With the development of Sixth Generation (6G) Internet of Things (IoT) technology, ensuring data reliability and security in networks has become a critical issue. To address the identification of abnormal behaviors in network traffic, this study proposes an anomaly traffic detection algorithm combining wavelet analysis and machine learning. By utilizing wavelet analysis, this paper ex-tracts time-frequency features from Fifth Generation (5G) core network traffic data, which effectively capture abrupt changes and periodic fluctuations in the data. Combining deep learning models, particularly dynamic convolution and attention mechanisms, this method adaptively optimizes the feature extraction process, enhancing the model’s sensitivity and accuracy in detecting key traffic features. Experimental results demonstrate that the proposed algorithm outperforms traditional methods in multiple standard datasets, with superior performance in accuracy, precision, recall, and other evaluation metrics.

As distributed edge intelligence (DEI) advances within the Internet of Electric Vehicles (IoEV), the deployment of mobile charging stations (MCSs) offers a solution to the uneven distribution of fixed charging stations (FCSs), enhancing energy access in remote areas. However, MCS faces the problem of passive scheduling, limiting effective resource utilization and prolonging charging waiting time. This article proposes a hybrid charging model algorithm (HCMA) to address the above challenge, particularly in regions with limited available FCS. We first formulate a multiobjective optimization problem to optimize electric vehicle (EV) charging modes, volumes, and MCS scheduling arrangements. Then, we decompose the original problem into two subproblems. By determining EV charging locations and EV charging mode, the two subproblems are solved, respectively. Finally, simulations based on real-world data demonstrate that HCMA performs better compared to several representative methods, including random working, ARMM, and RBA, in terms of average charging waiting time, extra traveling distance, average unit price of energy, and number of MCS schedules.

With the explosive growth in Industrial Internet of Things (IIoT) devices, the volume of multimedia data in the field of flexible manufacturing has also increased significantly in recent years, especially the vast amount of unlabelled image data. Image annotation provides machines with a more natural way to interact with users, enhancing the level of intelligence in IIoT flexible manufacturing. This article proposes a multifeature fusion multikernel learning image annotation method to tackle imbalanced label distribution, image weak labeling, and varying representational abilities of features. Initially, oversampling techniques with synthetic minority class samples address the influence of minority classes, while a label enhancer extends label vectors to overcome the influence of weak labeling. Subsequently, the integration of traditional visual features with deep features based on multikernel learning is investigated to enhance feature representation capability. This approach combines complementary information from multiple features, establishing intrinsic connections between images and annotated keywords. Experimental evaluations are conducted on three benchmark datasets, comparing our method with several classical methods. Evaluation results demonstrate that our proposed method captures semantic information more accurately and comprehensively. By effectively accomplishing automatic image annotation, our method can enhance human-machine-interaction to improve the level of intelligence in IIoT flexible manufacturing.

With the development of artificial intelligence technology, UAVs have the ability to perceive the environment. UAV combined with target detection technology for road environment perception has received extensive attention. However, the complexity and variability of real-world road environments pose challenges for target detection. To address these challenges, we propose a uav small target detection algorithm AS-YOLOV5 with adaptive feature fusion and improved attention mechanism. In the feature extraction phase, AS-YOLOV5 employs soft pooling to bolster the feature extraction network, mitigating the loss of critical edge information of small targets inherent in standard down-sampling techniques. Our feature fusion method incorporates learnable parameters, effectively rebalancing feature layers to ensure small target information remains significant during the fusion process and is not obscured by large target features. The subspace attention module is optimized to enhance the representation of small target features while suppressing background interference. To ensure the detection branch captures essential shallow information about small objects, we introduce an additional feature extraction layer for feature fusion. Simulation results show that the proposed method outperforms the existing algorithms. AS-YOLOV5 achieved an mAP(mean Average Precision) of 56.36% on the BDD100K dataset and an impressive 93.33% on the KITTI dataset with an IOU (Intersection over Union) threshold of 0.5.

Scanned halftone images are degraded for the presence of screen patterns. It's a challenge to automatically detect the halftone images and remove the noises on the fly. This paper proposes a novel adaptive real-time descreening method based on Support Vector Machine (SVM) and modified Smoothing over Univalue Segment Assimilating Nucleus (SUSAN) filter for imaging devices, including scanners and multifunction printers. The proposed algorithm contains two major steps: image classification and adaptive descreening. The image classification uses SVM methods to accurately classify the scanned images into three categories: continuous tone, amplitude modulation (AM) halftone or frequency modulation (FM) halftone. The halftone images are needed to descreen. The proposed descreening method is based on modified SUSAN filter. It considers screen cell size to choose the optimal filter parameters which can preserve more high frequency image detail. The experiment results show that the algorithm is effective and fully automatism, and maintains higher image quality.

In live peer-to-peer (P2P) streaming, each peer (child) has a number of supplying parents whose packets have to be scheduled and delivered in time for continuous playback at the child. It is challenging to develop a scheduling algorithm that achieves low delay given heterogeneous bandwidth, propagation delays and available content in all the parents. This paper proposes a novel, simple and effective scheduling scheme called pattern-push. As compared to the traditional mesh-pull, pattern-push does not require continuous buffermap advertisements from the parents, and operates on the packet level instead of the larger segment level. In pattern-push, each parent pushes its packets according to a pattern as indicated by a starting packet ID and a cycle bitmap. Pattern-push requires only minimal feedback from the child, as the pattern only needs to be changed when the child detects a marked change in network conditions or its parents. Simulation results show that pattern-push achieves a significantly lower delay and overhead as compared with both traditional and recent scheduling algorithms proposed in the literature.