High-speed rail (HSR) station areas are evolving into integrated energy systems with significant energy demands and diverse resources, necessitating coordinated optimization to achieve sustainable development. However, the integration of electric vehicles (EVs) and carbon trading mechanisms in these areas introduces complexities in balancing multiple stakeholders’ interests. This study proposes an aggregated optimization scheduling strategy for electric vehicles in high-speed railway station areas, taking into account carbon trading. A dual-layer Stackelberg game model is developed to ensure mutual benefits for both HSR station areas and EVs. In the lower layer, the EV model predicts peak passenger flow based on daily averages and characterizes travel behavior to minimize user charging/discharging costs, feeding these behaviors to the upper layer. The upper-layer station model minimizes operational costs by leveraging EV scheduling data and achieves optimal pricing decisions within a carbon trading framework through iterative game-theoretic strategies. A case study at Neijiang North Station demonstrates that the proposed model reduces station operating costs by 4.6%, decreases EV user scheduling costs by 3.5%, and increases carbon reduction by 5.6% compared to conventional approaches, effectively promoting economic and low-carbon operations.

This paper presents a novel event-triggered approach for regulating energy prices of heterogeneous agent-based demand response including prosumer groups and energy companies. First, a price-based demand response management (PDRM) model is developed using networked evolutionary game theory. Then, a necessary and sufficient criterion is proposed to verify the stabilizability of the controlled PDRM system, significantly reducing iterative complexity compared to existing methods. By utilizing truth matrices, an algorithm is proposed to calculate the event-triggered feedback gain matrices and determine the triggering times. The proposed matrix-based event-triggered control guarantees that the strategies of all participants converge to the target game equilibrium set while reducing the update frequencies of the controller. Finally, the simulation results demonstrate the effectiveness of the proposed method.

This article investigates energy trading management involving users, suppliers, and the utility company, focusing on a periodic energy trading mechanism that incorporates time-varying delays in the information transmission process. Compared with previous studies, the time-varying delays considered in this paper are different among participants. The time-varying delays for each participant are distributed according to an independent probability. A novel model for energy trading with time-varying delays is proposed using networked evolutionary game theory. Based on the algebraic state space representation, a criterion is provided for determining the convergence of the networked evolutionary game-based energy trading model. In order to converge all strategies of the networked evolutionary game-based energy trading model to the target game equilibrium set, a networked evolutionary game-based energy trading model with strategy feedback control is proposed. Then, an algorithm is presented for designing the strategy feedback control gain, which enables the strategies of all users and suppliers to converge to the target game equilibrium set, thereby regulating energy trading prices to the desired level. Finally, the effectiveness of the proposed approach is verified through an illustrative example.

The rapid development of fog-assisted vehicular crowdsensing systems (FVCSs) enables real-time vehicular data sharing, but redundant and similar data in report results in unnecessary costs. However, previous studies only focus on duplicate reports and neglect deduplication of similar data. Besides, transmitting crowdsensing data in Internet of Vehicles (IoV) exposes vulnerabilities to offline brute-force and fake report attacks. In this article, we present P2SimiDedup, a scheme for secure deduplication of similar crowdsensing reports. Specifically, we develop cryptographic primitives and introduce an improved generalized deduplication technique (GreedyGD) to achieve secure deduplication over similar crowdsensing data. Then, we construct a two-level deduplication framework that can perform secure and efficient similar-based deduplication at fog nodes and cloud server. Besides, P2SimiDedup can ensure that only data requesters can decrypt and recover crowdsensing data. The security analysis and evaluation results demonstrate that P2SimiDedup can achieve privacy-preserving deduplication for similar crowdsensing reports with moderate computational, communication, and storage costs.

Many parallel programs are intended to yield deterministic results, but unpredictable thread or process interleavings can lead to subtle bugs and nondeterminism. We proposed a producer-consumer virtual memory-SPMC-for efficient system-enforced deterministic parallelism, and prototyped the SPMC model and its software stack entirely in Linux user space, called DLinux. This paper summarizes the implementation policies and limitations in our previous DLinux. To reduce SPMC page fault overhead and suspend/resume overhead which severely degrade the performance of DLinux, we enhance the SPMC model with nonblocking test and direct read and write primitives. Based on the extended SPMC model, we improve the implementation of upper programming abstractions. Experimental results show that relative to the previous version, the new DLinux can improve the performance of NPB workloads up to 2.33X and 1.76X on 8 and 16 processes, respectively. For CG on 8 processes, its runtime relative to MPICH2 decreases from 4.12X to 1.77X.