In distributed learning, the End-Edge-Cloud architecture is gaining widespread adoption. However, the cross-layer data interaction in EEC systems significantly increases the risk of privacy breaches. To address this challenge, this paper proposes a novel dynamic privacy-protection framework named MetaDP-HE. The framework is designed to enhance privacy protection while maintaining model performance. It integrates meta-model guided differential privacy (MetaDP) with CKKS homomorphic encryption that supports floating-point operations. A dynamic coordination mechanism is introduced to optimize the parameter configurations between MetaDP and HE. Specifically, clients use the meta-model to predict privacy budgets based on data sensitivity and adjust the noise in differential privacy accordingly. Edge servers then dynamically adjust the encryption parameters of homomorphic encryption based on the noise level, achieving adaptive regulation of encryption strength. The combination of layered encryption and dynamic parameter optimization enables the system to ensure privacy protection and efficient operations when handling data at different levels. Experimental results show that MetaDP-HE outperforms traditional single-privacy methods in both privacy protection and model performance, validating its effectiveness and applicability in practical scenarios.

Metacomputing optimizes distributed computing resources to enhance federated learning systems by enabling efficient resource allocation, improved scheduling, and greater scalability, thereby addressing challenges in large-scale and dynamic environments. This paper proposes an innovative framework integrating Directed Acyclic Graph (DAG) technology with federated learning within a metacomputing environment. The key contributions include a three-layer decentralized federated learning model integrating DAG and metacomputing to enhance resilience and scalability, two advanced tip selection models LazyEval Tip Selector and Precision Tip Selector to optimize node selection and improve data flow, and a Benchmark Improvement Protocol (BIP) for efficient node publishing and role adaptation.The BIP ensures that only high-performing models are published by comparing new models against established benchmarks, which enhances node collaboration and optimizes resource allocation. LazyEval Tip Selector minimizes redundant computations by leveraging a global cache and employing a lazy evaluation strategy, thereby improving computational efficiency. On the other hand, Precision Tip Selector uses a precise scoring mechanism to ensure accurate tip selection, thereby enhancing the robustness and reliability of the entire system. Collectively, these innovations enhance model training efficiency, support real-time updates, and improve the scalability of federated learning systems, making them well-suited for managing complex, dynamic environments.

By exploring the future network, smart technologies promote the development of cutting-edge industrial applications. Internet of Things (IoT) systems use sensing approaches to acquire data and control real-time processing and complex tasks. Several techniques have been proposed for coping with environmental behavior in industrial management and reducing the response in crucial circumstances. However, due to the unique and limited constraints of the industrial environment, managing data routing and sustainable development are recent research concerns. In addition, security is essential for industrial communication systems due to the probability of unauthorized access, thus trust level must be improved. The framework addresses real-world challenges in industrial networks by incorporating a lightweight data verification algorithm designed for green communication, reducing energy consumption while maintaining data integrity. First, predictive computing is implemented using ant colony optimization (ACO) based on real-time requirements and selects the dynamic and communication channels for data transmission across the industrial platform. Second, mobile sinks offer more authentic techniques for verifying sensor data and delivering it securely to the cloud servers. The framework was evaluated and validated in a simulation-based environment, revealing a considerable improvement in terms of network throughput, packet drop ratio, connectivity ratio, and network overhead over the existing approaches.

Drone participation in truck delivery is a potential booster for the last-mile logistics system, which has been an emerging hot research field. Among that, how to arrange a fleet of drones from the truck and optimize the vehicle routing problem with drones (VRPDs) is a key issue. However, most existing studies fail to derive the feasible solutions due to unordered customer distributions and multivariant drone feature constraints. In this article, we propose a novel self-driven reinforcement learning structure, named constraint-based hybrid pointer network (CH-Ptr-Net) model, which is a hybrid pointer network approach composed of graph neural network (GNN) embedding and attention decoder. We go into developing the simpler embedding version for multiple drones-assisted truck delivery. The CH-Ptr-Net model tends to generate a set of optimal delivery sequence, after constructing the mixed-integer linear program (MILP) formulation. Extensive numerical testing indicates that the proposed method performs better than recent exact and heuristic approaches for collaborative delivery routing optimization with the truck carrying multiple drones.

The National Science Foundation (NSF) supports engineering and computing education research that generates new knowledge about evidence-based practices and broadens participation in the engineering and computing workforce. Multiple NSF programs provide funding for projects that address current challenges in engineering and computing education at all levels of education including K-12, technical, undergraduate, and graduate education at different institutions (including HSIs). The goal of this workshop is for Program Director to provide prospective Principal Investigators with guidance on engineering and computing education funding opportunities at NSF and provide insights on preparing competitive proposals.

Abstract Image recognition based on computers is something human beings have been working on for many years. It is one of the most difficult tasks in the field of computer science, and improvements to this system are made when we speak. In this paper, we propose a methodology to automatically propose an appropriate title and add a specific sound to the image. Two models have been extensively trained and combined to achieve this effect. Sounds are recommended based on the image scene and the headings are generated using a combination of natural language processing and state-of-the-art computer vision models. A Top 5 accuracy of 67% and a Top 1 accuracy of 53% have been achieved. It is also worth mentioning that this is also the first model of its kind to make this forecast.

With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to build smart industry. Especially, explosive time-series data pose enormous challenges to the information mining and processing for modern industry. How to identify and detect the multidimensional industrial time-series anomaly is an important issue. However, most of the existing studies fail to handle with large amounts of unlabeled data, thus generating the undesirable results. In this article, we propose a novel integrated deep generative model, which is built by generative adversarial networks based on bidirectional long short-term memory and attention mechanism (AMBi-GAN). The structure for the generator and the discriminator is the bidirectional long short-term memory with attention mechanism, which can capture time-series dependence. Reconstruction loss and generation loss test the input of sample training space and random latent space. Experimental results show that the detection performance of our proposed AMBi-GAN has the potential to improve the detection accuracy of industrial multidimensional time-series anomaly toward IIoT in the era of artificial intelligence.

Recently we have witnessed the rise of Artificial Intelligence of Things (AIoT) and the shift of sensing paradigm from cloud-centric to the edge-centric, which effectively improves the sensing capability of intelligence transportation systems. To improve the real-time sensing performance, in this work we propose an ensemble sensing based scheme to solve the time-constraint synchronized inference problem and achieve robust inference with heterogeneous IoT devices in intelligence transportation systems. We design and implement Ensen, which incorporates various novel techniques such as customized DNN model design, KD-based model training, and dynamic deep ensemble management, etc., to achieve improved accuracy and maximize the computational resource usage of the whole sensing group. Extensive evaluations on different types of common IoT devices have shown that Ensen achieves a robust performance and can be easily extended to different types of convolutional neural networks.

To detect the wheelset deformation and wear, an intuitive method is to first collect wheelset multi-line laser stripe images by monitors, and then extract the centerlines to construct 3D contours that are transferred to the cloud data center by 6G communication. The images, however, contain flairs and fractures due to the influence of environmental interference and reflected light on the smooth surfaces. The image defects affect the accurate extraction of stripe centerlines. To segment the defects and inpaint them, we propose a new lightweight U-shaped segmentation model URS. A target-shaped receptive field is designed to efficiently extract the details of the local, the ring-shaped, and the cross-shaped context around the local, which facilitates segmenting various defects. A scale-select sub-module is designed to adjust the weights of features from different receptive fields. To train the model, a multi-line laser image defect segmentation dataset MLIDSD is constructed. Experiments demonstrate that the performance of our model surpasses twelve SOTA models explicitly and can achieve a balance between the accuracy and the lightweight requirement.

Drone logistics delivery is a potential booster to redefine the logistics system efficiency, which has been a new special hot research field. Among that, how to optimize drone trajectory data and find optimal delivery path is a crucial problem. However, most existing studies fail to derive the feasible trajectory data due to underestimating drone energy consumption and delivery multi-variant constraints for air transportation. In this paper, we propose a novel self-driven learning procedure, named attention-based pointer network (A-Ptr-Net) model, which can solve drone delivery trajectory optimization problem. The generated A-Ptr-Net model coupled with attention mechanism is effective on adapting to the new drone trajectory data automatically, regardless of explicit distance matrix. We go into developing the convex function constraints related to drone nonlinear energy consumption, customer demand and service time windows, which is applied on A-Ptr-Net model for optimizing the drone logistics delivery. Numerical experiments indicate that the proposed method performs significantly better than classical heuristics for drone trajectory analysis and optimization.

An unprecedented prosperity in artificial intelligence promotes the development of Internet of Vehicles (IoV). Assisted by edge computing, vehicles enable to offload data to edge servers in close proximity to users for processing, thus making up for the shortage of local computing resources. However, due to the uneven space-time distribution of traffic flow, edge servers of a certain road segment may be overwhelmed by the surge of service requests. Furthermore, IoV system will incur significant additional energy consumption and time delay because of the absence of a proper computation offloading scheme between edge servers. To cope with above challenges, a computing offloading method for energy and delay trade-offs with traffic flow prediction in edge computing-enabled IoV is proposed. We first design the graph weighted convolution network (GWCN) that can fully excavate the connectivity and distance relation information between road segments to conduct traffic flow prediction. The short-term prediction results are utilized as the basis for adjusting the resource allocation of edge resources in different regions. Then, a computation offloading method driven by deep deterministic policy gradient (DDPG) is leveraged to obtain an optimal computation offloading scheme for edge servers. Finally, extensive comparative experiments demonstrate the low prediction error of GWCN and superior performance of DDPG-driven method in reducing total time delay and energy consumption.

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.

Abstract Epilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95.

With unmanned aerial vehicle (UAV) technologies advanced rapidly, many applications have emerged in cities. However, those applications do not widely spread as the safety consideration hinders the UAV from integrating into the civilian environment. This work focuses on investigating the UAV emergency landing problem which is a critical safety functionality of UAV. This work proposed a graph convolution network (GCN)-based decision network to learn by imitating the human pilots’ landing strategy. To alleviate the needs of a large amount of real-world data for model training, the proposed model allows to be trained in a simulated environment and then transferred to the real-world scenario due to the separation of domain-specific terrain classes and domain-independent topological structures among down-looking camera images. The GCN-based decision network can be coupled with a topological heuristic to improve the performance of action prediction in an emergency situation. To evaluate the proposed method, this work implemented a simulation environment for collecting data and testing the UAV emergency landing. The empirical results in both simulated and real-world scenarios show that the proposed methods can outperform the state-of-the-art counterparts in terms of predictive accuracy and success landing rate.

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work.

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.

With the development of unmanned aerial systems (UAS), the ubiquitous deployment of UAS is becoming a trend. With the digital transceiver, the remote pilot can control the UAS remotely and effectively. With the deployment of 5G technology on a large scale, the convenience of remote control is becoming obvious and stable. However, the convenience of remote control technologies also brings more vulnerabilities to UAS. Software defined radio (SDR) has been explored to play system exploitation and penetration test for the radio system widely. With block programming, an SDR can become a hands-on system exploitation tool for research. With the adjustment of antennas and programmings, we can exploit the vulnerabilities of the UAS system and fixed the problem in advance. In this paper, we introduce an approach to leverage SDR to realize signal spoofing for GPS location and injection for digital communication. Based on SDR, we analyze the security of UAS on the GPS and digital connections. With the adjustment of antennas, we can define the SDR into a GPS spoofing tool and inject the packets in the communication of the digital transceiver. The evaluation shows the approach can delay the GPS searching for tens of minutes and disorder the connections between ground control station to UAS.

In the era of the Internet of Things (IoT), people access many applications through smartphones for controlling smart devices. Therefore, such a centralized node must follow a robust access control mechanism so that an intruder cannot control the connected devices. Recent reports suggest that password can be used as an authentication factor for accessing the smart setups. However, this static information can be compromised under the light of different machine learning (ML)-empowered attack mechanisms. Alarmingly, different sensors used in the IoT setup can also expose this static information to the adversaries. Password-based authentication that uses a challenge-response strategy is an effective solution for handling such threat scenarios. In this article, at first, we show that no existing usable challenge-response protocol is safe to be used in the public area network. Following this, we propose a challenge-response protocol that is more secure to use in the public domain. By using eight classifiers, we show that a learning-based threat specific to our protocol has a marginal impact on the method's security standard. The discussion in this article also suggests that the proposed protocol has usability and security advantages compared to the existing state of the art (e.g., reduces the number of interactions between the user and verifier by a factor of 0.5).

The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information. This article proposes a semisupervised prediction model, which exploits the improved unsupervised clustering algorithm to establish the fuzzy partition function, and then utilize the neural network model to build the information prediction function. The main purpose of this article is to effectively solve the time analysis of massive industry data. In the experimental part, we built a data platform on Spark, and used some marine environmental factor datasets and UCI public datasets as analysis objects. Meanwhile, we analyzed the results of the proposed method compared with other traditional methods, and the running performance on the Spark platform. The results show that the proposed method achieved satisfactory prediction effect.

Real-time multimedia applications have gained immense popularity in the industrial Internet of Things (IIoT) paradigm. Due to the impact of the complex industrial environment, the transmission of video streaming is usually unstable. In the duration of a low bandwidth transmission, existing optimization methods often reduce the original resolution of some frames in a random way to avoid the video interruption. If the key frames with some important content are selected to be transmitted with a low resolution, it will greatly reduce the effect of industrial supervision. In view of this challenge, a real-time video streaming optimization method by reducing the number of video frames transmitted in the IIoT environment is proposed. Concretely, a deep learning-based object detection algorithm is recruited to effectively select the key frames in our method. The key frames with the original resolution will be transmitted along with audio data. As some nonkey frames are selectively discarded, it is helpful for smooth network transmitting with fewer bandwidth requirements. Moreover, we employ edge servers to run the object detection algorithm, and adjust video transmission flexibly. Extensive experiments are conducted to validate the effectiveness, and dependability of our method.

Industrial Internet-of-Things (IIoT) has revolutionized almost every aspect of industrial manufacturing through industrial intelligence by incorporating production equipment, mobile terminals, and smart devices with wireless or wired networks. However, industrial visual information, such as images, videos, graphs, and texts, generated and collected from the industrial processes, contains various kinds of hidden value for industrial intelligence. Therefore, for the trend of providing ubiquitous industrial intelligence, new paradigms of perception and processing technologies of visual information such as recognition methods are required. However, industrial visual information is heterogeneous and complex with multiattributes, which presents significant challenges on visual information perception and processing technologies such as multiattributes recognition method. In this article, to provide industrial intelligence, a tensor-based visual feature recognition method is used to recognize the object from the perspective of multiattributes with the combination of attributes. To demonstrate its practical implementation, a case study about the industrial intelligence on the faulty location and diameter of bearings in the IIoT is described. Also, experiments on object recognition are carried out on the public image set COIL-100 to demonstrate the performance of the proposed method.

Data of the train communication network(TCN) is becoming more complicated, which results in higher requirements of the data processing unit-the multifunction vehicle bus controller (MVBC) connected within the TCN. Developing an MVBC is challenging because of the integrated hardware-software solutions to support reactions in real time and dynamic environment. Hence, there is an urgent need for a rigorous design framework to facilitate the development of MVBC. In this paper, we propose a design framework TooMVBC to generate executable MVBC code from formal verified computation model. TooMVBC uses formal computation model MVBChart to capture the specification of the MVBC at high level. First, primitive syntax of MVBChart is designed to model MVBC features (e.g. hierarchy structure, data flow of the encoder, the control logic of communication protocol), and semantics of MVBChart is formalized for simulation and verification. Then, semantics-preserving code generation algorithms are designed to generate VHDL code for partitioned hardware implementations and C code for partitioned software implementations from verified MVBChart model. The generated code can be loaded into the proposed flexible MVBC hardware architecture directly. Finally, supporting graphical model editor, simulator, verification translator, partitioning and code generator are implemented and seamlessly integrated into TooMVBC. When we apply TooMVBC to design MVBC with the highest class 5 according to the description of the standard IEC 61375, several critical ambiguousness or bugs in the standard are detected during formal verification of the constructed system model.

