Multi-vehicle collaborative sensing has emerged as a new paradigm to boost the safety of autonomous vehicles. The cornerstone of this vision is the real-time and accurate registration of mmWave radar point clouds among multiple vehicles. To accomplish this, we designmmReg, an innovative system capable of achieving centimeter-level and real-time sensing fusion between vehicles.mmRegconsists of three major components: (i) aSAR imaging-driven point cloud generationcomponent leverages SAR imaging to image sparse and disordered radar point clouds to generate high-quality point clouds; (ii) amotion-aware frame synchronizationcomponent can achieve the spatio-temporal alignment of point clouds between vehicles for effectively mitigating the impact of asynchronous radar frames; (iii) ashared object-based registrationcomponent can capture and understand the unique global position of shared objects, supporting real-time and accurate registration. We implement and evaluatemmRegon CARLA and real-world campus datasets. The results demonstrate thatmmRegcan improve the vehicle’s sensing range by 117% in an average of 99.91 ms, achieving a 4.82x improvement in accuracy.

Video transmission, as one of the indispensable core services in the Internet of Vehicles, is confronted with numerous challenges such as the dynamics in the Internet of Vehicles environment, the limited resources, and the demand of vehicles for high-quality user Experience (Quality of Experience, QoE). In real-time video streaming scenarios, multicast optimization strategies, including convex optimization, game theory, stochastic optimization, etc., usually only group based on channel quality without considering task types and priorities, which may lead to the mixture of urgent tasks and ordinary tasks, affecting QoE. Therefore, we propose a task-aware three-stage collaborative optimization framework (TARA-IoV). The objective is to prioritize critical mission video services under limited bandwidth while maximizing the overall QoE. The framework first performs dynamic and adaptive vehicle grouping based on multi-dimensional features including task type, priority, geographical location, and channel state. Second, it conducts QoE-driven video quality layer selection leveraging Scalable Video Coding (SVC). Finally, it employs a deep reinforcement learning agent to dynamically allocate bandwidth with explicit task priority awareness under resource constraints. Evaluations on a real-world vehicle trajectory dataset demonstrate that TARA-IoV achieves improved QoE performance and more stable video delivery compared with existing schemes.

Digital twin (DT) migration technology is pivotal for ensuring seamless synchronization between physical entities and their virtual counterparts. However, in complex urban environments, high device mobility, dynamic network topology, and uncertain wireless channel conditions often lead to suboptimal migration decisions. Such inefficiencies exacerbate resource contention and diminish migration timeliness, thereby increasing DT state deviation and compromising service quality. To address these challenges, this paper proposes an AP group-enhanced digital twin edge network model, where multi-antenna access points (APs) are integrated as relay nodes to optimize channel quality and transmission reliability. Building on this, a Group-Collaborative Multi-Agent Proximal Policy Optimization (GC-MAPPO) migration strategy is presented. The strategy formulates the migration problem as a partially observable Markov decision process (POMDP). Specifically, the K-means++ clustering algorithm is first employed to construct optimal AP collaborative groups for mobile devices; subsequently, the MAPPO algorithm is utilized to derive optimal migration policies in dynamic environments. Experimental results demonstrate that, compared to existing baselines, the proposed GC-MAPPO scheme reduces the average synchronization delay by 6.69% to 37.05% and decreases DT state deviation by 29.24% to 82.91%.

3D object detection is a crucial task for autonomous vehicles to perceive traffic environments. Existing methods typically employ tightly coupled feature fusion strategies with fixed sensor combinations, but it fails to adequately capture the modality-specific characteristics, resulting in suboptimal object detection performance. To this end, we propose Chameleon, a novel multi-sensor decoupling system capable of selecting the appropriate sensor combination for object detection across diverse traffic conditions, which consists of two key components: (i) anuncertainty-aware contribution calculationcomponent leverages the uncertainty perceived by sensors to predict the parameters of traffic factors, followed by evaluating their importance; (ii) amutual information-enhanced sensor combination optimizationcomponent leverages mutual information calculations to enhance the mixture of experts and improve the reliability of sensor combination predictions, followed by achieving adaptive multi-sensor decoupling to ensure accurate 3D object detection while reducing inference latency. We implement and evaluate Chameleon using the nuScenes and nuScenes-C datasets. The experimental results show that Chameleon achieves average improvements of 1.10% in the mAP and 0.61% in the NDS compared to the state-of-the-art method across various traffic scenes.

