Article
Engineering, Industrial
Moritz von Stietencron, Karl Hribernik, Katerina Lepenioti, Alexandros Bousdekis, Marco Lewandowski, Dimitris Apostolou, Gregoris Mentzas
Summary: Logistics 4.0 focuses on sustainable customer satisfaction and cost optimization using emerging technologies like IoT and streaming analytics. This paper introduces a software framework for streaming analytics in an edge-cloud environment to advance Logistics 4.0, with a specific application and evaluation in the aerospace industry.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Xiaokang Wang, Lei Ren, Ruixue Yuan, Laurence T. Yang, M. Jamal Deen
Summary: In this article, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented as an approach for efficiently processing CPSS big data. By decomposing the multi-attributes CPSS big data into the QTT form, and utilizing a distributed cloud-edge computing model, the proposed method effectively improves training efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Telecommunications
Dan Wang, Bin Song, Yingjie Liu, Mingjun Wang
Summary: This paper aims to minimize system latency, considering security and reliability requirements, by proposing a distributed blockchain-assisted CPIoTS and an efficient resource allocation algorithm PPO-SRRA for edge-cloud computing coupled with CPS.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Information Systems
Ning Zhang, Chenfei Zhang, Dengpan Wu
Summary: This article uses cloud computing, big data, mobile Internet technologies to build a physical health smart management system, which can reduce operating costs, improve work efficiency, and provide new ideas and methods for physical fitness assessment without being affected by individual assessment methods and results.
COMPUTER COMMUNICATIONS
(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
Green & Sustainable Science & Technology
Mihai Andronie, George Lazaroiu, Mariana Iatagan, Iulian Hurloiu, Irina Dijmarescu
Summary: This article reviews previous research indicating that cyber-physical production systems shape social sustainability performance technologically, contributing to the literature on sustainable smart manufacturing. Through a quantitative literature review, the study identified key components of data-driven sustainable smart manufacturing and emphasized the importance of Industry 4.0-based technologies in ensuring the sustainability of production systems. Future research should focus on exploring the use of Internet of Things sensing networks and deep learning-assisted smart process planning in achieving sustainability in manufacturing.
Article
Chemistry, Analytical
Giuseppe Loseto, Floriano Scioscia, Michele Ruta, Filippo Gramegna, Saverio Ieva, Corrado Fasciano, Ivano Bilenchi, Davide Loconte
Summary: This paper proposes a Cloud-Edge AI microservice architecture based on Osmotic Computing principles. It enables flexible training and inference, as well as direct mapping with Commercial-Off-The-Shelf (COTS) components. The feasibility and benefits of the approach are validated through experiments in a small-scale intelligent manufacturing case study.
Article
Engineering, Multidisciplinary
Yaliang Zhao, Laurence T. Yang, Yiwen Zhang, Jiayu Sun, Xiaojing Wang, Chunchun Zhang, Guangming Zhang
Summary: The paper discusses the importance of multiple clusterings in discovering different data patterns, introduces a tensor-based multiple clustering method, and studies the tensor train-based multiple clustering and its parallel computing method to improve efficiency and accuracy. Experiment results show that this approach can significantly enhance computation efficiency and clustering accuracy while reducing memory usage compared to the original method.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Zahra Pooranian, Mohammad Shojafar, Sahil Garg, Rahim Taheri, Rahim Tafazolli
Summary: The article introduces the issues of data deduplication and encryption in Cloud envisioned cyber--physical systems (CCPS), and proposes a new encryption protocol LEVER to address this, which has high performance and practicality.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Review
Engineering, Industrial
Diego G. S. Pivoto, Luiz F. F. de Almeida, Rodrigo da Rosa Righi, Joel J. P. C. Rodrigues, Alexandre Baratella Lugli, Antonio M. Alberti
Summary: The industrial sector is experiencing rapid changes due to advancements in technology and increasing demand, leading to a growing number of devices and systems in the industry's architectures. CPS and IIoT are seen as pivotal in Industry 4.0, with researchers and experts working to develop systems and architectures that can connect devices from different ICT systems, virtualize company assets, and integrate with other manufacturing sectors. This article surveys CPS architecture models, highlighting key characteristics and technologies, as well as exploring current projects and technologies in the CPS and IIoT field for vertical and horizontal industrial integration in the I4.0 context.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Jun Feng, Laurence T. Yang, Xin Nie, Nicholaus J. Gati
Summary: This article proposes a novel edge-cloud-aided differentially private tucker decomposition scheme to protect private data of data owners in CPSS. The scheme achieves efficient tensor factorization while preserving privacy through perturbation and local resolution.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Green & Sustainable Science & Technology
Souvik Pal, N. Z. Jhanjhi, Azmi Shawkat Abdulbaqi, D. Akila, Faisal S. Alsubaei, Abdulaleem Ali Almazroi
Summary: One of the most significant issues in IoT cloud computing is task scheduling. The rise in IoT technologies has led to a high demand for cloud storage and efficient planning methods are required to load IoT services onto cloud resources. Fog cloud computing is proposed to meet the growing demand for quick and reliable access to information, and efficient task scheduling plays a crucial role in reducing processing time and improving quality of service. This research introduces a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) that outperforms other well-known task allocation methods, achieving an 8.43% improvement in outcomes.
