Article
Computer Science, Information Systems
Abdul Razzaq, Aakash Ahmad, Asad Waqar Malik, Mahdi Fahmideh, Rabie A. Ramadan
Summary: The Internet of Underwater Things (IoUTs) is a specific genre of Internet of Things (IoTs) that continuously collects data about ocean ecosystems via underwater sensors. This paper proposes a layered architecture for IoUTs and evaluates its performance through a case study-based approach.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Priyank Sunhare, Rameez R. Chowdhary, Manju K. Chattopadhyay
Summary: This paper provides a systematic review of various data mining techniques employed in large and small scale IoT applications to create an intelligent environment. Additionally, it presents an overview of a cloud-assisted IoT Big Data mining system to emphasize the importance of data mining in an IoT environment.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Muhammad Babar, Akmal Saeed Khattak, Mian Ahmad Jan, Muhammad Usman Tariq
Summary: The rise of IoT has led to the development of smart cities, where energy management and efficient data processing are crucial for sustainability. A proposed framework in this article focuses on energy efficiency of IoT devices and data analysis for cities, showcasing promising results in terms of energy management and service optimization.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Engineering, Multidisciplinary
Manu Suvarna, Ken Shaun Yap, Wentao Yang, Jun Li, Yen Ting Ng, Xiaonan Wang
Summary: With the rise of Industry 4.0 and smart manufacturing, there is a growing belief that traditional manufacturing is transitioning towards a new paradigm focused on innovation, automation, better customer response, and intelligent systems. The concept of cyber-physical production systems (CPPS) plays a crucial role in data-driven manufacturing, decentralized manufacturing, and integrated blockchain for data security, connecting smart manufacturing aspects and transforming manufacturing towards intuition and automation.
Review
Computer Science, Information Systems
Ravi Sharma, Balazs Villanyi
Summary: Smart manufacturing systems (SMS) are important applications in the Industry 4.0 era, offering advantages over traditional production systems and being used as a performance-enhancing strategy in manufacturing enterprises. This study uses analytical and descriptive research techniques to identify and assess essential components for SMS evaluation.
INTERNET OF THINGS
(2022)
Article
Computer Science, Artificial Intelligence
Xianyu Zhang, Xinguo Ming
Summary: With the advancement of various technologies, the industrial internet has evolved through different stages. However, there is a lack of a comprehensive research framework for studying the top-level planning of the Industrial Internet Platform (IIP). Additionally, there are few studies on the specific path and steps for implementing IIP in specific industries and enterprises.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Luis Omar Colombo-Mendoza, Mario Andres Paredes-Valverde, Maria del Pilar Salas-Zarate, Rafael Valencia-Garcia
Summary: The convergence of IoT technologies and machine-learning techniques can lead to the development of smart farming systems in the agricultural field, serving as decision support systems for farmers. This study presents the design of a smart farming system based on low-cost IoT sensors and cloud-based data storage and analytics services. Additionally, a new data-mining method is proposed to predict production volume from heterogeneous data sources.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Xianyu Zhang, Xinguo Ming
Summary: With the development of industrial Internet environment and intelligent technology, enterprises are focusing more on system platform, information sharing, network collaboration, personalized customization and service recommendation in designing, implementing and operating Industrial Internet Platforms (IIP). However, there is a lack of a comprehensive framework for studying the high-level planning of IIP implementation and few studies on the detailed path and steps of IIP implementation in specific industries. The research aims to study the general model, reference architecture, service evaluation index system, implementation path and application verification for IIP to provide guidance for government and industry in planning, designing, implementing and promoting IIP.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Yuehua Liu, Wenjin Yu, Wenny Rahayu, Tharam Dillon
Summary: This article introduces the relationship between digital twins and IoT-based smart predictive maintenance (IoT-SPM) and proposes a reference IoT-SPM for the field. It conducts an analysis from multiple perspectives, including architecture, platforms, and components, to provide a comprehensive outlook on the IoT-SPM ecosystem. The article also focuses on the issues surrounding IoT data quality and discusses existing solutions, leading to open research issues and future directions.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Tania Cerquitelli, Daniele Jahier Pagliari, Andrea Calimera, Lorenzo Bottaccioli, Edoardo Patti, Andrea Acquaviva, Massimo Poncino
Summary: The article discusses the need for powerful software architectures and data-driven methodologies to extract valuable knowledge from the increasing amount of data generated in digital shop floor environments. It covers key functional and methodological aspects, as well as technologies and tools, to add intelligence to data-driven services in manufacturing environments. The deployment of these solutions in research project demonstrators shows their ability to reduce manufacturing line interruptions and associated costs.
