Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks.

More and more individuals and enterprises outsource data and applications to cloud servers in recent years. Since the public cloud servers are not completely trusted, users usually encrypt important data before sending it to cloud servers. As a result, ciphertext retrieval technology has gradually become a research hotspot. In the existing related schemes, there are some defects such as not supporting “multiple owners” mode and multi-keyword retrieval, having low retrieval efficiency, accuracy and security, and difficult data updating. Hence, we propose an efficient Ciphertext Retrieval scheme based on Homomorphic encryption for Multiple data owners in hybrid cloud (CRHM), in which the public cloud server and the private cloud server cooperate to perform the ciphertext retrieval. In CRHM, an encrypted balanced binary index tree structure and a homomorphic encryption method based on large integer operations are designed to support “multiple owners” mode and multi-keyword ranked retrieval. The security analysis shows that CRHM can effectively guarantee the privacy and security of user file and retrieval, and the performance evaluation demonstrates that compared with the existing related schemes, CRHM has high efficiency in the index generation and retrieval processes, while keeps relatively high retrieval accuracy.

Aiming to address the shortage of experimental resources, the high cost of large-scale deployment of hardware experimental environment and the difficulty for students to get started in the software-defined network (SDN) course, this article proposes an SDN experimental teaching scheme based on the virtualised environment, and gives a specific experimental scheme design. The scheme utilises virtualisation technology to build a SDN experimental environment quickly, uses a lightweight network simulation platform – that goes by the name of Mininet – to build the SDN network and uses open-source controller Floodlight for centralised control of the SDN network. The scheme is mainly divided into three phases: basic, improvement and synthesis. In the basic phase, experimental projects mainly include the study of SDN basic concepts and the use of relevant tools; in the improvement phase, experimental projects mainly include the use of SDN flow table, group table, etc; in the synthetic phase, we design two innovative experimental projects that use computational intelligence technology to achieve efficient load balancing and accurate malicious attack detection. The difficulty of each phase is increasing. The constantly evolving levels of difficulty allow the individual needs of students with different levels to be met, thereby improving the effect of SDN experimental teaching and cultivating innovative SDN talents.