Article
Automation & Control Systems
Lin Li, Hongchun Qu, Zhaoni Li, Jian Zheng, Xiaoming Tang, Ping Liu
Summary: In the industrial Internet of Things (IIoT), anomaly detection is crucial for ensuring system safety and product quality. However, the abundance of unlabeled, high-dimensional data in the IIoT presents challenges to existing anomaly detection methods. To address these challenges, a framework called data reconstruction via consensus graph learning (DRCG) and two anomaly score functions are proposed.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Xiaoding Wang, Sahil Garg, Hui Lin, Jia Hu, Georges Kaddoum, Md Jalil Piran, M. Shamim Hossain
Summary: This paper proposes a reliable anomaly detection strategy for Industrial Internet of Things (IIoT) using federated learning. By training local models with deep reinforcement learning algorithm and introducing privacy leakage degree and action relation, the detection accuracy can be greatly improved, achieving privacy preservation.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Telecommunications
Abbas Yazdinejad, Mostafa Kazemi, Reza M. Parizi, Ali Dehghantanha, Hadis Karimipour
Summary: In this paper, an ensemble deep learning model is proposed to identify out-of-norm activities for cyber threat hunting in IIoT. The model combines LSTM and AE to learn normal data patterns and reduce data dimension. It also addresses the imbalanced nature of IIoT datasets by extracting new balanced data. Experimental results show that the proposed model outperforms conventional machine learning classifiers and other related models.
DIGITAL COMMUNICATIONS AND NETWORKS
(2023)
Article
Automation & Control Systems
Mohamed Abdel-Basset, Victor Chang, Hossam Hawash, Ripon K. Chakrabortty, Michael Ryan
Summary: This article presents a forensics-based deep learning model, Deep-IFS, for intrusion detection in IIoT traffic. By utilizing local gated recurrent unit and multihead attention layers to learn local and global representations, and deploying and training the model in a fog computing environment, it effectively handles large-scale IIoT traffic data and achieves good distributed processing results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Henry Vargas, Carlos Lozano-Garzon, German A. Montoya, Yezid Donoso
Summary: This paper integrates Blockchain algorithms and Machine Learning techniques to create a comprehensive protection mechanism for IoT device networks, allowing for threat identification, activation of secure information transfer mechanisms, and adaptation to the computational capabilities of industrial IoT. The proposed solution achieves its objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network, surpassing traditional detection mechanisms such as an IDS in some cases.
Article
Computer Science, Information Systems
Yulei Wu, Hong-Ning Dai, Haina Tang
Summary: The Industrial Internet of Things (IIoT) is crucial for the digital transformation of traditional industries towards Industry 4.0. Graph neural networks (GNNs) have shown promise in anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. This article provides valuable insights and case studies in utilizing GNNs for anomaly detection in IIoT.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Akbar Telikani, Jun Shen, Jie Yang, Peng Wang
Summary: Cyber attacks and intrusions pose significant barriers to the adoption of Industrial Internet of Things (HoT) in critical industries. Researchers have introduced the EvolCostDeep model to address the problem of imbalanced data distribution in HoT environments, and have designed the DeepIDSFog framework to improve the scalability of HoT IDSs.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Xing Liu, Wei Yu, Fan Liang, David Griffith, Nada Golmie
Summary: This article proposes a general framework to adopt transfer learning in Industrial IoT (IIoT) systems, categorizing the application of transfer learning into four common scenarios based on dataset size and distribution. Design workflows are created for each scenario based on their characteristics.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Yi Liu, Sahil Garg, Jiangtian Nie, Yang Zhang, Zehui Xiong, Jiawen Kang, M. Shamim Hossain
Summary: This article proposes a communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. The framework includes an FL framework, AMCNN-LSTM model, and gradient compression mechanism, which can improve generalization ability, accurately detect anomalies, and enhance communication efficiency.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Analytical
Yuri Santo, Roger Immich, Bruno L. L. Dalmazo, Andre Riker
Summary: Industrial production and manufacturing systems require automation, reliability, and low-latency intelligent control. The Industrial Internet of Things (IIoT) enables precise and low-latency intelligent computing and provides essential building blocks to drive manufacturing systems to higher productivity, efficiency, and safety. However, hardware failures and faults in IIoT present critical challenges. This article proposes the DASIF framework, which applies edge computing and machine learning to detect IIoT faults and trigger an alert state for improved communication reliability and efficient decision-making.
Article
Automation & Control Systems
Fazlullah Khan, Ryan Alturki, Md Arafatur Rahman, Spyridon Mastorakis, Imran Razzak, Syed Tauhidullah Shah
Summary: This article proposes a novel approach to improve the trustworthiness of IIoT-enabled networks by combining deep learning and ensemble learning techniques to enhance cyberattack detection in SCADA-based networks, thereby improving the security and associated measure of trustworthiness in IIoT networks.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Sheng Chen, Qihang Zhang, Xiaodong Dong, Xiaoyi Tao, Keqiu Li, Tie Qiu, Ivan Lee
Summary: Mobile edge computing (MEC) is increasingly popular due to its powerful computing capacities near end users or devices. This article proposes a framework called Sublessor to reduce wide area network (WAN) transmission costs for cooperative MEC providers. The key idea is to allow certain MEC providers to act as Internet transit brokers and resell network traffic at a reasonable price. The algorithm presented in this article finds the optimal number of brokers and reselling price, significantly reducing transmission costs by up to 35% according to experimental results.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Yange Chen, Yuan Ping, Zhili Zhang, Baocang Wang, SuYu He
Summary: A novel privacy-preserving image multi-classification deep-learning (PIDL) model is presented in this paper, with two schemes proposed that adopt secure calculation protocols applied in a fog control center (FCC) with a non-colluding honest server to protect data and model privacy in robot systems. The proposed schemes realize security, correctness, and efficiency with low communication and computational costs, as demonstrated in security analysis and performance evaluation.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Nenavath Chander, Mummadi Upendra Kumar
Summary: The Industrial Internet of Things (IIoT) is the essential part of the transition towards Industry 4.0 for conventional industries. By integrating instruments, sensors, and other industry devices with the Internet, IIoT enables data analysis, acquisition, and automated control, ultimately enhancing the production and performance of IIoT systems. In this study, a novel metaheuristic feature selection with deep learning enabled anomaly detection technique, named MFSDL-ADIIoT, is developed to effectively identify and classify anomalies in the IIoT environment. The MFSDL-ADIIoT model utilizes a deer hunting optimization algorithm based feature selection technique and cascaded recurrent neural network (CRNN) system for anomaly detection, with the parameters of the CRNN model optimized using sparrow search algorithm. Extensive simulations demonstrate the better performance of the MFSDL-ADIIoT model compared to other recent approaches.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Chao Li, Hui Yang, Zhengjie Sun, Qiuyan Yao, Bowen Bao, Jie Zhang, Athanasios V. V. Vasilakos
Summary: The Industrial Internet of Things (IIoT) is considered a revolutionary technology to increase productivity. This article proposes a federated hierarchical trust interaction scheme (FHTI) based on blockchain for cross-domain IIoT to protect data privacy and ensure security of devices. Simulation results show that the FHTI scheme improves identity authentication speed and detection accuracy.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Civil
Vincent Havyarimana, Zhu Xiao, Alexis Sibomana, Di Wu, Jing Bai
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Computer Science, Theory & Methods
Di Wu, Xiang Nie, Eskindir Asmare, Dmitri I. Arkhipov, Zhijing Qin, Renfa Li, Julie A. McCann, Keqin Li
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2020)
Article
Engineering, Civil
Di Wu, Lambros Lambrinos, Thomas Przepiorka, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Engineering, Civil
Yourong Huang, Zhu Xiao, Dong Wang, Hongbo Jiang, Di Wu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2020)
Article
Computer Science, Information Systems
Di Wu, Xin Huang, Xiaofeng Xie, Xiang Nie, Lichun Bao, Zhijin Qin