As the growth of vehicles and the acceleration of urbanization, the urban traffic congestion problem becomes a burning issue in our society. Constructing a software defined Internet of things(SD-IoT) with a proper traffic control scheme is a promising solution for this issue. However, existing traffic control schemes do not make the best of the advances of the multi-agent deep reinforcement learning area. Furthermore, existing traffic congestion solutions based on deep reinforcement learning(DRL) only focus on controlling the signal of traffic lights, while ignore controlling vehicles to cooperate traffic lights. So the effect of urban traffic control is not comprehensive enough. In this article, we propose Modified Proximal Policy Optimization (Modified PPO) algorithm. This algorithm is ideally suited as the traffic control scheme of SD-IoT. We adaptively adjust the clip hyperparameter to limit the bound of the distance between the next policy and the current policy. What's more, based on the collected data of SD-IoT, the proposed algorithm controls traffic lights and vehicles in a global view to advance the performance of urban traffic control. Experimental results under different vehicle numbers show that the proposed method is more competitive and stable than the original algorithm. Our proposed method improves the performance of SD-IoT to relieve traffic congestion.

Learning from crowds often performs in an active learning paradigm, aiming to improve learning performance quickly as well as to reduce labeling cost by selecting proper workers to (re)label critical instances. Previous active learning methods for learning from crowds do not have any proactive mechanism to effectively improve the reliability of workers, which prevents to obtain steadily rising learning curves. To help workers improve their reliability while performing tasks, this paper proposes a novel Interactive Learning framework with Proactive Cognitive Enhancement (ILPCE) for crowd workers. The ILPCE framework includes an interactive learning mechanism: When crowd workers perform labeling tasks in active learning, their cognitive ability to the specific domain can be enhanced through learning the exemplars selected by a psychological model-based machine teaching method. A novel probabilistic truth inference model and an interactive labeling scheme are proposed to ensure the effectiveness of the interactive learning mechanism and the performance of learning models can be simultaneously improved through a fast and low-cost way. Experimental results on three real-world learning tasks demonstrate that our ILPCE significantly outperforms five representative state-of-the-art methods.

Cyber-Physical Systems (CPS) is a multidimensional complex system that integrates computing, network, and physical environments. Due to the existence of network communications and embedded computers, CPS is vulnerable to attacks, so its security has become an important issue. The paper studies the main types of network attacks and the methods of detection, including the security estimation and control of CPS. Finally, the paper puts forward the key issues and challenges faced by the CPS network attack research.

The deepening of ocean measurement work requires higher transmission bandwidth and information calculation efficiency, which provides an opportunity for fog computing. Compared with cloud computing, fog computing shows distribution because it concentrates data processing and application on devices at the edge of the network. In this article, the Ocean of Things (OoT) framework is designed for marine environment monitoring based on the Internet of Things technology. The OoT is divided into three layers: 1) data acquisition layer; 2) fog layer; and 3) cloud layer. In the fog layer, in order to complete the quality control of the sensor measurement data, we use the numerical gradient-based method to process the original acquisition data. An improved D-S algorithm is designed for multisensor information fusion, reducing the data capacity and improving data quality. In the cloud layer, we build ocean information change models based on the fog layer data to predict the dynamic ocean environment. The designed fog layer is evaluated based on marine multisensor information. The results have shown that fog-based multisensor data processing shows low time consumption and high reliability. Moreover, this article uses real temperature data sets to evaluate the prediction accuracy of the cloud model. Finally, we tested the performance of the designed OoT framework with multiple data sets. The simulation results show that the framework can improve the efficiency of data utilization at sea and improve the efficiency of information utilization.

We study the problem of the energy-efficient networking in cloud services with geographically distributed data centers for industrial Internet-of-Things (IIoT) networks, specially for multimedia IIoT networks. This is significantly challenged by dynamic end-to-end request demands and unbalanced link energy efficiency, unbalanced and time-varying link utilization, and bandwidth and delay constraints for service requirements. To solve these issues, we propose a multi-constraint optimization model for the energy efficiency optimization in cloud computing services where data centers are geographically distributed and are interconnected by cloud networks. Our model jointly optimizes energy efficiency in data centers and cloud networks. An intelligent heuristic algorithm is presented to solve this model for dynamic request demands between different data centers and between data centers and users. This is implemented by combining the niche genetic algorithm and the random depth-first search. Simulation results for energy-efficient networking show that better gains in network energy efficiency can be achieved by our joint optimization. Joint optimization between industrial data centers and industrial cloud networks can further improve energy savings and link utilization for time-varying requests.

In the era of industrial 4.0, industrial Internet of Things (IIoT) has brought essential changes to human society. For IIoT, communication in network can be defined as the basic condition for further development and integrated information exchange. In this way, cached-enabled heterogeneous industrial network is necessary to be optimized. In this paper, we consider the optimal geographical placement of contents in cache-enabled heterogeneous networks to minimize the total missing probability. And the probability represents that typical user cannot find requested file in the nearby base stations (BSs). In contract to existing works which only concern content placement, we jointly optimize content placement at BSs and activation densities of BSs of different tiers subject to the cache size limits and the constraint on the BSs energy consumption cost. In addition, the user distribution in this work is modeled by a homogeneous Poisson Point Process. We prove that the original optimization problem can be transformed to a convex problem. The convexity of the optimization problem allows us to apply the KKT conditions to derive useful analytical results of the optimal solution. Based on this, we propose a low-complexity near-optimal algorithm to find the approximated content placement probabilities. We further extend the optimization to heterogeneous networks with the user distribution modeled by the modified Cluster Process. Extensive simulation results show the superior performance of joint optimization of content placement and BSs activation densities compared to only optimizing content placement.

Distributed machine learning has gained lots of attention due to the rapid growth of data. In this paper, we focus regularized empirical risk minimization problems, and propose two novel Distributed Accelerated Alternating Direction Method of Multipliers (D-A2DM2) algorithms for distributed classification. Based on the framework of Alternating Direction Method of Multipliers (ADMM), we decentralize the distributed classification problem as a global consensus optimization problem with a series of sub-problems. In D-A2DM2, we exploit ADMM with variance reduction for sub-problem optimization in parallel. Taking global update and local update into consideration respectively, we propose two acceleration mechanisms in the framework of D-A2DM2. In particular, inspired by Nesterov's accelerated gradient descent, we utilize it for global update to further improve time efficiency. Moreover, we also introduce Nesterov's acceleration for local update, and develop the corrected local update and symmetric dual update to accelerate the convergence with only a little change in the computational effort. Theoretically, D-A2DM2 has a linear convergence rate. Empirically, experimental results show that D-A2DM2 converge faster than existing distributed ADMM-based classification, and could be a highly efficient algorithm for practical use.

Fetal heart rate (FHR) is very significant to evaluate the status of fetus. However, based on traditional classification criteria is not accurate. With the rapid development of computer information technology, computer technology is vital for the analysis of FHR in electronic fetal monitoring (EFM). FHR is divided into three classes as: 1) normal; 2) suspicious; and 3) abnormal. Through the cooperation with the hospital, we got 4473 records, including 3012 normal, 1024 suspicious, 437 abnormal records by our EFM system. In order to improve the accuracy of fetal status assessment, high 1-D FHR records are divided into ten d-window segments, and then use convolutional neural network (CNN) to process the data in parallel. Finally, we use the voting method to determine the class of FHR records. We also made a comparative experiment, the feature extraction method based on basic statistics is used to extract the features of FHR. And then the features were applied as the input to support vector machine (SVM) and multilayer perceptron (MLP) to classify. According to the results of the experiment, the accuracy of classification of SVM, MLP, and CNN are 79.66%, 85.98%, and 93.24%, respectively.

The design of unified, efficient, and lightweight cryptographic platform for resource-constrained on-board devices such as sensors, microcontrollers, and actuators in the context of Internet of Vehicles remains an open and challenging problem, for both academic and industry. Elliptic curve cryptography (ECC) is considered as a promising encryption algorithm for the next generation communications, as it could provide the same strong security level using relatively smaller key size when compared to the currently used Rivest–Shamir–Adleman algorithm. However, traditional ECCs have the disadvantage of using a fixed curve, making it very easy to be intensively analyzed while being hard to construct a united platform for on-board devices with processors of different instruction lengths. To mitigate the above problem, this paper suggests a dynamic scalable elliptic curve cryptosystem. To synchronize the curve in use, a curve list of different security levels is generated and preserved on both parties. Since both parties randomly choose the curve and the prime number, a extra security level could be provided, so that the security level can still remain the same even using smaller key sizes, while the computation efficiency will be enhanced and the power consumption will be reduced, which is especially suitable for the application in on-board embedded devices. Detailed experimental results illustrate that the presented scheme improves the efficiency by 30% in average when compared with traditional ECC implementations on a similar security level. Therefore, the proposed scalable ECC scheme as a unified cryptographic platform is more economic for these on-board devices in vehicles.

For cyber-physical industrial networks, more and more multimedia data is faced in high-speed information transmission. The explosive data brings more challenges to the architecture of modern industrial networks. In this way, cache resource allocation technology is necessary for practical applications. In order to design reasonable caching framework, how to predict the multimedia data request is an important issue. In order to keep efficient and reliable data transmission in wireless industrial networks, security design is also critical for existing cache resource allocation. Based on previous works, some promising technologies have been applied, such as heterogeneous ultradense networks, wireless edge caching, and web content popularity prediction. In this paper, we summarize these promising technologies and provide a useful guidance for security design in cyber-physical cache resource allocation system. Specially, we can divide the proposed method into three main steps. First of all, a spatio-temporal multimedia content prediction based on long short-term memory is proposed for accurate prediction on multimedia data request. After that, we make use of Zipf fitting for caching model. At last, the caching optimization considering security is put forward in this paper. Experimental results show the satisfied performance of our proposed algorithm and it has obvious potential application value in cyber-physical industrial networks with cache resource allocation technology.

Recently, more and more users and enterprises have entrusted data storage and platform construction to proxy cloud service provider (PCSP) through cloud technology. Under this background, the attribute-based encryption (ABE) mechanism is an alternative to fill the drawbacks of the traditional encryption through flexible fine-grained access policy and collusion prevention. However, there exist some security issues when the access policy and file need to be updated in practical applications. And the ABE has the problems of excessive computation and storage costs. In this article, an efficient ciphertext-policy ABE scheme with policy update and file update is proposed in cloud computing. The ciphertext components generated by first encryption can be shared when the policy update and file update happens. It reduces the storage and communication costs of the client, and the computational cost of the PCSP. Moreover, the proposed scheme is proved to be secure under the assumption of decision q-parallel bilinear Diffie–Hellman exponent (BDHE). Finally, experimental simulation shows that the proposed scheme is highly efficient in terms of policy update and file update.

Radio frequency identification (RFID) technology, with its advantages such as battery-free tags, low cost, and scalability, has been playing an important role in many application domains, such as large-scale storage systems, supermarkets, construction sites, etc. Many of those application scenarios also require indoor positioning technologies, for example, warehouse goods positioning, item positioning in production assembly lines, and worker positioning in construction sites. However,indoor positioning using RFID faces accuracy degradation in dynamic environments, especially when tracking randomly moving targets. In this paper, we proposePSOTrack, a continuous RFID-based tracking system for random moving targets in unconstrained indoor environments. InPSOTrack, a data preprocessed, and an optimized particle swarm optimization algorithm is applied to determine the initial position, after that a dynamic correction method for trajectory prediction is proposed for continuous tracking. Results show that the proposed algorithm effectively improves the positioning accuracy and is able to achieve 1 m localization accuracy in dynamic indoor environments, which makes it a promising technology to support future pervasive RFID-based tracking applications.

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.

A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to $22\%$ relative to existing methods.