In modern urban areas, inefficiency traffic management is one of the main causes of road congestion, leading to reduced fuel efficiency and increased traffic safety hazards. Traditional researches typically focus only on enhancing the throughput of intersections by optimizing traffic signals or individual vehicle trajectories. However, these methods often overlook the dynamic nature of the traffic system and the potential benefits of vehicle platooning, limiting their effectiveness in complex traffic environments. Addressing this challenge, this article presents MARP, a Cooperative Multiagent deep reinforcement learning (DRL) System for connected autonomous vehicle (CAV) Platooning. Utilizing vehicle to vehicle (V2I) and vehicles to infrastructure (V2V) technologies, MARP integrates sensing, computing, and communication to collect and process real-time data on traffic conditions, thereby achieving dynamic synchronization between traffic signal controllers and CAV platoons. By constructing platoons that collaborates with the infrastructure through a multiagent DRL collaboration model, MARP adapts to real-time traffic flow changes, significantly optimizing the fluidity and efficiency of the entire traffic network. Detailed experiments show that MARP effectively reduces traffic congestion, shortens intersection travel times, and cuts fuel consumption and emissions, surpassing the state-of-the-art approach.

In smart distribution networks (SDNs), false data injection attacks (FDIAs) on the state of charge (SoC) estimation of battery energy storage systems (BESSs) can successfully escape bad data detection, making SDNs suffer serious security risks. Some valuable work has been done on FDIAs and the detection of FDIAs, but how to defend FDIAs is still tough. Considering the spatio-temporal characteristics of the measurement data in SDNs containing BESSs, a TSCW-GAN defense method is proposed in this article. The method consists of the following two parts: 1) a generator and 2) a discriminator. The generator combines the transformer with the extraction of valid path features to form path features, which are then used to represent the regularity of the data in terms of the expected values. In addition, the discriminator uses conditional signature Wasserstein-1 metrics, thus capturing the conditional joint law of the measurement data. In IEEE13 and IEEE33 bus systems, the experimental results show that the proposed method achieves good performance with 97.5% accuracy.

Quantum secret sharing (QSS) represents the fusion of quantum mechanics principles with secret information sharing, allowing a sender to distribute a secret among receivers for collective recovery. This paper introduces the concept of quantum anonymous secret sharing (QASS) to enhance the practicality of such protocols. We propose a QASS protocol leveraging W states, ensuring both recover-security and anonymity of shared secrets. Our protocol undergoes rigorous evaluation verifying their accuracy and fortifying their security against scenarios involving the active adversary. Additionally, acknowledging the imperfections inherent in real-world communication channels, we conduct a comprehensive analysis of protocol security and efficacy in noisy quantum networks. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications.