In Internet of Things(IoT) systems, ensuring the secure exchange of information between devices from different domains is crucial. Current cross-domain authentication schemes based on a single blockchain struggle to meet the confidentiality requirements for data and information exchange in large-scale IoT systems. This paper proposes a blockchain-based identity authentication scheme (BCPPAS) for IoT, featuring dual-chain collaboration, and designs a novel certificateless aggregate signature algorithm to address complex certificate management and key escrow issues. The edge server is capable of aggregating different signatures to achieve batch authentication, markedly improving authentication efficiency and reducing computational and storage overhead. Also, BCPPAS is designed to avoid costly bilinear pairing operations, providing less computational overhead for IoT devices with limited resources. To protect the privacy of IoT devices, BCPPAS uses the pseudonym instead of the real identity. Finally, efficiency of BCPPAS are demonstrated through theoretical analysis and experiments.

Contrastive learning has shown impressive progress for the self-supervision based 3D point clouds feature learning. Based on the distance control in the feature space of positive and negative samples, it can obtain effective point cloud feature representations in a self-supervised manner. However, most existing contrast based point cloud learning methods only consider the feature similarity relationship between samples, (e.g., point cloud, voxel or image), which lack of explicit exploration on point cloud structure. Considering that structure is an important property of point clouds, for better feature learning, we propose a effective cross-modal contrast based method with image jigsaw (CrossCon-Jig) to better learn point cloud representations with both semantic and structural information. Specifically, our method includes intra-modal contrast of point cloud, cross-modal contrast between point cloud and rendered image, and point cloud guided image jigsaw. The intra-modal contrast and the contrast of cross-modal focus on the exploring of invariant and consistent feature representations, and image jigsaw guides the model to explore spatial structure information of point clouds. Extensive experimental tests on 3D object classification and 3D object part segmentation tasks have achieved excellent performance, demonstrating the effectiveness of the proposed method.

The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with \(\alpha=0.05\) , representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications.

This study presents SwinKAN, a multivariate prediction model based on the Swin Transformer and the Convolutional Kolmogorov–Arnold Networks. The model aims to enhance the prediction of severe convective weather using dual-polarization radar data. SwinKAN employs a multi-input, multi-output architecture. It simultaneously predicts multiple variables, including radar reflectivity Zh, differential reflectivity Zdr, and specific differential phase Kdp, fully leveraging the advantages of dual-polarization radar. To enhance interaction between radar variables, SwinKAN integrates them within the channel dimension. This integration improves the model’s ability to capture complex spatiotemporal features. This study also introduces KAN to the extrapolation of dual-polarization radar data, a novel contribution. Additionally, the Swin-Kolmogorov Feature Extraction Module is designed to efficiently extract and integrate both local and global features. This significantly improves the model’s ability to predict precipitation intensity and evolution. To further optimize performance, the Cross-Feature Fusion module is designed. This module enhances prediction accuracy, especially in regions with heavy rainfall. Experimental results demonstrate that SwinKAN outperforms existing methods across multiple evaluation metrics. These results validate the model’s superiority and effectiveness in severe convective prediction.

Summary As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

Fake news is a prevalent issue in modern society, leading to misinformation, and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awareness and understanding of news, while also effectively mitigating the dissemination of fake news. However, news credibility assessment meets challenges when processing large-scale and constantly growing data, due to insufficient and unreliable labels and standards, and diversity and semantic ambiguity of news contents. Recently, machine learning models have been well developed to address these issues, but suffer from limited effectiveness. A unified framework is also required for them to represent various entities and relationships involved in news stories. This article proposes an entity ontology-based knowledge graph network (EKNet) to leverage knowledge graphs and entity frameworks for news credibility assessment. The model utilizes the information from knowledge graphs by combining entities and relationships from news and knowledge graphs. Experimental results show that the EKNet has advantages in evaluating news credibility over existing methods. Specifically, compared to several strong baselines, the model demonstrates a significant performance improvement in scores across various tasks. Which indicates that using the EKNet to address the challenges in news credibility assessment is highly effective and can conduct better performance for the problem of fake news in the social media environment.

A near-real-time precipitation estimation product derived from geosynchronous Earth-orbiting (GEO) satellite data is highly desirable due to its ability to provide extensive coverage with high spatial and temporal resolution. This research presents a novel Deep-Learning-based Precipitation Estimation algorithm using a Multi-SpatioTemporal network (DLPE-MST), to investigate the potential of Geostationary Operational Environmental Satellite-16 (GOES-16) multitemporal images in precipitation estimation. First, a series of Advanced Baseline Imager (ABI) bispectral satellite images (6.19 and$10.35~\mu \text {m}$) from GOES-16 are used as inputs. Second, a module based on 3-D convolutional neural networks (3-D CNNs) is proposed to be embedded into the DLPE-MST for extracting motion features within rainfall areas. Third, a novel loss function, separated domain error (SDE), is proposed for DLPE-MST to mitigate the issue of underestimation arising from imbalanced precipitation datasets. Finally, to assess the feasibility of the DLPE-MST, GOES-16 satellite images covering the eastern Continental United States (CONUS) of America during the summer of 2020–2021 are utilized to generate raster maps depicting hourly rainfall rates at a resolution of$ 0.04^{\circ } \times 0.04^{\circ }$. The experimental results indicate that our algorithm outperforms others in terms of probability of detection (POD) and correlation coefficient (CC), achieving scores of 91.79% and 0.58, respectively. The statistical analysis of multiple rainfall events also demonstrates that the DLPE-MST outputs are closer to the ground truth compared to other products. Furthermore, the SDE shows significant potential in alleviating the underestimation of heavy rain events. After testing, this algorithm takes only 0.09 s to generate one raster map of the rainfall rate.

Severe convective weather, a meteorological phenomenon, is distinguished by its abrupt initiation, swift propagation, extreme atmospheric conditions, and formidable capacity for destruction, all of which have a significant impact on human productivity and livelihoods. In this study, we introduce an improved multitask transformer-based algorithm, MT-Transformer, for predicting parameters related to dual-polarization radar. These parameters encompass radar reflectivity Zh, differential reflectivity Zdr, and specific differential phase Kdp so that more information about the dynamic structure of convective storms can be obtained. MT-Transformer has the following main improvements. First, to overcome the insensitivity of the transformer to high-frequency information, before the data are fed into the transformer, 2-D convolution operation is used for the downscaling and image feature extraction. Second, for the input side of the Transformer decoder, we develop the future feature extraction (FFE) module for multiscale feature prediction, which is a structure for capturing the global multiscale contextual information of multivariate. Third, the fully connected structure in the feedforward network is replaced by a 3-D convolution layer, which can effectively extract the spatiotemporal information of precipitation. The quantitative results demonstrate a reduction in the root-mean-square error (RMSE) values for Zh, Zdr, and Kdp by 14.48%, 3.47%, and 6.91%, respectively, in comparison to those of the second-best MIM model for a 1-h forecast duration.

Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance.

Abstract Energy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect, it also helps to conserve energy for future use. Over the years, several methods for energy forecasting have been proposed, all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment. This research, however, proposes the uses of Deep Neural Network (DNN) for energy forecasting on mobile devices at the edge of the network. This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery. Nevertheless, the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them. Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source (D-RES) network. Moreover, a novel grid control algorithm that uses the forecasting model to administer a well-coordinated and effective control for renewable energy sources (RESs) in the electrical network is designed. The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network. The model was trained using a dataset from a solar power generation company in Belgium (elis) and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations. The performance of each architecture was evaluated using the mean square error (MSE) and the r-square.

Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three-hidden-layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k-nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large-scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

With the fast growing demands for the big data, we need to manage and store the big data in the cloud. Since the cloud is not fully trusted and it can be accessed by any users, the data in the cloud may face threats. In this paper, we propose a secure authentication protocol for cloud big data with a hierarchical attribute authorization structure. Our proposed protocol resorts to the tree-based signature to significantly improve the security of attribute authorization. To satisfy the big data requirements, we extend the proposed authentication protocol to support multiple levels in the hierarchical attribute authorization structure. Security analysis shows that our protocol can resist the forgery attack and replay attack. In addition, our protocol can preserve the entities privacy. Comparing with the previous studies, we can show that our protocol has lower computational and communication overhead.

At present, the study of upper‐limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper‐limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three‐channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine‐ (FSM‐) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

With the advancement of national policies and the rise of Internet of things (IoT) technology, smart meters, smart home appliances, and other energy monitoring systems continue to appear, but due to the fixed application scenarios, it is difficult to apply to different equipment monitoring. At the same time, the limited computing resources of sensing devices make it difficult to guarantee the security in the transmission process. In order to help users better understand the energy consumption of different devices in different scenarios, we designed a nonintrusive load management based on distributed edge and secure key agreement, which uses narrowband Internet of things (NB‐IoT) for transmission and uses edge devices to forward node data to provide real‐time power monitoring for users. At the same time, we measured the changes of server power under different behaviors to prepare for further analysis of the relationship between server operating state and energy consumption.

Nowadays, Edge computing is being introduced as a powerful paradigm to collaborate with the cloud to provide sufficient computing resources for IoT to implement intelligent analysis and data mining. Generally, due to the edge nodes are closer to mobile users, the access latency and the cost of using cloud services are effectively reduced. However, the implementation of cross-layer resource scheduling between edge nodes and servers deployed in the cloud to meet service requirements (i.e., shortest completion time, maximum resource utilization, lower energy consumption, etc.) still faces great challenges. To address this challenge, a cross-layer resource scheduling method, named CRSM, for the IoT applications is proposed in this paper. Technically, the Pareto archived evolution strategy (PAES) is employed to optimize the time cost of IoT applications, resource utilization and energy consumption of edge node. Then, the technique for order preference by similarity to ideal solution (TOPSIS) and the multiple criteria decision making (MCDM) are leveraged to acquired the optimal cross-layer resource scheduling strategy. Finally, the comprehensive analysis of CRSM is introduced in detail.

Blurred vision images caused by rainy weather can negatively influence the performance of outdoor vision systems. Therefore, it is necessary to remove rain streaks from single image. In this work, a multiscale generative adversarial network- (GAN-) based model is presented, called DR-Net, for single image deraining. The proposed architecture includes two subnetworks, i.e., generator subnetwork and discriminator subnetwork. We introduce a multiscale generator subnetwork which contains two convolution branches with different kernel sizes, where the smaller one captures the local rain drops information, and the larger one pays close attention to the spatial information. The discriminator subnetwork acts as a supervision signal to promote the generator subnetwork to generate more quality derained image. It is demonstrated that the proposed method yields in relatively higher performance in comparison to other state-of-the-art deraining models in terms of derained image quality and computing efficiency.

Nowadays, Software Defined Network (SDN) develops rapidly for its novel structure which separates the control plane and the data plane of network devices. Many researchers devoted themselves to the study of such a special network. However, some limitations restrict the development of SDN. On the one hand, the single controller in the conventional model bears all threats, and the corruption of it will result in network paralysis. On the other hand, the data will be increasing more in SDN switches in the data plane, while the storage space of these switches is limited. In order to solve the mentioned issues, we propose two corresponding protocols in this paper. Specifically, one is an anonymous protocol in the control plane, and the other is a verifiable outsourcing protocol in the data plane. The evaluation indicates that our protocol is correct, secure, and efficient.

Internet of Things (IoT) is a research field that has been continuously developed and innovated in recent years and is also an important driving force for the improvement of people’s life in the future. There are lots of scenarios in IoT where we need to collaborate through devices to complete tasks; that is, a device sends data to other devices, and other devices operate on the aid of the data. These transmitted data are often users’ privacy data, such as medical data and grid data. We propose an instant encrypted transmission based security scheme for such scenarios in IoT. The analysis in this paper indicates that our scheme can guarantee the security of users’ data while ensuring rapid transmission and acquisition of instant IoT data.

Smart mobile devices are one of the core components of the wireless body area networks (WBANs). These devices shoulder the important task of collecting, integrating, and transmitting medical data. When a personal computer collects information from these devices, it needs to authenticate the identity of them. Some effective schemes have been put forward to the device authentication in WBANs. However, few researchers have studied the WBANs device authentication in emergency situations. In this paper, we present a novel system named emergency medical system without the assistance of doctors. Based on the system, we propose an identity‐based fast authentication scheme for smart mobile devices in WBANs. The scheme can shorten the time of device authentication in an emergency to achieve fast authentication. The analysis of this paper proves the security and efficiency of the proposed scheme.

Abstract A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large‐scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial–temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel‐based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi‐objective algorithm called time and space optimized NSGA‐II (TS‐NSGA‐II). Experiment results have shown that compared with the original load‐balancing scheme, our approach can save approximate 47–55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6%improvement over the original scheme. Copyright © 2016 John Wiley & Sons, Ltd.