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.

The surge in Internet video traffic driven by 5G advancement strains network infrastructure. Edge computing emerges as a solution for video distribution, yet faces challenges from limited cache capacity and dynamic user requests. To address these challenges, we propose IceCache - a recommendationdriven edge Caching architecture for the life cycle of video streaming. IceCache enhances Quality of Experience (QoE) while reducing backhaul traffic through two-stage caching: cache placement before playback, dynamic prefetching and cache admission during playback. A user behavior simulation integrating recommender systems was developed to evaluate the proposed caching strategy. Experiments on real-world MovieLens and synthetic datasets validated the strategy's performance.

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.

Since fog nodes are resource-constrained and imperfectly trusted heterogeneous devices, guaranteeing a real-time response to Internet of Things (IoT) tasks while optimizing system energy consumption remains a significant challenge. To overcome this, we first propose a acrlong DBC-enabled cloud–fog collaborative task scheduling architecture. Second, a task scheduling model is constructed to optimize system energy consumption and task deadline violation time while adhering to the IoT task response time restriction. Finally, two blockchain-enabled task scheduling algorithms are developed: 1) the reputation-based priority-aware algorithm (DB_RP) and 2) the accelerated ant colony system algorithm (DB_AACS). Extensive experiments are conducted to assess the proposed algorithm in four dimensions: 1) task completion rate; 2) system makespan; 3) system energy consumption; and 4) task deadline violation time. The experimental results demonstrate that the proposed algorithm is superior to the existing literature, and the acceleration strategy in DB_AACS is effective.

Due to an increasing amount of users, service providers are paying more attentions on how to properly tackle the User Allocation (UA) problem. In the literature, few solutions to the UA problem took into account the heterogeneity of devices and the complexity of the network. Therefore, the paper takes these two factors into account and studies the UA problem in Non-Orthogonal Multiple Access (NOMA)-Based heterogeneous edge computing scenarios. To solve the above problem, we propose the Heuristic Ant colony Systems (HAS) algorithm, which aims to enhance the number of allocated users while reducing the overall deadline violation time. Extensive evaluations that employ a public dataset demonstrate the benefits of our proposed approach.