PROCEEDINGS OF THE IEEE
(2021)
Article
Green & Sustainable Science & Technology
Rafael Gomes Alves, Rodrigo Filev Maia, Fabio Lima
Summary: This paper presents a digital twin model of a smart irrigation system, which utilizes an internet of things platform and a discrete event simulation model to enable automatic data flow and interaction. The system allows farmers to evaluate the behavior and test different irrigation strategies, leading to improvements in agricultural operations and water usage reduction.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Information Systems
Albert M. M. Villarreal III, Robin Kumar Verma, Oren Upton, Nicole Lang Beebe
Summary: The smart speaker with an AI-powered voice assistant is commonly found in modern households. Researchers have been exploring methods to extract data from these IoT devices, specifically in the case of Amazon Echo Dot. However, traditional methods alter the device or its data, which is undesired in digital forensics. This study focuses on developing a nondestructive methodology using CT scan imagery to extract data from IoT devices, exemplified by Amazon Echo Dot version 2 with eMMC/eMCP chips, using a 3-D fixture and an eMMC reader.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Gautam Srivastava, Jerry Chun-Wei Lin, Xuyun Zhang, Yuanfa Li
Summary: This paper presents a four-stage MapReduce framework based on the Spark platform for high-utility sequential pattern mining, which is shown to create a more efficient and faster mining performance for dealing with large data sets.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Ravesa Akhter, Shabir Ahmad Sofi
Summary: Modern agricultural science is becoming more accurate, data-driven, and powerful with the advancement of Internet of Things technology. The application of IoT data analytics and machine learning in agriculture brings new benefits and has a positive impact on increasing crop production and quality.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Liang Zhao, Fangyu Li, Maria Valero
Summary: The article presents a novel data analytics framework for edge computing using a decentralized algorithm, allowing all nodes to obtain global optimal model without sharing raw data. The local IoT nodes send calculated information to edge nodes, which cooperate with each other by exchanging analytics with their neighbors, demonstrating effective fast data analytics in the edge computing infrastructure.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Zuowei Zhang, Songtao Ye, Zechao Liu, Hao Wang, Weiping Ding
Summary: This paper proposes a new deep hyperspherical clustering (DHC) method for skin lesion medical image segmentation. It combines deep convolutional neural networks and the theory of belief functions to eliminate the dependence on labeled data and improve the segmentation performance. The DHC method can well characterize the imprecision caused by data uncertainty, making it particularly important for medical procedures. Experimental results on four dermoscopic benchmark datasets demonstrate that the proposed DHC yields better segmentation performance and can perceive imprecise regions compared to other typical methods.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Software Engineering
Aolin Che, Jing-Hua Yang, Cai Guo, Hong-Ning Dai, Haoran Xie, Ping Li
Summary: This article proposes a novel face protection approach using a generative adversarial network (GAN) and an autoencoder (AEGAN) to synthesize protection images. Extensive experiments have shown that this method can maintain comfortable visual quality and prevent recognition by commercial face recognition systems.
COMPUTER ANIMATION AND VIRTUAL WORLDS
(2023)
Article
Computer Science, Artificial Intelligence
Qiong Chen, Weiping Ding, Xiaomeng Huang, Hao Wang
Summary: This article introduces a feature discretization algorithm based on the generalized interval type-II fuzzy rough set. It calculates the primary grades of pixels to each ground object using the fuzzy mean vector and the fuzzy covariance matrix, and determines the secondary grades based on the distribution of pixels in the boundary region of the rough set. Then, it searches for the best discrete breakpoints in all bands of the remote sensing image using an adaptive genetic algorithm. Compared with current mainstream discretization algorithms, this method achieves better search efficiency, minimum number of discrete intervals, data consistency, and highest classification accuracy.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Yanping Wang, Xiaofen Wang, Hong-Ning Dai, Xiaosong Zhang, Muhammad Imran
Summary: Intelligent Transport Systems (ITS) have attracted attention due to advances in the Industrial Internet of Vehicles (IIoV). However, existing data reporting protocols for ITS have limitations in terms of storage, computation costs, and revocation of malicious users. This paper proposes a novel data reporting protocol for edge-assisted ITS that addresses these issues, achieving better performance and security.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Qian Wang, Cai Guo, Hong-Ning Dai, Min Xia
Summary: Intelligent transportation systems often encounter blurry images or videos, which is a challenge for practical applications due to resource limitations. This work proposes a variant-depth network (VDN) that addresses this challenge by employing variant-depth sub-networks and a stack connection, resulting in high deblurring quality with the shortest running time and smallest model size. The evaluation on various datasets demonstrates that VDN outperforms state-of-the-art image-deblurring methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Wang, Xiaoyang Suo, Xiangyu Wei, Bin Wang, Hao Wang, Hong-Ning Dai, Xiangliang Zhang
Summary: Graph Auto-Encoder is a framework for unsupervised learning on graph-structured data. However, it is not applicable for heterogeneous graphs that contain more abundant semantic information. Therefore, this work proposes a novel HGATE method for unsupervised representation learning on heterogeneous graph-structured data.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Jieyu Xie, Jiafu Wan
Summary: This paper introduces the key technology of digital twins in intelligent manufacturing and proposes a digital twin four-dimensional fusion modeling method to solve the application problems of digital twin technology in discrete manufacturing. The proposed method can describe the geometric and physical characteristics of a physical entity, map its behavior mechanism, and reveal the control logic and virtual-real mapping rules, providing important support for virtual-real intelligent mutual control.
