Showing papers in "IEEE Transactions on Industrial Informatics in 2021"

An Overview of Recent Advances in Coordinated Control of Multiple Autonomous Surface Vehicles

Abstract: Autonomous surface vehicles (ASVs) are marine vessels capable of performing various marine operations without a crew in a variety of cluttered and hostile water/ocean environments For complex missions, there are increasing needs for deploying a fleet of ASVs instead of a single one to complete difficult tasks Cooperative operations with a fleet of ASVs offer great advantages with enhanced capability and efficacy Despite various application potentials, coordinated motion control of ASVs pose great challenges due to the multiplicity of ASVs, complexity of intravehicle interactions and fleet formation with collision avoidance requirements, and scarcity of communication bandwidths in sea environments Coordinated control of multiple ASVs has received considerable attention in the last decade This article provides an overview of recent advances in coordinated control of multiple ASVs First, some challenging issues and scenarios in motion control of ASVs are presented Next, coordinated control architecture and methods of multiple ASVs are briefly discussed Then, recent results on trajectory-guided, path-guided, and target-guided coordinated control of multiple ASVs are reviewed in detail Finally, several theoretical and technical issues are suggested to direct future investigations including network-based coordination, event-triggered coordination, collision-free coordination, optimization-based coordination, data-driven coordination of ASVs, and task-region-oriented coordination of multiple ASVs and autonomous underwater vehicles

Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks

Abstract: Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems

Abstract: The rapid convergence of legacy industrial infrastructures with intelligent networking and computing technologies (e.g., 5G, software-defined networking, and artificial intelligence), have dramatically increased the attack surface of industrial cyber–physical systems (CPSs). However, withstanding cyber threats to such large-scale, complex, and heterogeneous industrial CPSs has been extremely challenging, due to the insufficiency of high-quality attack examples. In this article, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against industrial CPSs. Specifically, we first design a new deep learning-based intrusion detection model for industrial CPSs, by making use of a convolutional neural network and a gated recurrent unit. Second, we develop a federated learning framework, allowing multiple industrial CPSs to collectively build a comprehensive intrusion detection model in a privacy-preserving way. Further, a Paillier cryptosystem-based secure communication protocol is crafted to preserve the security and privacy of model parameters through the training process. Extensive experiments on a real industrial CPS dataset demonstrate the high effectiveness of the proposed DeepFed scheme in detecting various types of cyber threats to industrial CPSs and the superiorities over state-of-the-art schemes.

From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges

Abstract: The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Abstract: The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes In this article, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using modified convolutional neural network (CNN) with transfer learning First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system

QoS-Guarantee Resource Allocation for Multibeam Satellite Industrial Internet of Things With NOMA

Abstract: The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

Blockchain-Enhanced Data Sharing With Traceable and Direct Revocation in IIoT

Abstract: The industrial Internet of Things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semicredibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users has become an urgent problem. To solve these problems, in this article, a blockchain-enhanced security access control scheme that supports traceability and revocability has been proposed in IIoT for smart factories. The blockchain first performs unified identity authentication, and stores all public keys, user attribute sets, and revocation list. The system administrator then generates system parameters and issues private keys to users. The domain administrator is responsible for formulating domain security and privacy-protection policies, and performing encryption operations. If the attributes meet the access policies and the user's ID is not in the revocation list, they can obtain the intermediate decryption parameters from the edge/cloud servers. Malicious users can be tracked and revoked during all stages if needed, which ensures the system security under the Decisional Bilinear Diffie–Hellman (DBDH) assumption and can resist multiple attacks. The evaluation has shown that the size of the public/private keys is smaller compared to other schemes, and the overhead time is less for public key generation, data encryption, and data decryption stages.

A Blockchained Federated Learning Framework for Cognitive Computing in Industry 4.0 Networks

Abstract: Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks, performance, and inadequate data resources, etc., have to be resolved. Recent research works studied the problem lightly, which often leads to unreliable performance, inefficiency, and privacy leakage. In this article, we developed a decentralized paradigm for big data-driven cognitive computing (D2C), using federated learning and blockchain jointly. Federated learning can solve the problem of “data island” with privacy protection and efficient processing while blockchain provides incentive mechanism, fully decentralized fashion, and robust against poisoning attacks. Using blockchain-enabled federated learning help quick convergence with advanced verifications and member selections. Extensive evaluation and assessment findings demonstrate D2C's effectiveness relative to existing leading designs and models.

Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

Abstract: With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry field. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional anomaly detection for intelligent industrial application. In this article, to mitigate the inconsistency between dimensionality reduction and feature retention in imbalanced IBD, we propose a variational long short-term memory (VLSTM) learning model for intelligent anomaly detection based on reconstructed feature representation. An encoder–decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data. Three loss functions are defined and quantified to constrain the reconstructed hidden variable into a more explicit and meaningful form. A lightweight estimation network is then fed with the refined feature representation to identify anomalies in IBD. Experiments using a public IBD dataset named UNSW-NB15 demonstrate that the proposed VLSTM model can efficiently cope with imbalance and high-dimensional issues, and significantly improve the accuracy and reduce the false rate in anomaly detection for IBD according to F1, area under curve (AUC), and false alarm rate (FAR).

A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

Abstract: Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery (LIB). However, accurately predicting the remaining useful life (RUL) of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods is greatly limited. Besides, mathematical models established by model-driven methods to represent degradation process are unstable because of external factors like temperature. To solve this problem, a new LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (LSTM), namely Auto-CNN-LSTM, is proposed in this article. This method is developed based on deep CNN and LSTM to mine deeper information in finite data. In this method, an autoencoder is utilized to augment the dimensions of data for more effective training of CNN and LSTM. In order to obtain continuous and stable output, a filter to smooth the predicted value is used. Comparing with other commonly used methods, experiments on a real-world dataset demonstrate the effectiveness of the proposed method.

Trustworthiness in Industrial IoT Systems Based on Artificial Intelligence

Abstract: The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

A Survey on Deep Learning for Data-Driven Soft Sensors

Abstract: Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to the poor performance of traditional soft sensor modeling methods. Deep learning, as a kind of data-driven approach, shows its great potential in many fields, as well as in soft sensing scenarios. After a period of development, especially in the last five years, many new issues have emerged that need to be investigated. Therefore, in this article, the necessity and significance of deep learning for soft sensor applications are demonstrated first by analyzing the merits of deep learning and the trends of industrial processes. Next, mainstream deep learning models, tricks, and frameworks/toolkits are summarized and discussed to help designers propel the developing progress of soft sensors. Then, existing works are reviewed and analyzed to discuss the demands and problems occurred in practical applications. Finally, outlook and conclusions are given.

Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

Abstract: As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Intelligent Fault Diagnosis by Fusing Domain Adversarial Training and Maximum Mean Discrepancy via Ensemble Learning

Abstract: Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart manufacturing. The massive amount of data in IIoT promote the development of deep learning-based health monitoring for industrial equipment. Since monitoring data for mechanical fault diagnosis collected on different working conditions or equipment have domain mismatch, models trained with training data may not work in practical applications. Therefore, it is essential to study fault diagnosis methods with domain adaptation ability. In this article, we propose an intelligent fault diagnosis method based on an improved domain adaptation method. Specifically, two feature extractors concerning feature space distance and domain mismatch are trained using maximum mean discrepancy and domain adversarial training respectively to enhance feature representation. Since separate classifiers are trained for feature extractors, ensemble learning is further utilized to obtain final results. Experimental results indicate that the proposed method is effective and applicable in diagnosing faults with domain mismatch.

Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction

Abstract: Accurate prediction of remaining useful life (RUL) has been a critical and challenging problem in the field of prognostics and health management (PHM), which aims to make decisions on which component needs to be replaced when. In this article, a novel deep neural network named convolution-based long short-term memory (CLSTM) network is proposed to predict the RUL of rotating machineries mining the in situ vibration data. Different from previous research that simply connects a convolutional neural network (CNN) to a long short-term memory (LSTM) network serially, the proposed network conducts convolutional operation on both the input-to-state and state-to-state transitions of the LSTM, which contains both time–frequency and temporal information of signals, not only preserving the advantages of LSTM, but also incorporating time–frequency features. The convolutional structure in the LSTM has the ability to capture long-term dependencies and extract features from the time–frequency domain at the same time. By stacking the multiple CLSTM layer-by-layer and forming an encoding-forecasting architecture, the deep learning model is established for RUL prediction in this article. Run-to-failure tests on bearings are conducted, and vibration responses are collected. Using the proposed algorithm, RUL is predicted, and as a comparison, the performance from other methods, including deep CNNs and deep LSTM, is evaluated using the same dataset. The comparative study indicates that the proposed CLSTM network outperforms the current deep learning algorithms in URL prediction and system prognosis with respect to better accuracy and computation efficiency.

