Traffic state estimation (TSE) is critical in underpinning the route planning of intelligent transportation systems (ITS). In light of vertical split traffic data might be from various entities, such as municipal authority (MA) and multiple mobility providers (MPs), vertical federated learning (VFL)-based TSE is proposed to resolve the vertical data privacy issue. However, due to discrepancies in data collection and missing data imputation technologies of MPs, the data quality of MPs regarding the same road segment might vary. To this end, we propose a reliable VFL-based TSE framework, including data provider selection and VFL model training. Concretely, given the high-dimension nature of traffic data, the MA will train a tiny mutual information (MI) model for data provider selection. After that, the MA will split the well-trained MI model into sub-models and top models and deploy them on MPs and MA, respectively, so as to preserve the nature of VFL. Eventually, upon MI models, the most representative MP of each road segment is selected for a reliable VFL model. Numerical simulation on real-world datasets shows that our framework augments the performance of traffic flow and traffic density by 11.23% and 21.15% in comparison with the baseline without data provider selection.

Advanced AI-Generated Content (AIGC) technologies have injected new impetus into teleoperation, enhancing its security and efficiency. Edge AIGC networks have been introduced to meet the stringent low-latency requirements of teleoperation. However, the inherent uncertainty of AIGC service quality and the need to incentivize AIGC service providers (ASPs) make the design of a robust incentive mechanism essential. This design is particularly challenging due to uncertainty and information asymmetry, as teleoperators have limited knowledge of the remaining resource capacities of ASPs. To this end, we propose a distributionally robust optimization (DRO)-based contract theory to design robust reward schemes for AIGC task offloading. Notably, our work extends the contract theory by integrating DRO, addressing the fundamental challenge of contract design under uncertainty. In this paper, we employ contract theory to model information asymmetry while utilizing DRO to capture the uncertainty in AIGC service quality. Given the inherent complexity of the original DRO-based contract theory problem, we reformulate it into an equivalent, tractable bi-level optimization problem. To efficiently solve this problem, we develop a Block Coordinate Descent (BCD)-based algorithm to derive robust reward schemes. Simulation results on our unitybased teleoperation platform demonstrate that the proposed method improves teleoperator utility by 2.7% to 10.74% under varying degrees of AIGC service quality shifts and increases ASP utility by 60.02% compared to the SOTA method, i.e., Deep Reinforcement Learning (DRL)-based contract theory. The code and data are publicly available at https://github.com/Zijun0819/DROContract-Theory

Integrating low-light image enhancement techniques, in which diffusion-based AI-generated content (AIGC) models are promising, is necessary to enhance nighttime teleoperation. Remarkably, the AIGC model is computation-intensive, thus necessitating the allocation of AIGC tasks to edge servers with ample computational resources. Given the distinct cost of the AIGC model trained with varying-sized datasets and AIGC tasks possessing disparate demand, it is imperative to formulate a differential pricing strategy to optimize the utility of teleoperators and edge servers concurrently. Nonetheless, the pricing strategy formulation is under information asymmetry, i.e., the demand (e.g., the difficulty level of AIGC tasks and their distribution) of AIGC tasks is hidden information to edge servers. Additionally, manually assessing the difficulty level of AIGC tasks is tedious and unnecessary for teleoperators. To this end, we devise a framework of AIGC task allocation assisted by the Vision Language Model (VLM)-empowered contract theory, which includes two components: VLM-empowered difficulty assessment and contract theory-assisted AIGC task allocation. The first component enables automatic and accurate AIGC task difficulty assessment. The second component is capable of formulating the pricing strategy for edge servers under information asymmetry, thereby optimizing the utility of both edge servers and teleoperators. The simulation results demonstrated that our proposed framework can improve the average utility of teleoperators and edge servers by$10.88 \sim 12.43\%$and$1.4\! \sim \!2.17\%$, respectively.

The rapid integration of augmented reality (AR) and virtual reality (VR) technologies into contemporary automotive development has led to unprecedented opportunities and challenges. This work addresses the integration of edge computing and AR/VR applications within connected autonomous vehicles, focusing on the pivotal role of object detection. The edge-assisted object detection problem is formulated as a constrained optimization problem, aiming to minimize the adverse effects on the object detection process. To solve the problem, we introduce an innovative edge-assisted algorithm, transmitting live camera frames to an edge server for detailed processing. Only essential detection data is then relayed to AR/VR devices, marking a significant advancement over existing strategies. Notable outcomes include a reduction in latency (averaging between 37.06% and 44.76%), enhanced data throughput (ranging from 27.66% to 41.18%), improved freshness loss (between 36.36% and 69.57%), and a frame loss reduction to 7.5%, surpassing baseline methods by 6.5% to 36%. These findings underscore the potential of this methodology for optimizing AR/VR applications in vehicular environments.

High-definition (HD) map is an essential building block in the autonomous driving era, which enables fine-grained environmental awareness, exact localization, and route planning. However, because HD maps include rich, multidimensional information, the volume of HD map data is enormous, making it expensive and time-consuming to transmit on vehicular networks. Therefore, in this paper, we propose a data optimization scheme for effective HD map updates in vehicular named data networking (NDN) scenarios. We formulate the HD map data optimization problem as a convex optimization problem and solve it with modified convolutional neural networks (CNNs) from YOLOX's real-time object detection system. Specifically, we modify the YOLOX object detection algorithm to detect and compress redundant pixels in local map data before transmission to the MEC server. To deploy our proposed scheme, we construct a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Extensive simulations show that our proposed scheme can significantly reduce the transmission data size and time by 48.25% - 65.78% and 46.85% - 78.84% compared with state-of-the-art HD map update techniques like RLSS, Pro-RTT, and Loss-based systems.

Highly-dynamic (HD) map is an indispensable building block in the future of autonomous driving, allowing for fine-grained environmental awareness, precise localization, and route planning. However, since HD maps include rich, multidimensional information, the volume of HD map data is substantial and cannot be transmitted frequently by several vehicles over vehicular networks in real-time. Therefore, in this paper, we propose a data source selection scheme for effective HD map transmissions in vehicular named data networking (NDN) scenarios. To achieve our goal, we created a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Next, due to our vehicular NDN’s dynamic and complex nature, we formulate the data source selection problem as a Markov decision process (MDP) and solve it using a reinforcement learning approach. For simplicity, we termed our proposed scheme data source optimization with reinforcement learning (DSORL), which selects suitable vehicles for HD map data transmission to MEC servers. The experiment results indicate that our suggested method outperformed existing baseline schemes, such as RLSS, Pro-RTT, and HDM-RTT, across all performance criteria in the evaluation. For instance, the system throughput increases by$65\%-72.68\%$compared to other baseline systems. Similarly, the proposed approach can minimize packet loss rate, data size, and transmission time by up to 60.6%, 77.5%, and 54.1%, respectively.

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.