BIG DATA AND COGNITIVE COMPUTING
(2023)
Article
Automation & Control Systems
Zhaolin Yuan, Yewan Wang, Xiaojuan Ban, Chunyu Ning, Hong-Ning Dai, Hao Wang
Summary: Periodic Jump processes in complex industrial systems pose challenges in learning their dynamics and accurate forecasting. This study proposes AJ-ODENet, a model consisting of H-ODENets and a stage transition predictor, to learn the continuous-time periodic jump system. The model can simulate the working patterns of a real cooling system with an error of less than 5% and optimize cooling energy consumption by 6%-25%.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Guoxia Xu, Hao Wang, Meng Zhao, Marius Pedersen, Hu Zhu
Summary: “The constraint based correlation filter has been widely used in UAV tracking. In this study, a distribution-based temporal knowledge driven method is proposed to address the issue of temporal degeneration in tracking. By learning parametric distribution and approximating optimal response reasoning with low-rank constraint, the proposed method achieves superior tracking performance compared to state-of-the-art algorithms.”
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Yalin Liu, Qiu Wang, Hong-Ning Dai, Yaru Fu, Ning Zhang, Chi Chung Lee
Summary: This paper investigates the uplink transmission performance in UAV-assisted wireless backhaul networks (UABNs). The connectivity of a two-hop uplink path from a reference UE to a remote GBS via a reference UAV is analyzed, considering the location variation of UAVs and the complexity of interference. Based on a theoretical model using stochastic geometry, the connectivity of the two-hop uplink path is derived by limiting the signal-to-noise-plus-interference (SINR) above a threshold. The theoretical values match with simulation results, confirming the accuracy of the proposed analytical model, and providing insights for constructing and configuring UABNs.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Dan Xia, Jianhua Shi, Ke Wan, Jiafu Wan, Miguel Martinez-Garcia, Xin Guan
Summary: This article proposes a DT-based system architecture and a mobile-enhanced edge computing-cloud collaborative mechanism for intelligent planning and deployment of 6G networks, aiming to improve network performance and reduce operational costs.
IEEE WIRELESS COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Xiangdong Wang, Xiaofeng Hu, Zijie Ren, Tianci Tian, Jiafu Wan
Summary: The digital twin workshop is a new workshop operation paradigm that combines virtual and physical space for precise decision-making. However, integrating models from different domains and updating parameters pose challenges. This paper proposes a knowledge graph (KG)-based multi-domain model integration method for digital twin workshops, which includes model elements, ontology, data, semantic integration, and network connection. The efficacy of the proposed method is demonstrated through scenarios in the subassembly workshop for hull construction.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Ligang Wu, Liang Zhang, Le Chen, Jianhua Shi, Jiafu Wan
Summary: This paper proposes a method based on a lightweight neural network and multisource information fusion for real-time monitoring of lump coal in the process of mining conveyor belt transportation. By performing image preprocessing, optimizing feature extraction, and fusing feature information, effective monitoring of lump coal is achieved.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Information Systems
Safia Amir Dahri, Muhammad Mujtaba Shaikh, Musaed Alhussein, Muhammad Afzal Soomro, Khursheed Aurangzeb, Muhammad Imran
Summary: The coverage and capacity required for 5G and beyond can be achieved using heterogeneous wireless networks. This study explores various factors such as distance, line of sight, idle mode capability, and path loss models to improve the performance of 5G networks. The installation of directional antennas at macro base stations and omnidirectional antennas at pico base stations significantly improves coverage and area spectral efficiency.