Summary: In response to the challenges posed by IoT devices on mobile access control and monitoring, LEDGE is introduced as a secure and agile software-defined edge computing system. By incorporating efficient location authentication, optimal access point assignment, scalable Personal AP protocol, and anomaly detection through deep learning, LEDGE demonstrates promising results in mobile IoT access management.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Di Wu, He Xu, Zhongkai Jiang, Weiren Yu, Xuetao Wei, Jiwu Lu
Summary: The paper introduces EdgeLSTM system, which enhances IoT computing performance using Grid LSTM network and multi-class support vector machine. Experimental results demonstrate the robust performance of the EdgeLSTM system in handling time series data.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2021)
Article
Surgery
Renke He, Wenxiu Yang, Di Wu, Haining Wang
Summary: This study analyzed the total soft tissue thickness and fat layer thickness in the craniofacial CT data of 280 Chinese individuals, revealing significant differences in thickness based on sex and age. While male individuals generally had greater total soft tissue thickness, female individuals had thicker fat layers. The total soft tissue thickness did not show a clear trend with age, whereas the fat layer thickness generally decreased with age.
JOURNAL OF CRANIOFACIAL SURGERY
(2021)
Article
Engineering, Civil
Di Wu, Zhanxiu Zeng, Fengrui Shi, Weiren Yu, Tao Wu, Qiang Liu
Summary: This paper introduces a mobile crowdsensing system called ParkHop to address challenges in timely information sharing and low-cost infrastructure deployment regarding parking availability in cities. By utilizing a joint estimator to process data and evaluate worker reliability, as well as designing specific worker selection methods and incentive schemes, the aggregation and dissemination of parking information has been effectively enhanced.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jianhua Xiao, Zhu Xiao, Dong Wang, Vincent Havyarimana, Chenxi Liu, Chengming Zou, Di Wu
Summary: A novel ensemble transfer regression framework is proposed in this paper to address the challenges of inaccurate and incomplete trajectory data caused by GNSS outages. The framework utilizes transfer learning to construct a fine-grained trajectory dataset and integrates a regression-to-classification process for incremental training in dynamically changing environments. Experimental results demonstrate the superiority of the proposed framework in trajectory interpolation prediction compared to other methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Engineering, Civil
Zhengyu Tan, Ningyi Dai, Yating Su, Ruifo Zhang, Yijun Li, Di Wu, Shutao Li
Summary: This paper provides an in-depth review of Human-Machine Interaction (HMI) in Intelligent and Connected Vehicles (ICVs), including the cutting-edge technology classification, human factors issues, and future opportunities for advanced and pleasant HMI in ICVs. It emphasizes the significance and challenges of HMI technology in ICVs through discussions on research and application development status, interaction quality, and value acquisition.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Proceedings Paper
Computer Science, Cybernetics
Weiren Yu, Jian Yang, Maoyin Zhang, Di Wu
Summary: In this paper, the authors propose a novel scalable graph-theoretic similarity model based on heat diffusion called CoSimHeat. They first formulate the CoSimHeat model by using heat diffusion to simulate similarity propagations on the Web. The experimental results show that CoSimHeat achieves higher accuracy and is significantly faster than state-of-the-art competitors.
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)
(2022)
Article
Computer Science, Information Systems
Tao Wu, Pengfei Zhang, Jianxin Qin, Di Wu, Longgang Xiang, Yiliang Wan
Article
Computer Science, Information Systems
Dmitri I. Arkhipov, Di Wu, Tao Wu, Amelia C. Regan
Article
Computer Science, Information Systems
Shigeng Zhang, Yinggang Li, Xuan Liu, Song Guo, Weiping Wang, Jianxin Wang, Bo Ding, Di Wu
PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT
(2020)