The edge of the Internet of Things (IoT), which consists of unmanned aerial vehicles (UAVs), is vulnerable to network intrusion because software and wireless connections are used extensively in the IoT. Designing an efficient intrusion detection system (IDS) model is imperative. However, when creating IDS models with distributed data collected by UAVs, it is necessary to take precautions to protect the data’s security and privacy. Furthermore, most of the IDS models are focused on one-time learning but not on continuous learning. To this end, we propose a federated continuous learning framework with a stacked broad learning system (FCL-SBLS) based on the digital twin network (DTN), which can learn and train the IDS model on new data quickly and continuously. In order to improve the efficiency and quality of the IDS model when training and aggregation, we employ an asynchronous federated learning (FL) architecture, and a deep deterministic policy gradient (DDPG)-based UAV selection scheme assisted by DTN is proposed to help the global IDS model aggregation. The presented algorithm is validated using the CIC-IDS2017 data set, and the simulation results reveal that our algorithm achieves higher efficiency and accuracy than the existing FL scheme.

Network availability and service continuity are major concerns for network operators to provide reliable communication services for Internet of Things (IoT), which are particularly challenging to achieve in virtualized network slicing environment where network services are exposed to the failure risks of both software (virtual network function (VNF) instances) and hardware (physical nodes). In general, the redundancy-based VNF backup solutions are used to improve the reliability of virtualized network slices. However, backup VNFs require the same amount of resources as the primary VNFs, which will result in high-resource cost. In this article, we propose a joint VNF partition and hybrid backup scheme for VNF orchestration, backup and mapping, whose aim is to construct the reliability-enhanced and delay-guaranteed network slices at minimum cost. Specifically, the VNF partition method divides a single VNF into multiple thinner VNFs with lower processing capacity and is expected to enhance the reliability of network slices with less additional resources. The hybrid backup scheme includes both onsite and offsite backup forms. Then, considering the time-varying network environment and IoT service requirements, we formulate the VNF orchestration, backup and mapping as a dynamic mixed integer linear programming (DMILP) problem, and model the dynamic problem as a Markov decision process (MDP). In view of the large action space of the formulated MDP, we propose a multiagent deep reinforcement learning (DRL) approach with an action space reduction strategy to achieve the dynamic VNF orchestration, backup and mapping solution. Simulation results demonstrate that the proposed joint VNF partition and hybrid backup scheme can obtain superior delay and reliability performance with low-network cost.

The rapid development of artificial intelligence has significantly advanced the field of computer vision, particularly in image analysis and understanding. This paper provides a comprehensive review of the current state of the field, key technologies, and their applications across various real-world scenarios. It delves into the value of image analysis in critical areas such as personalized art, healthcare and medical image analysis, security monitoring and recognition technology, autonomous driving and traffic management, as well as industrial automation and quality control. This paper not only highlights the challenges and limitations, including dataset constraints, algorithm generalization, real-time computational costs, and privacy and ethical concerns, but also offers a forward-looking analysis of development trends such as interdisciplinary integration, weakly supervised and unsupervised learning, algorithm optimization and hardware advancements, and the protection of personal privacy and information. These insights provide a profound perspective on the future trajectory of computer vision and AI-driven image analysis.

As a three-layer association of Internet of things equipment (IoTE) -network slicing (NS) -base station (BS) in radio access network (RAN) slicing, handoff control and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing are investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then an improved algorithm based on actor-critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for Handoff Control and an optimizer for Resource Allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.

In recent years, global attention to carbon emissions has increased, becoming one of the main drivers of global climate change. Accurate prediction of carbon emission trends in small and medium-sized countries and scientific regulation of carbon emissions can provide theoretical support and policy references for the effective and rational use of energy and the promotion of the coordinated development of energy, environment, and economy. This paper establishes a grey prediction model using the classical Logistic mathematical model in a determined environment to investigate the carbon emission system. At the same time, we use the basic principle of fractional-order accumulation to establish a grey prediction model with fractional-order Logistic and obtain the parameter estimation and time-response equation of the new model by solving the model through the theory related to fractional-order operators. The particle swarm optimization algorithm is used to complete the optimization process of the order of the fractional order grey prediction model and obtain the optimal model order. Then, the new model is applied to predict carbon emissions in five medium-emission countries: Ethiopia, Djibouti, Ghana, Belgium, and Austria. The new model shows better advantages in the validity analysis process, and the simulation results indicate that the new model proposed in this paper has stronger stability and better simulation and prediction accuracy than other comparative models, proving the model’s validity. Finally, the model is used to forecast the carbon emissions of these five countries for the five years of 2021–2025, and the results are analyzed, and relevant policy recommendations are made.