Millimeter wave radar has recently emerged as a promising modality for enabling pervasive gesture recognition while protecting user privacy. However, personalized user behaviors, interference from unexpected actions, and long-term variability in user gestures significantly degrade the accuracy of gesture and user identity recognition, thereby compromising the quality of user experiences. To this end, we designmmGeswith four key modules: (i) afine-grained feature extractorextracts micro-level features from the time-series radar data to identify users' personalized behaviors; (ii) auser-specific feature classifierextracts coarse-grained features from a global perspective, followed by analyzing the micro-details of gesture features to recognize the user; (iii) avoting-based multi-user recognizerretrieves all pre-trained models from the user model database, followed by obtaining the probability indicators of each to return recognition results; (iv) alifelong learning modelretains previous knowledge while adjusting its feature selection capabilities using newly collected data to adapt to gesture changes. We implement and evaluatemmGesusing three self-collected real-world radar datasets, demonstrating its superior performance compared to other state-of-the-art gesture recognition methods.

Rapid urban development and the steady increase in vehicle ownership have made traffic congestion a persistent challenge for transportation management. In this context, the capability to achieve both high-precision and efficient traffic flow prediction is paramount for modern transportation systems. In response to this challenge, this paper presents GFLDT, a novel prediction framework grounded in the integration of digital twin and graph federated learning, which leverages collaborative intelligence and the distributed computing capabilities of IoT devices. The framework organizes the digital twin network into two components: client twins and a global twin. By employing graph federated learning, the model can extract temporal patterns from local data on client twins while capturing complex spatial dependencies through the global twin. Leveraging realworld traffic datasets for testing, we show that GFLDT achieves accurate traffic flow prediction while effectively ensuring data privacy through the collaborative mechanism between client and global twins.

Infrastructure-assisted autonomous driving has become a new paradigm that enables autonomous vehicles to fuse sensor data and improve driving safety, where a key enabling technology for achieving this vision is to real-time and accurate registering 3-D mmWave radar point clouds between the infrastructure and the vehicle. To this end, we proposeArtemis, a novel lightweight system capable of achieving real-time registration with decimeter-level localization.Artemisconsists of three components: 1) a modal association-based salient object extraction component leverages the complementary advantages of cameras and radars to extract semantics and areas of salient objects for radar point clouds; 2) a salient object shape construction component extracts the shape contour of salient objects based on their inherent geometries; and 3) a contour-guided 3-D point cloud registration component combines two key strategies, keypoint matching strategy and early exit strategy, to quickly select keypoints and transformation directions for achieving accurate registration in real-time. We implement and evaluateArtemiswith two multiview datasets collected in the CARLA platform and campus. The experiment results show thatArtemisachieves an average registration error of 0.33 m within 32.26 ms.

With the wide application of emerging technologies such as ultra-high definition video in Vehicular Edge Computing (VEC), the massive heterogeneous video data generated by vehicles have put forward higher requirements for real-time performance, energy efficiency and accuracy of processing. However, higher video analysis accuracy often leads to an increase in delay and energy consumption. How to balance the relationship between the three is an urgent problem to be solved. Meanwhile, the balanced or fixed bandwidth allocation mechanism adopted by most studies often ignores the characteristic differences of video tasks, resulting in inefficient resource allocation. At the same time, the security risks in the Internet of vehicles cannot be ignored. In order to deal with these challenges, this paper proposed a distributed task offloading framework combining Analytic Hierarchy Process (AHP) and Deep Deterministic Policy Gradient (DDPG). An adaptive bandwidth allocation mechanism based on the characteristics of video tasks is designed, and an improved blockchain consensus mechanism is introduced to ensure the optimal offloading decision in a trusted environment. Experimental results show that compared with the existing offloading schemes, the proposed algorithm reduces the task offloading delay by about 7.54%, reduces the energy consumption by about 6.37%, and improves the accuracy of video analysis by about 5.02% while ensuring security.