Blockchain-Based Reliable and Efficient Certificateless Signature for IIoT Devices

Abstract: In this paper, we propose a novel pairing-free certificateless scheme that utilizes the state-of-the-art blockchain technique and smart contract to construct a novel reliable and efficient CLS scheme. Then we simulate the Type-I and Type-II adversaries to verify the trustworthiness of our scheme. Security analysis as well as performance evaluation outcomes present our design can hold more reliable security assurance with less computation cost (i.g., reduced by around 40.0% at most) and communication cost (i.g., reduced by around 94.7% at most) than other related schemes.

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

Abstract: With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

Battery Thermal- and Health-Constrained Energy Management for Hybrid Electric Bus Based on Soft Actor-Critic DRL Algorithm

Abstract: Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning

Abstract: In the past years, the practical cross-domain machinery fault diagnosis problems have been attracting growing attention, where the training and testing data are collected from different operating conditions. The recent advances in closed-set domain adaptation have well addressed the basic problem where the fault mode sets are identical in the source and target domains. While some attempts have also been made on the partial and open-set domain adaptations, no prior information of the target-domain fault modes can be usually available in the real industries, that forms a challenging problem in transfer learning. This article proposes a universal domain adaptation method for fault diagnosis, where no explicit assumption is made on the target label set. A hybrid approach with source class-wise and target instance-wise weighting mechanism is proposed for selective adaptation. By using additional outlier identifier, the proposed method can automatically recognize the unknown fault modes while achieving class-level alignments for the shared health states, without knowing the target label set. Experiments on two rotating machine datasets validate the proposed method, which is promising for practical applications under strong data uncertainties.

Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis

Abstract: In this article, a challenging fault diagnosis task is studied, in which no samples of the target faults are available for the model training. This scenario has hardly been studied in industrial research. But it is a common problem that massive fault samples are not available for the target faults, which limits the successes of conventional data-driven approaches in practical application. Here, we introduce the idea of zero-shot learning into the industry field, and tackle the zero-sample fault diagnosis task by proposing the fault description based attribute transfer method. Specifically, the method learns to determine the fault categories using the human-defined fault descriptions instead of the collected fault samples.The defined description consists of arbitrary attributes of the faults, including the fault positions, the consequences of the fault, and even the cause of the fault, etc. For the attribute knowledge of target faults, they can be prelearned and transferred from some readily available faults occurred in the same process. Afterwards, the target faults can be diagnosed based on the defined fault descriptions without the need for any additional data based training. Besides, the supervised principle component analysis is adopted in our method to extract the attribute related features to offer an effective attribute learning. We analyze and interpret the feasibility of the fault description based method theoretically. Also, the zero-sample fault diagnosis experiments are designed and conducted on the benchmark Tennessee–Eastman process and the real thermal power plant process to validate the effectiveness. The results show that it is indeed possible to diagnose target faults without their samples.

A Deep Graph Neural Network-Based Mechanism for Social Recommendations

Abstract: Nowadays, the issue of information overload is gradually gaining exposure in the Internet of Things (IoT), calling for more research on recommender system in advance for industrial IoT scenarios. With the ever-increasing prevalence of various social networks, social recommendations (SoR) will certainly become an integral application that provides more feasibly personalized information service for future IoT users. However, almost all of the existing research managed to explore and quantify correlations between user preferences and social relationships, while neglecting the correlations among item features which could further influence the topologies of some social groups. To tackle with this challenge, in this article, a deep graph neural network-based social recommendation framework (GNN-SoR) is proposed for future IoTs. First, user and item feature spaces are abstracted as two graph networks and respectively encoded via the graph neural network method. Next, two encoded spaces are embedded into two latent factors of matrix factorization to complete missing rating values in a user-item rating matrix. Finally, a large amount of experiments are conducted on three real-world data sets to verify the efficiency and stability of the proposed GNN-SoR.

Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning

Abstract: Data-driven machinery fault diagnosis methods have been successfully developed in the past decades. However, the cross-domain diagnostic problems have not been well addressed, where the training and testing data are collected under different operating conditions. Recently, domain adaptation approaches have been popularly used to bridge this gap, which extract domain-invariant features for diagnostics. Despite the effectiveness, most existing methods assume the label spaces of training and testing data are identical that indicates the fault mode sets are the same in different scenarios. In practice, new fault modes usually occur in testing, which makes the conventional methods focusing on marginal distribution alignment less effective. In order to address this problem, a deep learning-based open-set domain adaptation method is proposed in this study. Adversarial learning is introduced to extract generalized features, and an instance-level weighted mechanism is proposed to reflect the similarities of testing samples with known health states. The unknown fault mode can be effectively identified, and the known states can be also recognized. Entropy minimization scheme is further adopted to improve generalization. Experiments on two practical rotating machinery datasets validate the proposed method. The results suggest the proposed method is promising for open-set domain adaptation problems, which largely enhances the applicability of data-driven approaches in the real industries.

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

Abstract: Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Abstract: Visual perception refers to the process of organizing, identifying, and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this article, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis, and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this article also summarizes the current challenges of visual perception and predicts its future development trends.

Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Abstract: Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation

Abstract: Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this article, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.

Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT

Abstract: The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV

Abstract: The automotive industry, a key part of industrial Internet of Things, is now converging with cognitive computing (CC) and leading to industrial cognitive Internet of Vehicles (CIoV). As the major data source of industrial CIoV, social media has a significant impact on the quality of service (QoS) of the automotive industry. To provide vehicular social media services with low latency and high reliability, edge computing is adopted to complement cloud computing by offloading CC tasks to the edge of the network. Generally, task offloading is implemented based on the premise that edge servers (ESs) are appropriately quantified and located. However, the quantification of ESs is often offered according to empirical knowledge, lacking analysis on real condition of intelligent transportation system (ITS). To address the abovementioned problem, a c ollaborative method for the q uantification and p lacement of ESs, named CQP, is developed for social media services in industrial CIoV. Technically, CQP begins with a population initializing strategy by Canopy and K-medoids clustering to estimate the approximate ES quantity. Then, nondominated sorting genetic algorithm III is adopted to achieve solutions with higher QoS. Finally, CQP is evaluated with a real-world ITS social media data set from China.

PMRSS: Privacy-preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare

Abstract: In medical field, previous patients' cases are extremely private as well as intensely valuable to current disease diagnosis. Therefore, how to make full use of precious cases while not leaking out patients' privacy is a leading and promising work especially in future privacy-preserving intelligent medical period. In this paper, we investigate how to securely invoke patients' records from past case-database while protecting the privacy of both current diagnosed patient and the case-database and construct a privacy-preserving medical record searching scheme based on ElGamal Blind Signature. In our scheme, by blinded the healthy data of the patient and the database of the intelligent doctor respectively, the patient can securely make self-helped medical diagnosis by invoking past case-database and securely comparing the blinded abstracts of current data and previous records. Moreover, the patient can obtain target searching information intelligently at the same time he knows whether the abstracts match or not instead of obtaining it after matching. It greatly increases the timeliness of information acquisition and meets high-speed information sharing requirements especially in 5G era. What's more, our proposed scheme achieves bilateral security, that is, whether the abstracts match or not, both of the privacy of the case-database and the private information of the current patient are well protected. Besides, it resists different levels of violent ergodic attacks by adjusting the number of zeros in a bit string according to different security requirements.

Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications

Abstract: To improve the quality of service (QoS) needed by several applications areas, the Internet of Things (IoT) tasks are offloaded into the fog computing instead of the cloud. However, the availability of ongoing energy heads for fog computing servers is one of the constraints for IoT applications because transmitting the huge quantity of the data generated using IoT devices will produce network bandwidth overhead and slow down the responsive time of the statements analyzed. In this article, an energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the QoSs required by users. In addition to the standard MPA, we proposed the other two versions. The first version is called modified MPA (MMPA), which will modify MPA to improve their exploitation capability by using the last updated positions instead of the last best one. The second one will improve MMPA by the ranking strategy based reinitialization and mutation toward the best, in addition to reinitializing, the half population randomly after a predefined number of iterations to get rid of local optima and mutated the last half toward the best-so-far solution. Accordingly, MPA is proposed to solve the continuous one, whereas the TSFC is considered a discrete one, so the normalization and scaling phase will be used to convert the standard MPA into a discrete one. The three versions are proposed with some other metaheuristic algorithms and genetic algorithms based on various performance metrics such as energy consumption, makespan, flow time, and carbon dioxide emission rate. The improved MMPA could outperform all the other algorithms and the other two versions.