together with the look-up table technique, is developed for online solutions. At the regularization stage, the vehicles form a standard formation by linear acceleration/deceleration only. At the action stage, the vehicles execute lane change motions computed offline and recorded in the look-up table. This makes online motion planning feasible because 1) the computational complexity at the regularization stage scales linearly rather than exponentially with the vehicle number; and 2) online computation at the action stage is fully avoided through data extraction from the look-up table.

Smartphones have become an important component of everyday's life. They store a large amount of users' private and sensitive information like contacts, GPS location, messages and interests. Privacy issues are a growing concern for the phone users. However, despite an existing rich literature in privacy leakage on mobile network measurement, our empirical knowledge of users' private leakage is relatively limited. In this work, we present a large scale and comprehensive investigation spanning over 9 months of users' private information leakage that consisted of monitoring 180K popular apps coming from 50+ Chinese AppStores. In order to do this, we used a customized platform that can monitor the execution of applications running over Android system to observe in vivo privacy leakage of applications. Our key findings are that: (1) Accessing users' private information is very common among mobile apps, i.e. over 90% of apps accesses some kind of user private information, and to our surprise, almost 95% apps claimed access to private information without concretely accessing them (2) We analyzed different category of Apps and observed slight differences in the pattern of access to private information among different categories (3) Downloading apps from big Appstores does not necessarily mean safer and more private apps. We observe that local Chinese shop and Google Play generate similar observations.

In ad hoc networks, each node is required to forward packets for others. However, based on energy consumption considerations, a node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. Most of the existing research work assumes that all the nodes in the network should provide full cooperation in order to optimize the transmission efficiency. However, this assumption is too strict because the activities of the nodes in ad hoc networks have some inherent uncertainty. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. Our scheme does not require all the nodes in the ad hoc network to absolutely cooperate with each other. The simulation results show that, compared with the existing schemes, our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by ∼25% and has good scalability.

Based on energy consumption considerations, an ad hoc network node may reject other nodes' forwarding requests to save the limited battery power for its own data transmission. Therefore, a lot of incentive schemes have been proposed to promote the cooperation of the nodes. The utilization of the incentive schemes makes the nodes willing to cooperate with each other, because their non-cooperation can be punished in the future. However, the activities of the nodes in ad hoc networks have some inherent uncertainty. For example, the batteries of some nodes are exhausted or some nodes move to other regions. Under these situations, the existing incentive schemes are no longer effective and the nodes have to terminate their cooperation and stop forwarding packets for others. In this paper, we propose a power control mechanism in ad hoc networks under a dynamic repeated game-theoretic framework. A notion of nodes' evaluation levels for the future experiences is defined to take account of the non-cooperation due to the inherent uncertainty in the ad hoc network nodes' activities. The nodes achieve their optimal transmission efficiency by using a two-step power control mechanism. The simulation results show that compared with the existing schemes our power control mechanism considering non-cooperative packet forwarding improves the average transmission efficiency by approximately 25%.

In ad hoc networks, reducing energy consumption and improving throughput are both important for high network performance. This paper presents a joint power and rate control adaptive algorithm to optimize the trade-off between power consumption and throughput in ad hoc networks. Each node chooses its own transmission power and rate based on limited environment information in order to achieve optimal transmission efficiency. In a fictitious game framework with strategy space transformation, our joint power and rate control adaptive algorithm can be viewed as a supermodular game. By interpreting the supermodular game using myopic best response updates, this algorithm can converge to the unique optimal transmission efficiency. Finally, the simulation results show that this supermodular game approach improves the average transmission efficiency by about 33%.

With the development of heterogeneous networks, multimode terminals are becoming more and more popular. However, when there are several different kinds of sessions requiring transmission simultaneously, how to distribute these sessions among the available access networks according to the different features of the flows and the current link conditions of the candidate networks is a new challenge. In this paper, we propose a new solution to the flow distribution problem for multimode terminals. Our proposal, automatic flow distribution (AFD), includes a network selection algorithm located at the terminals and an admission control algorithm located at the access points of the networks. Consequently, the terminals and the networks can cooperate with each other and realize automatic flow distribution among the different available access networks. We utilize the notion of priority, ensuring that the more important sessions have preferential use of the network resources. In addition, in order not to excessively deteriorate the transmission performance of the lower priority flows, a probabilistic suspension scheme is introduced. Finally, the AFD method utilizes the concept of "entropy" to automatically compute the weights of different attributes which influence the flow distribution decision making, thus avoiding the users' difficulty to specify the weights manually.

This paper focuses on the issue of flow distribution for multimode mobile terminals in heterogeneous networks, and proposes a flow distribution algorithm based on priority and weight self-production mechanism. The algorithm utilizes the notion of "priority" , assigning higher priorities to more important sessions, therefore ensuring these sessions have the preferential utilization of the network resources. In addition, this paper proposes a new method to automatically determine the weights of the multiple attributes which influence the session flow distribution decision, thus avoiding the difficulty and irrationality when relying on the users to specify the weights directly. Simulation results show that our algorithm can efficiently distribute the session flows on a multimode mobile terminal to the appropriate networks for transmission based on the current conditions of different access networks, and on the QoS requirements of different types of sessions. The decisions made by our algorithm reduce the monetary cost and power consumption of the communication as well as guarantee the QoS of different types of sessions. Finally, our algorithm offers significant improvements in performance indicators such as adjacency to the optimal network and session completion rate.