Multi-modal data-driven object detection typically depends on large-scale datasets. However, under adverse weather conditions, these datasets often exhibit a long-tail distribution, resulting in a substantial decline in detection performance and posing risks to system safety in applications such as autonomous driving. To this end, we propose Cerberus, a real-time and high-accuracy object detection system, which consists of two key components: (i) asimilarity-driven proposal extractioncomponent, which utilizes pre-trained detectors and convolutional neural networks to enhance low-quality features and suppress noise across image, LiDAR, and radar data, followed by aiming to extract and refine multi-modal features, thereby improving the quality of object proposals; (ii) anadaptive weight-based box enhancementcomponent, which integrates a frozen-weight regionbased convolutional network sub-network with a sensor fusion mechanism, followed by refining bounding boxes by normalizing confidence vectors and dynamically weighting multi-modal features, ensuring accurate detection under adverse weather conditions. Experimental results demonstrate that Cerberus surpasses existing baselines, yielding performance gains of 2.7% in the mean average precision and 2.0% in the nuScenes detection score under adverse weather conditions.

Federated learning (FL) has been extensively utilized in distributed learning scenarios for the Internet of Vehicles (IoV). However, two key challenges exist: 1) gradients are frequently transmitted between vehicles during FL training, which reduces the communication timeliness between the traffic participants and 2) the fixed gradient compression method cannot sufficiently adapt to the dynamic IoV network topology. Therefore, we design an adaptive vehicle clustering method constructed according to multiple attributes, such as computational resources, communication distance, and latency. Accordingly, we propose a dynamic gradient compression strategy that filters similar local training models between vehicles and uses the Wasserstein distance to compute a sparsity threshold. This threshold acts as a dynamic compression factor that compresses gradient model parameters, reducing redundant parameter transmission. Furthermore, we conduct experiments using two datasets to evaluate the proposed strategy’s effectiveness. The compression ratio improved by 132- and 178-fold compared to the baselines, and the aggregated accuracy increased by an average of 10.13%. Additionally, experiments incorporating communication noise revealed that the aggregation model of the signal noise ratio is -29 dB.

Query exchange in the Social Internet of Vehicles (SIoV) can protect users’ trajectory information. However, this method lacks an appropriate incentive mechanism, which leads to cooperative users refusing to participate in query exchange. In order to provide cooperative users with incentives to participate in query exchange, this paper proposes a smart contract-based query exchange (SC-QE) trajectory privacy protection method. By creating a many-to-many smart contract, the method encourages the cooperative users to bid to the requesting users. Subsequently, in order to select a Best Similarity Deviation User (BSDU) for the requesting user to perform query exchange, the users in the smart contract are modeled as a weighted bipartite graph, and the matching between the requesting users and BSDUs is realized by means of a weighted bipartite graph best matching algorithm. Following successful verification of the query exchange transaction in the smart contract, the base station distributes rewards to the BSDU and uploads the query exchange transaction to the consortium blockchain. Experimental results show that compared with the deviation-based query exchange (DQE) method, the proposed method reduces the user processing time by 12% while increasing the continuous anonymous success rate by 29%. Therefore, the proposed method can reduce the service query time and improve the level of trajectory privacy protection.

With the emergence of a large number of computing resource-intensive applications and a variety of content delivery services, data in Internet of Vehicles (IoV) is exploding. In order to improve the service performance of IoV, Vehicle Edge Computing (VEC) accelerates the response process of content requests and reduces the backhaul burden of the base station by caching content at the nodes of the edge network. However, the existing caching strategies are usually affected by high computing and communication overhead, and can not well capture the dynamic changes and content popularity of the vehicle network. In order to solve these problems, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for Federated Learning (called CMCF), which integrates mobility prediction and consistent hashing into the content caching scheme, and uses the federated learning framework to optimize the cached content, then we use deep reinforcement learning algorithm to develop the optimal cooperative caching policy to reduce the average delay of content transmission. Extensive simulation results prove the superiority of our method. Compared with other advanced caching schemes, CMCF can increase the cache hit rate by 8.7% and reduce the average content delivery delay by 17.8%.

With the rapid advancement of in-vehicle communication technology, vehicular edge caching has garnered considerable attention as a pivotal technology to improve the efficiency of data transmission. However, existing studies often overlook the issues of increased average content access latency and decreased caching hit rate, stemming from the conflict between limited storage space in in-vehicle edge servers and vehicle mobility. To address these issues, this paper proposes a Multi-agent Federated Deep Reinforcement Learning based Collaborative Caching Strategy (MFDRL-CCS), leveraging Vehicle-to-Vehicle (V2V) communications. Specifically, we first perform vehicle connectivity prediction based on Recurrent Neural Network (RNN) considering the characteristics of vehicle nodes and their interrelations. Then, the optimal caching vehicle is selected based on the connectivity between vehicle nodes and the density of vehicle nodes. Meanwhile, a Multi-Head Attention Popularity Prediction (MHAPP) model is also constructed, which amalgamates multi-dimensional features, including historical popularity, social relationships, and geographic location, to predict content popularity. Finally, the edge collaborative caching model is formulated as a Markov Decision Process (MDP). Under the multi-agent competitive deep Q-learning framework, each vehicle learns the optimal caching strategy through an independent Q-network to maximize long-term rewards, and uses federated learning to train the caching replacement algorithm in a distributed manner. Compared to existing caching policies, the caching policy proposed in this paper improves the caching hit rate by approximately 19.8% and reduces the content access latency by about 12.5%.

User alignment refers to linking a user's accounts across multiple social networks, which is important for studying community discovery, recommendation systems, and other related fields. However, existing methods primarily perform user alignment by correlating user features, neglecting the causal relationship between network topology and user alignment, which makes it challenging to achieve superior user alignment accuracy and generalization capabilities. Therefore, we propose a counterfactual inference-based social network user-alignment algorithm (CINUA). This improves user connection retention due to the non-Euclidean geometric characterization of hyperbolic spaces. The similarity of aligned users is augmented using a hyperbolic graph attention network. User-feature embedding and fusion facilitate user relevance mining. Furthermore, there are causal relationships between network topology structure and user linkages. In various communities, there are some highly similar user pairs, and based on counterfactual inference, the network topology is adjusted to enhance sample diversity. Multilevel factual and counterfactual networks are constructed through iterative diffusion based on user alignment and their linkages. By integrating the users’ causal features in multiple networks, the accuracy and generalization capabilities of the user alignment model are effectively improved. In this article, the experimental results indicate that CINUA achieves a user alignment accuracy improvement of 5.98% and 3.03%, on two datasets respectively compared to the baseline methods on average. CINUA can achieve favorable alignment results even when the training dataset is small. This demonstrates that our algorithm can ensure both user alignment accuracy and generalization capability.