Article
Computer Science, Information Systems
Xin Wang, Pei Guo, Xingyan Li, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman, Jianwu Wang
Summary: This article introduces the use of serverless computing and containerization techniques to address the challenges of reproducing batch based Big Data analytics in the cloud. It also presents the development of an open-source toolkit for automated execution and reproducibility. Experiments on AWS and Azure demonstrate that the toolkit achieves good performance, scalability, and efficient reproducibility for cloud-based Big Data analytics.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Christos L. Stergiou, Elisavet Bompoli, Konstantinos E. Psannis
Summary: Due to its unique services, Cloud Computing attracts researchers to develop sustainable systems. It offers users the opportunity to access and manage information, applications, and data anytime, anywhere. Big Data, a service that includes large amounts of data produced by the Internet of Things, is also discussed in this work.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Fadi AL-Turjman, B. D. Deebak
Summary: Technological advancements have enabled the connectivity of Internet devices for data observation and measurement of physical entities, with a focus on transforming raw data into smart data for better decision-making processes and privacy protection. This transformation plays a crucial role in improving user experience and device access.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Nidal Nasser, Qazi Emad-ul-Haq, Muhammad Imran, Asmaa Ali, Imran Razzak, Abdulaziz Al-Helali
Summary: This study proposes an intelligent healthcare system based on IoT-cloud technologies for real-time patient tracking and reliable COVID-19 detection. By utilizing deep learning algorithms and state-of-the-art classification techniques, the system achieves high accuracy and effectiveness in processing CT scan images.
NEURAL COMPUTING & APPLICATIONS
(2023)
Review
Computer Science, Information Systems
Muhammad Shafay, Raja Wasim Ahmad, Khaled Salah, Ibrar Yaqoob, Raja Jayaraman, Mohammed Omar
Summary: This paper explores the importance of integrating blockchain technology with deep learning and reviews existing literature on this topic. By devising a thematic taxonomy and discussing and comparing existing frameworks, this paper provides insights into the important research challenges in developing deep learning frameworks.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zainab Ayaz, Saeeda Naz, Naila Habib Khan, Imran Razzak, Muhammad Imran
Summary: The recent advancements in information technology and bioinformatics have significantly contributed to medical sciences. Artificial intelligence has been widely utilized in the diagnosis of Parkinson's disease, with promising results achieved using different datasets and techniques.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Zijie Ren, Jianhua Shi, Muhammad Imran
Summary: Product lifecycle management is an effective method for enhancing the market competitiveness of modern manufacturing industries. The digital twin, with its integration of physics and information systems, provides a technical means to integrate multisource information and overcome communication barriers in the lifecycle. However, there is a lack of focus on twin data and its evolution mechanisms, limiting the full potential of digital twin technology in global data resource management.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Muhammad Nouman, Umar Qasim, Hina Nasir, Abdullah Almasoud, Muhammad Imran, Nadeem Javaid
Summary: In this study, blockchain is used to register nodes and address security issues at Base Stations and Cluster Heads. A Machine Learning classifier called HGB is used to classify nodes as malicious or legitimate. Malicious nodes are revoked from the network, while legitimate nodes have their data stored in IPFS with their hashes stored in blockchain. Performance evaluation shows that HGB outperforms other classifiers and VBFT performs better than PoW. Furthermore, the proposed model efficiently detects malicious nodes and ensures secure data storage.
Article
Computer Science, Information Systems
Raja Wasim Ahmad, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob, Samer Ellahham, Mohammed Omar
Summary: The emergence of COVID-19 in 2020 has had a global impact on the economy, health, and human lives. Existing healthcare systems have shown limitations in handling public health emergencies efficiently. Blockchain technology can assist in combating the pandemic by ensuring safe medical supplies, accurate identification of virus hotspots, and establishing data provenance for verifying the authenticity of personal protective equipment.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(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
Computer Science, Theory & Methods
Haya R. Hasan, Mohammad Madine, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman, Dragan Boscovic
Summary: A digital twin is a precise digital replica of a physical object or asset that helps manage, control, or monitor the real object. The current centralized systems for managing digital twins are not easily accessible. This paper proposes using non-fungible tokens (NFTs) to manage ownership of digital twins and provide proof of delivery of their physical assets in a decentralized, secure, traceable, and transparent manner. The solution involves representing digital twins and their sub-twins hierarchically using NFTs and sub-NFTs, and includes system architecture, algorithm implementation, smart contracts, testing, validation, security analysis, and generalization.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Roopdeep Kaur, Gour Karmakar, Muhammad Imran
Summary: This paper investigates the importance of denoising in digital image processing and compares the performance of traditional and embedded denoising methods. The experimental results show that traditional denoising methods have better accuracy, while embedded denoising methods have lower computational time.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Sajid Ali, Omar Abusabha, Farman Ali, Muhammad Imran, Tamer Abuhmed
Summary: Despite the increasing threat of IoT-specific malware, assessing IoT systems' security and developing mitigation measures are critical. This study proposes a multitask DL model using LSTM for detecting IoT malware, achieving high accuracy in tasks of determining benign/malicious traffic and identifying malware types. Traffic data from 18 IoT devices were used for training and feature selection enhanced the model's performance.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2023)
Article
Mathematics
Iftikhar Ahmad, Muhammad Imran, Abdul Qayyum, Muhammad Sher Ramzan, Madini O. Alassafi
Summary: This research proposes a new hybrid deep learning intrusion detection model called HD-IDM, which combines GRU and LSTM classifiers for analyzing network traffic. HD-IDM achieves remarkable performance on multiple datasets, with outstanding accuracy and precision for classification tasks. However, it has limitations such as the need for labeled data and potential challenges in handling new intrusion methods.
Review
Computer Science, Information Systems
Ibrar Yaqoob, Khaled Salah, Raja Jayaraman, Mohammed Omar
Summary: In recent years, there has been a global trend towards the metaverse, which consists of immersive and interconnected digital spaces where users can interact through computer-generated environments. This paper discusses how leveraging the metaverse can revolutionize and reshape smart cities by stimulating innovations and bringing about significant improvements. It explores the key enabling technologies, benefits, and opportunities of implementing the metaverse in smart city applications, along with ongoing projects and case studies. The paper also highlights critical research challenges and outlines future directions for the development and integration of the metaverse with smart cities.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Ammar Battah, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob, Ashraf Khalil
Summary: Ranking systems are important for improving education quality and academic institution reputation. However, current systems lack transparency, traceability, and fairness, as they are managed centrally and rely on subjective indicators. This paper proposes a blockchain-based solution that provides transparent, traceable, and decentralized academic ranking systems. Smart contracts are developed to govern interactions and decentralized storage, oracles, and threshold encryption ensure secure data retrieval and sharing. The solution is evaluated based on cost, throughput, latency, and security, and all smart contract codes are publicly available on GitHub.
Article
Computer Science, Information Systems
Sajjad Khan, Jorao Gomes, Muhammad Habib ur ur Rehman, Davor Svetinovic
Summary: This paper presents a decentralized federated learning architecture that detects and eliminates participants with adaptive behavior by evaluating the quality of gradients. Experimental results show that the proposed protocol can effectively detect and eliminate participants with adaptive behavior, while centralized federated learning fails to do so.
INTERNET OF THINGS
(2023)
Article
Information Science & Library Science
Muhammad Javed Ramzan, Saif Ur Rehman Khan, Inayat Ur-Rehman, Muhammad Habib Ur Rehman, Ehab Nabiel Al-khannaq
Summary: This study aims to guide transmuters in becoming data scientists by exploring the challenges faced by data scientists according to their educational backgrounds. The findings reveal significant variability in skill requirements and tool usage based on educational background, but regardless of academic background, data scientists spend more time analyzing data than operationalizing insight. The study provides suggestions for universities and online academies to recommend required knowledge for prospective students based on their educational background.
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