As the industry related to intelligent vehicles becomes increasingly mature, emerging in-vehicle applications and services are mushrooming. They have strict requirements on service response delay and network bandwidth. In the face of dynamic environment in the Internet of Vehicles (IoV), how to design an adaptive edge caching strategy becomes a challenge. To cope with this challenge, some researchers have introduced optimization methods based on learning algorithms in cooperative caching. However, general learning algorithms tend to waste bandwidth and computing resources on repetitive task. To this end, we make full use of the idle resources in the road to build a cooperative caching system and propose a Social-Aware Decentralized Cooperative caching (SADC) for IoV. This strategy uses the federated learning framework to train the collaborative caching algorithm based on Deep Reinforcement Learning (DRL). Among them, the Road Side Unit (RSU) is responsible for the training and updating of the global model, and vehicles use local data to provide local updates to the RSU, which then averages the updates provided by all vehicles to improve the shared model. In addition, we use the social network of vehicle users to obtain vehicle contact rates in different areas. The SADC strategy can reduce the content transmission latency and response time, thereby improving the experience quality of vehicle users. Compared with traditional caching strategies, this strategy reduces the average content access delay by about 20%. We also demonstrate the effectiveness of this strategy using an extensive set of experiments.

With the increasing popularity of video delivery among mobile users, the problem of explosive traffic growth becomes more and more serious for the mobile wireless networks. To deal with this problem, in this paper we propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Based on this idea, a Quality of VIDEO (QoV) oriented pricing incentive scheme, namely Vbargain, is proposed to stimulate mobile users to deliver video data collaboratively. More specifically, in our scheme, the network is regarded as a Virtual Market, and the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Driven by the profit-hunting nature of mobile users, the video data is delivered from the source to its destination. In order to gain deep insight into our scheme, we also analyze the overhead and the influence of start-up capital on the performance of video delivered theoretically, and give its lower bound and upper bound. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme and analysis.

With the rapid development of the Internet of Things (IoT) in 4G/5G deployments, the massive amount of network data generated by users has exploded, which has not only brought a revolution to human’s living, but also caused some malicious actors to utilize these data to attack the privacy of ordinary users. Therefore, it is crucial to identify the entity users behind multiple virtual accounts. Due to the low precision of user identification in the many-to-many mechanism of user identification, a random forest confirmation algorithm based on stable marriage matching (RFCA-SMM) is proposed in this study. It consists of three key steps: we first employ the stable marriage matching model to calculate the similarity between multiple users and utilize a scoring model to calculate the overall similarity of the users, after which candidate matching pairs are selected; second, we construct the random forest model that exploits a user similarity vector training set; afterward, the candidate matching pairs combine the secondary confirmation of the random forest model, which both improve the precision of the many-to-many user identification and protect private user data in the IoT. Extensive experiments are provided to demonstrate that the proposed algorithm improves precision rate, recall rate, and F-Measure (F1), as well as Area Under Curve (AUC).

Due to the varieties of services and the openness of network architectures, great challenges for information security of the 5G systems are posed. Although there exist various and heterogeneous security communication mechanisms, it is imperative to develop a more general and more ubiquitous authentication method for data security. In this paper, we propose for the 5G networks a novel multimedia authentication framework, which is based upon the trusted content representation (TCR). The framework is general and suitable for various multimedia contents, e.g., text, audio, and video. The generality of the framework is achieved by the TCR technique, which authenticates the contents’ semantics in both high and low levels. Analysis shows that the authentication framework is able to authenticate multimedia contents effectively in terms of active and passive authenticating ways.

Node buffer size has a big influence on performance of Mobile Opportunistic Networks (MONs). This is mainly because each node should temporarily cache packets to deal with the intermittently connected links. In this paper, we study fundamental bounds on node buffer size below which the network system can not achieve the expected performance. Given the condition that each link has the same probability p to be active, and q to be inactive during each time slot, there exits a critical value pcfrom a percolation perspective. If p > pc, the network is in the supercritical case, there is an achievable upper bound on the buffer size of nodes, independent of the inactive probability q. When pc, the network is in the subcritical case, and there exists a closed-form solution for buffer occupation, which is independent of the size of the network.

With the increasing popularity of video delivery among mobile users, the explosive traffic growth problem becomes more and more serious for the mobile wireless networks. We propose to exploit opportunistic transmission and the idea of crowdsourcing to offload mobile video traffic. Specifically, we propose a QoV (Quality of Video) oriented pricing incentive scheme, namely Vbargain, to stimulate mobile users to deliver video data collaboratively. In our scheme, the video packets are treated as commodities, which are dynamically priced according to their expected marginal gains on the quality of reconstructed video; the process of video delivery is regarded as a sequence of packet transactions which are modeled as two-person cooperative games. Our simulation results, based on both the synthetic and real-life traces of mobile users, verify the efficiency of our scheme.

Due to the ability to provide more precise and user friendly information, video streaming delivery over vehicular ad hoc networks (VANETs) has become a hot topic in recent years. In spite of many challenges, several routing schemes have been proposed. However, these schemes excessively focused on the minimization of delivery delay, and did not realize that immoderate utilization of wireless fading channels could incur high distortion due to high probabilities of video package loss and damage. Moreover, in these schemes, the interference from neighbors is not considered during the relay selection, which could decrease the delivery quality greatly. Therefore, in this paper, we take interference into account and formulate rate distortion model of live video streaming in VANETs. Based on the model, we propose a novel routing protocol, which could maximize the end-to-end delivery quality of live video streaming by seeking a balance between distortion and delay. The performance of our protocol is validated based on the real-life traces.

In recent years, more and more people begin to focus on data delivery through opportunistic contacts among mobile users, and in most case, the data that we expect to share with others are videos. Due to that the delivery of video data will consume numerous resources of devices, few people will provide such service for others without any benefit. Thus, this paper centers on the incentive mechanism design for video delivery in opportunistic networks. Up to now, several incentive schemes for opportunistic networks have been proposed, however, they all mainly focus on the encouragement of general data forwarding, and almost none of them aims to stimulate nodes to realize the performance objective of networks. In this paper, we propose a novel quality-oriented incentive mechanism for video delivery, which takes the characteristics of video data into account and formulates the video delivery among nodes as a game. This mechanism could not only encourage nodes to participate in the video delivery but also stimulate them to follow the defined rules to optimize the video delivery quality of whole networks. The performance of this mechanism is validated through extensive simulations.

In recent years, more and more people have begun to focus on data delivery among mobile users through opportunistic networks, and in most cases, such as emergency, traffic accident and disaster, we need to transmit video data to other users by the means of opportunistic contacts between mobile users. Meanwhile, the buffers of mobile wireless devices are usually limited, then some messages which have different importance on video recovery, will be dropped during transmission. Therefore, it's imperative to design efficient buffer management strategy to improve the video delivery quality. Several policies have been presented, but they are just designed for general data and not suitable for video transmission. In this paper, we comprehensively take the temporal correlation of video data and the diffusivity of messages into account, and propose DSVM, a novel buffer management strategy. Extensive simulations validate its performance, and up to about 3dB Peak Signal-to-Noise Ratio (PSNR) gain can be achieved over the state-of-the-art buffer management policies.

How to deliver live video streaming in vehicular ad hoc networks (VANETs) has been a hot topic in recent years. However, due to high vehicular speed, frequent disconnection and dynamic topology, it's difficult to guarantee the low-delay delivery of real-time data, and up to now, there exists no scheme that can deliver such kind of data with satisfied quality. In this paper, we take fully advantage of the existing wireless enabled traffic cameras, and propose CAVD, a novel live video streaming delivery strategy. As the core part of this strategy, we design an algorithm to adaptively select proper camera as Optimal Buffering Point (OBP) for each segment of live video streaming, and through the cooperations of these OBPs, the live video streaming could be delivered to a mobile target vehicle with the minimal start-up delay while satisfying the required visual quality and playback performance. Extensive simulations have been done on real-life taxi trace of Beijing, and the results show that our proposed strategy outperforms existing solutions greatly.