Journal Information
Computers & Electrical Engineering
http://www.journals.elsevier.com/computers-and-electrical-engineering/
Impact Factor:
3.818
Publisher:
Elsevier
ISSN:
0045-7906
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Call For Papers
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.

Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.

Specific topics of interest include:

    Applications of high-performance computing and novel computing systems

    Internet-based, multimedia, and wireless networks and applications

    Communications, especially wireless

    Signal processing architectures, algorithms, and applications

    Green technologies in information, computing, and communication systems

    Multi-disciplinary areas, including robotics, embedded systems, and security
Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Edge computing with intelligent internet of things learning (VSI-iotl)
Submission Date: 2022-06-30

In recent years, intelligent computing methods have become feasible and promising tools to meet the challenges in the traditional ways of learning. This special section aims to develop the intelligent edge computing methods in the internet of things learning sciences, learning analytics, and networked learning, and educational evaluation and assessment. Potential readers include the research community, scientists, engineers, policy makers and operators of internet of things learning, as well as other related fields. Any topic related to advanced intelligent computing methods as well as their applications with internet of things learning will be considered. All aspects of design, theory and realization are of interest. Guest editors: Shiping Wen (Lead), University of Technology Sydney, Australia; Email: shiping.wen@uts.edu.au Zhong-kai Feng, Hohai University, China; Email: myfellow@163.com Guoguang Wen, Beijing Jiaotong University, China; Email: guoguang.wen@bjtu.edu.cn Jianying Xiao, Southwest Petroleum University, China; Email: shawion1980@yahoo.com Special issue information: Computers and Electrical Engineering Special Section on Edge computing with intelligent internet of things learning (VSI-iotl) Overview As an effective way to promote knowledge exchange and information sharing, learning has been playing an important role in the development and progress of human society for thousands of years. Various mediums, materials, tools, and methods have been provided for communication among researchers, engineers, practitioners, and policy makers. In recent years, the booming development of information technologies have made significant changes in the traditional ways of learning. For instance, communication is moving from offline to online; audiences are changing from school-aged students to people of all ages; and knowledge is moving from single discipline to interdisciplinarity. Hence, how to scientifically handle unprecedented changes in human history is becoming an increasingly popular research hotspot in the field of the internet of things. In recent years, intelligent computing methods have become feasible and promising tools to meet the challenges in the traditional ways of learning. They can connect a wide range of terminal computing nodes (like mobile phones, tablet computers, laptops or servers) to accomplish the goal of the high-computing and low-latency operating environment required in internet of things learning applications. However, there aren’t many reports on using advanced computer methods to address learning-associated problems. Edge computing is a feasible and promising technique to meet these challenges. It places a large number of computing nodes near the terminal device to meet the high computing and low latency requirements of deep learning applications. It also provides additional benefits in terms of bandwidth efficiency, privacy, and scalability. However, the edge computing system is much more resource-sensitive than the cloud structure, thus a more efficient deep network model is necessary. This special section aims to develop the intelligent edge computing methods in the internet of things learning sciences, learning analytics, and networked learning, and educational evaluation and assessment. Potential readers include the research community, scientists, engineers, policy makers and operators of internet of things learning, as well as other related fields. Any topic related to advanced intelligent computing methods as well as their applications with internet of things learning will be considered. All aspects of design, theory and realization are of interest. Topics: · Intelligent edge computing methods for internet of things learning · High-efficiency edge computing databases in internet of things learning · Interactive edge computing software in internet of things learning · Cloud platforms of computing in internet of things learning · Innovative learning systems via intelligent edge computing methods · Adaptative learning assessment via intelligent edge computing methods · Safe learning system design via intelligent edge computing methods · Learning risk analysis via intelligent edge computing methods · Big data in computer learning via intelligent edge computing methods · Efficient learning features extraction via intelligent edge computing methods Guest Editors: Shiping Wen (Lead), University of Technology Sydney, Australia; Email: shiping.wen@uts.edu.au Zhong-kai Feng, Hohai University, China; Email: myfellow@163.com Guoguang Wen, Beijing Jiaotong University, China; Email: guoguang.wen@bjtu.edu.cn Jianying Xiao, Southwest Petroleum University, China; Email: shawion1980@yahoo.com Shiping Wen is a Professor at the Australian Artificial Intelligence Institute, University of Technology Sydney, Australia. He received a M.Eng. degree in Control Science and Engineering from the School of Automation, Wuhan University of Technology, Wuhan, China, in 2010, and received a Ph.D degree in Control Science and Engineering from School of Automation, Huazhong University of Science and Technology, Wuhan, China, in 2013. His research interests include memristor-based neural networks, deep learning, computer vision, and their applications in medical informatics, et al. In 2018 and 2020, he was listed as a Clarivate Analytics Highly Cited Researcher in the Cross-Field, respectively. He received the 2017 Young Investigator Award of the Asian Pacific Neural Network Association and the 2015 Chinese Association of Artificial Intelligence Outstanding PhD Dissertation Award. He currently serves as an Associate Editor for Knowledge-Based Systems, IEEE Access, and Neural Processing Letters and has served as Leading Guest Editor of Special Issues in IEEE Transactions on Network Science and Engineering, Sustainable Cities and Society, Environmental Research Letters, et al. He has also served as a general/publication chair or a member of the Technical Programming Committee for various international conferences. He is also a Senior Member of IEEE. Zhong-kai Feng is a Professor at Hohai University, Nanjing, China. He received a Ph.D. degree in Hydraulic and Hydropower Engineering from Dalian University of Technology, Dalian, Liaoning, China, in 2016. His current research interests include renewable energy operation, hybrid power systems, machine learning, artificial intelligence, and decision support system development. He has received various Awards, including the first prize of the Ministry of Education’s Science and Technology Award, first prize of the Hydropower Science and Technology Award, Excellent doctoral thesis of Liaoning Province, Annual Excellent Paper of Journal of Hydraulic Engineering, F5000 Top academic paper, as well as Outstanding Reviewer of several Top international journals (like Applied Energy and Energy). Guoguang Wen is an Associate Professor with the Department of Mathematics, School of Science, Beijing Jiaotong University. His current research interests include intelligent control, cooperative control for multiagent systems, control of multirobots formation, control of fractional systems, nonlinear dynamics and control, and neural networks. He received a B.S. degree from the Department of Mathematical Science, Inner Mongolia University, Hohhot, China, in 2007, a M.S. degree from the Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, China, in 2009, and a Ph.D. degree from LAGIS, UMR 8219 CNRS, École Centrale de Lille, Villeneuve-d’Ascq, France, in 2012. Jianying Xiao is an Associate Professor with School of Science, Southwest Petroleum University. Her current research interests include quaternion-valued neural networks, memristor-based neural networks, and factional-order systems. Jianying Xiao received a B.S. degree in mathematics from China West Normal University, Nanchong, Sichuan, China, in 2003, a M.S. degree in mathematics from Southwest Petroleum University, Chengdu, Sichuan, China, in 2010, and a Ph.D. degree in mathematics from the University of Electronic Science and Technology of China, Chengdu, China, in 2018. Manuscript submission information: Submission Guidelines: New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this section, the authors agree to referee one paper (if asked) within the time frame of the special section. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-iotl” under the “Issues” tab. For additional questions, contact the Main Guest Editor. Schedule Submission of manuscript: June 30, 2022 First notification: August 15, 2022 Submission of revised manuscript: September 20, 2022 Notification of the re-review: October 20, 2022 Final notification: November 20, 2022 Publication: Feb. 2023 Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Last updated by Dou Sun in 2022-01-29
Special Issue on Visual Transformer for Image/Video Understanding (VSI-vti)
Submission Date: 2022-07-01

Visual understanding is a fundamental cognitive ability in human beings, which is essential for identifying objects/stuff in images and videos. This cognitive skill makes interaction with the environment extremely effortless. Image/video understanding attempts to mimic this human behavior. Recently, with the exponential growth of visual data, visual-based transformers begin to show their potential for image and video understanding, and exhibit their compatibility on a broad range of vision tasks. This special session will feature original researches and cutting-edge papers related to transformer-based models and algorithms for various image and video tasks, ranged from image classification, and downstream dense prediction tasks. Guest editors: Dr. Quan Zhou, Associate Professor, Ph.D. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, China (Managing Guest Editor) Email: quan.zhou@njupt.edu.cn https://yjs.njupt.edu.cn/dsgl/nocontrol/college/dsfcxq.htm?dsJbxxId=9B9D05C52C112DCFE050007F01006EFE Dr. Guangwei Gao Professor, Ph.D. National Institute of Informatics, Japan E-mail: csggao@gmail.com https://guangweigao.github.io Dr. Weihua Ou Professor, Ph.D. School of Big Data and Computer Science, Guizhou Normal University, China E-mail: ouweihuahust@gmail.com https://www.scholat.com/ouweihua Dr. Longin Jan Latecki Professor, Ph.D. Department of Information and Computer Science, Temple University, USA E-mail: latecki@temple.edu https://cis.temple.edu/~latecki/ Short Bio Dr. Quan Zhou received B.S. degree in electronics and information engineering from China University of Geosciences (Wuhan), China, 2002. He received M.S. and Ph.D. degree in from Huazhong University of Science and Technology, Wuhan, China in 2006 and 2013, respectively. His has been scholar visitor of Umea University, Sweden, in 2004, Kyushu Institute of Technology, Japan, in 2008, and Temple University, USA, in 2020. Now he is an associated professor in the college of Telecommunications and Information engineering at Nanjing University of Posts and Telecommunications. His research interests include computer vision and pattern recognition. He has published more than 70 academic papers on top journals and conferences, including IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, IEEE Transactions on Intelligence of Transportation Systems, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, and so on. He has served as guest editor for some SCI journals, such as Springer/ACM of Mobile Networks and Applications, Multimedia Tools and Applications, and Optical Laser Technology. He is member of IAPR and IEEE. Guangwei Gao received the Ph.D. degree in pattern recognition and intelligence systems from the Nanjing University of Science and Technology, Nanjing, in 2014. He was a visiting student with the Department of Computing, The Hong Kong Polytechnic University, in 2011 and 2013, respectively. He is currently a Project Researcher with the National Institute of Informatics, Tokyo, Japan. His research interests include pattern recognition, and computer vision. He has published 40 scientific papers in IEEE TIP, IEEE TCSVT, IEEE TITS, PR, AAAI, etc, and served as reviewer for journals and conferences including IEEE TMM, IEEE TCSVT, CVPR, ICCV, ECCV, AAAI, etc. He is member of IEEE. Dr. Weihua Ou received his M.S. degree from Southeast University, Nanjing China in 2006, and received his Ph.D. degree in Information and Communication Engineering from Huazhong University of Science and Technology, Wuhan, China in 2014. He worked as postdoc from 2016 to 2017 in University of Technology Sydney, Australia. Now, he served as full professor at School of Big Data and Computer Science, Guizhou Normal University, China. His research interests include computer vision and pattern recognition. He has published more than 60 academic papers on top journals and conferences, including IEEE Transactions on Multimedia, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, and so on. He has served as Program Committee Member of top conference, such as IJCAI, AAAI, CVPR. Prof. Longin Jan Latecki received the Ph.D. degree in computer science from Hamburg University, Germany, in 1992. He now serves as a full professor of department of computer science at Temple University, Philadelphia, USA. His main research interests include shape representation and similarity, object detection and recognition in images, robot perception, data mining, and digital geometry. He has published more than 150 academic papers on top journals and conferences. He won the 25th Annual Pattern Recognition Society Award and Olympus Award in 1998 and 2000, respectively. He is now the Associate Editors-in-Chief of Pattern Recognition and an editorial board member of Computer Vision and Image Understanding, and the International Journal of Mathematical Imaging. He is senior member of IEEE. Special issue information: Computers and Electrical Engineering Special Section on Visual Transformer for Image/Video Understanding (VSI-vti) Overview Visual understanding is a fundamental cognitive ability in human beings, which is essential for identifying objects/stuff in image and video data. This cognitive skill makes interaction with the environment extremely effortless and provides an evolutionary advantage to humans as a species. Image/Video Understanding is the area of research, which attempts to mimic this human behavior. Recently, with the exponential growth of visual data and rapid increase of computational capability of hardware, visual-based transformers begin to show their potential for Image/Video Understanding, and exhibit their compatibility on a broad range of vision tasks. Although remarkable progress has been achieved, it has also brought new challenges, such as the heavy computational cost of transformer backbones, and the limitation of global contextual representation in multi-head self-attention. Furthermore, the debate between convolutional neural networks (CNNs) and transformers still remains in the community of computer vision. The aim of this special section is to feature original researches and cutting-edge scientific papers related to transformer-based models and algorithms for various image and video tasks, ranged from image/video classification, and downstream dense prediction tasks, such as object detection and semantic segmentation, together with widespread applications to real-world issues. Topics We solicit submissions of special session addressing the topics listed below using visual transformers for image/video understanding, including: l Pure Transformer for Visual Understanding l CNN and Transformer Hybrids for Visual Understanding l Lightweight Visual Transformers for Visual Understanding l Interpretability of Visual Transformers for Visual Understanding l Real-world Applications of Visual Transformers l Overview, Dataset, and Evaluation of Recent Study in Transformers for Image/Video Understanding Manuscript submission information: New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this section, the authors agree to referee one paper (if asked) within the time frame of the special section. Before submission, authors should carefully read the Guide for Authors available at: https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-vti” under the “Issues” tab. For any additional questions, please do to hesitate to contact with the Main Guest Editor. Submission Schedule Submission begins: March 1, 2022 Submission deadline: July 1, 2022 First notification: May 15, 2022 Submission of revised manuscript: July 30, 2022 Notification of the re-review: August 30, 2022 Final notification: September 15, 2022 Final paper due: September 30, 2022 Publication: December 30, 2022 Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Last updated by Dou Sun in 2022-01-29
Special Issue on Computer Vision in Smart Agriculture and Crop Surveillance (VSI-sacs)
Submission Date: 2022-07-30

Smart agriculture is an evolving approach due to the demands of the society and importance of sufficient food supply. Through the use of information technology, smart agriculture can aid farmers in remote areas. Agriculture automation with existing innovation aids to achieve the advantages like low cost, high efficiency, and high precision which in turn lead to sustainable improvement. Guest editors: List of Guest Editors: Dr. Antonio Zuorro (Managing guest editor), Professor Department of Chemical Materials, Materials and Environment, Sapienza University of Rome, Italy E-mail: antonio.zuorro@uniroma1.it;drantonio@antoniozuorro.org, Google Scholar: https://scholar.google.co.in/citations?user=F8oFgtAAAAAJ&hl=en https://sites.google.com/uniroma1.it/greenspirit/contacts Prof. Dr. Gniewko Niedbała, Professor Faculty of Environmental and Mechanical Engineering, Department of Biosystem Engineering, Laboratory of Energy Systems Engineering, Poznań University of Life Sciences, Poland Email: gniewko.niedbala@up.poznan.pl Website: http://www.up.poznan.pl/~gniewko/ Dr Marco Medici Department of Agricultural and Food Sciences, University of Bologna m.medici@unibo.it https://scholar.google.com/citations?hl=it&user=JJ2HZJoAAAAJ&view_op=list_works&sortby=pubdate Short Biography Dr. Antonio Zuorro Antonio Zuorro is Professor of Chemical Engineering Fundamentals at the Department of Chemical Engineering, Materials and Environment of Sapienza University of Rome, from which he received his M.S. and Ph.D. degrees in Chemical Engineering. His main research activity is focused on the recovery of value-added compounds from a variety of biological sources, especially agro-industrial wastes, by innovative and mild extraction processes. The developed and patented technologies are based on the use of specifically designed enzyme mixtures and/or mixed-polarity green solvents as pretreatment or extracting agents. These technologies have been successfully applied to the recovery of lycopene from tomato processing waste and antioxidant phenolics from spent coffee grounds, olive pomace, fruit peels, corn husks and brewers’ spent grain. Recently, the developed enzymatic processes have been used to recover lipids and other valuable intracellular compounds from microalgae. An important part of the research is devoted to the analysis and validation ofprocess solutions allowing an integral valorization of the waste materials within a sustainable circular-economy approach. Most of the above-mentioned activities are carried out in collaboration with research groups of other universities or institutions from different countries. Antonio Zuorro has been visiting professor at important foreign universities and he hold invited lectures and presentations at several international conferences. He has been visiting professor at important foreign universities worldwide and he hold invited lectures and presentations at several international conferences. He has also been the Scientific Responsible of many joint projects with foreign universities and industrial companies, even organizing and Coordinating the collaborative European project “EXCornsEED”, granted in HORIZON 2020 – BBI JU (call 2017). His scientific activity is attested by over 120 scientific publications in the chemical and biochemical engineering sector and five industrial patents. He received several awards and prizes in competitions sponsored by scientific and industrial associations. Prof. Dr. Gniewko Niedbała Scientific researcher in academic level and associate professor in agriculture, applied informatics and machine learning. Professional activity: 2012-2016 Member of the Board of the National Centre for Research and Development, Poland. Research activity started in 2006 resulted in the publication of specialized books as author and / or co-author (published abroad), over 100 scientific articles (of which 40 in foreign journals / conferences). Associate member of networks, research institutions and scientific journals. His research interests include Application of artificial neural networks in agricultura, Forecasting of crops yields, Modeling of agricultural processes, Application of Data Mining, Modeling the growth of trees occurring in Poland. Dr Marco Medici Marco Medici, Eng.PhD, is currently a Postdoctoral Research Associate at University of Bologna, leading a research and extension precision agriculture and food value chain program. He gained an M.S. in Management Engineering from the University of Bologna in 2011, and a PhD in Industrial Engineering from the University of Parma in 2016. Since 2010he has been interested in sustainability issues. In his early career he performed research on optimization problems in the field of engineering physics and renewable energy systems. His present research interests include the socio-environmental performance of food systems, the Internet of Food and agricultural machine data traceability methods. He has several scientific publications in various fields. Special issue information: Overview Smart agriculture is an evolving approach due to the demands of the society and importance of sufficient food supply. Through the use of information technology, smart agriculture can aid farmers in remote areas. The computer vision (CV) technology is significant in agricultural automation systems and involves an important role in its development. Agriculture automation with existing innovation aids to achieve the advantages like low cost, high efficiency, and high precision which in turn lead to sustainable improvement. However, there are significant difficulties that persevere, computer vision technology with other intelligent technologies like deep learning can be used for agricultural production management concerning large-scale datasets for settling ongoing agricultural issues, and to improve the financial, general, and performance of agricultural automation systems for propelling it more intelligently. The aerial imagery is generally used to monitor crops at the time of the growing season. The high-throughput phenotypic analysis is expected to give great proportions of significant yield qualities and achieve efficient crop management decisions. The crop field surveillance with modern computer vision technology is capable of totally automating the security in the field. In agriculture, computer vision technology evolved rapidly due to its automation and detection abilities. The computer vision and machine learning algorithms support farmers efficiently to distinguish soil richness, natural treatments, and pest controls. It can also determine impure food products and faults in crop yield through color, shape, size, and surface texture. The computer vision-artificial intelligence (AI) models can be optimally used for the health detection and monitoring of plants. Besides, the AI aids to promote seasonal forecast models for propelling accuracy, productivity, and reducing production time. Hence, advances in computer vision, artificial intelligence, machine learning will ultimately develop remote sensing technology for detecting and managing plants, weeds, disease, pests and to secure the future of sustainable agriculture and farming. The aim of this special section is to bring advances in smart agriculture and crop surveillance with aid of computer vision technology and other evolving technologies for the well-being of the society. Topics: Modern computer vision and machine learning techniques to measure and improve crop yield Intelligent technologies in seeding methods for precise fertilization Advanced genetic algorithms for plant leaf disease detection Deep learning-based object detection models with computer vision algorithms Challenges and prospects of AI for efficient analyzes and decision-making in smart agriculture Optimized computer-vision algorithms for plant phenotyping AI-powered solutions, cloud computing, advanced analytics, and satellite imagery applications in smart farming Computer vision technology-based automated security and surveillance applications AI-enabled sensor and IoT in remote sensing for sustainable agriculture UAV applications in smart and precision agriculture Real-time monitoring of crop growth with CV technology and IoT Manuscript submission information: Submission Guidelines New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special section. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-sacs” under the “Issues” tab. For additional questions, contact the Main Guest Editor. Important dates: Submission deadline: July. 30, 2022 First notification: October 30, 2022 Submission of revised manuscript: November 30, 2022 Notification of the re-review: December 30, 2022 Final notification: January 30, 2023 Final paper due: February 28, 2023 Publication: June 2023 Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Last updated by Dou Sun in 2022-01-29
Special Issue on AI-Based Dynamic Access Control for the Internet of Vehicles (IoV) (VSI-diov)
Submission Date: 2022-12-15

The Internet of Vehicles is a multi-faceted system. A key area is a secure connectivity with hundreds or thousands of vehicles in an emergency. Dynamic Access Control provides a mechanism to allocate and prioritize emergency access requests, allowing the internet of vehicles to be secure while assisting emergency responders. It works with any automatic vehicle identification technology that conforms to the IEEE standards. The design leverages machine learning, AI, and its hyper-scale infrastructure for massive-scale problem-solving. Major categories of data access control techniques are attribute-based, role-based, location-based, static, and non-contextual access control policies. Guest editors: Dr. Celestine Iwendi (Lead Guest Editor) Associate Professor (Senior Lecturer), University of Bolton, United Kingdom.Email: drciwendi@gmail.com, c.iwendi@bolton.ac.uk, celestine.iwendi@ieee.org, celestine.iwendi@ieee.org GS: https://scholar.google.se/citations?hl=en&user=Ihq53wsAAAAJ Dr. Celestine Iwendi (Managing Guest Editor) is currently working at University of Bolton, United Kingdom. He received the second master s degree in communication hardware and microsystem engineering from Uppsala University, Sweden, ranked under 100 in the world University ranking, and the Ph.D. degree in electronics from the University of Aberdeen, U.K., in 2013. He has a strong teaching emphasis on communication, hands-on experience, willing-to-learn, and 19 years of technical expertise, and teaches Engineering team Project, Artificial Intelligence, Machine Learning, Data Networks, Electronics, Cybersecurity, Distributed Systems, and Control Systems. He has developed operational, maintenance, and testing procedures for electronic products, components, equipment, and systems, provided technical support and instruction to staff and customers. His research interests include wireless sensor networks, cybersecurity, security of things (SoT), machine learning, AI, communication controls, the Internet of Things (IoT), electromagnetic machines, 5G networks, and low power communication protocols. Dr. Joseph Henry Arinze Anajemba Dept of Internet of Things, Hohai University China. Email: henryjaa@hhu.edu.cn GS: https://scholar.google.com/citations?user=t-ZLHXkAAAAJ&hl=en&oi=sra Dr. Joseph Henry Arinze Anajemba (Co-Guest Editor) received his master s degree in information technology from the National Open University of Nigeria (NOUN), in 2016. He is also obtained a degree in computer science from the Federal Polytechnic Oko, Nigeria. Recently, in June 2021, he completed and obtained a Ph.D. degree in information and communication engineering with the College of Internet of Things, Department of Communication engineering, Hohai University, China. His research interests include, intelligent networks and computing, artificial intelligence, cellular wireless communications, antenna and V2V technology, 5G/6G cellular networks and security, and several other IoT related areas. Dr. Abdul Rehman Javed Lecturer, Department of Cyber Security, Air University, Islamabad, Pakistan Email: abdulrehman.cs@au.edu.pk GS: https://scholar.google.com.pk/citations?user=UFBVq7kAAAAJDr. Abdul Rehman Javed (Co-Guest Editor) is a lecturer at the Department of Cyber Security, Air University, Pakistan. He has worked with National Cybercrimes and Forensics Laboratory, Air University, Pakistan. He has received his Master's degree in Computer Science from the National University of Computer and Emerging Sciences, Pakistan. He has reviewed over 60 scientific research articles for various well-known journals including, IEEE Internet of Things Magazine, Transactions on Internet Technology (ACM), Telecommunication Systems (Springer). His current research interests include but are not limited to mobile and ubiquitous computing, data analysis, knowledge discovery, data mining, natural language processing, smart homes, and their applications in human activity analysis, human motion analysis, and e-health. He aims to contribute to interdisciplinary research of computer science and human-related disciplines. Special issue information: Computers and Electrical Engineering Special Section on AI-Based Dynamic Access Control for the Internet of Vehicles (IoV) (VSI-diov) Overview: The Internet of Vehicles (IoV) has been growing exponentially over the years, and a large number of vehicles are now connected to the network. There are huge security concerns as the number of endpoints increases, with no centralized management and control. Dynamic Access Control is a solution to this issue and allows secure access while still monitoring the vehicle's whereabouts. In an IoV environment, there is communication between vehicles and the environment, vehicle and vehicle, vehicle and pedestrians, vehicle and cloud, among intra-vehicle systems. The IoV architecture commonly has five layers such as endpoint layer, infrastructure layer, operation layer, service layer, and user vehicle interface layer. IoV provides wide scope for future vehicular technology such as smart parking, route planning, intelligent traffic signalling, autonomous driving. A few of the challenges in IoV during its implementation include trust management, security, privacy issues, complex routing algorithms, protocol validation, adaptability, dynamicity. IoV faces various security challenges such as identification attacks, confidentiality attacks, data authenticity attacks, authentication attacks, GPS deception, masquerading, denial of service, and eavesdropping. Of which, the notable issue is access control. The Internet of Vehicles is a multi-faceted system. A key area is a secure connectivity with hundreds or thousands of vehicles in an emergency. Dynamic Access Control provides a mechanism to allocate and prioritize emergency access requests, allowing the internet of vehicles to be secure while assisting emergency responders. It works with any automatic vehicle identification technology that conforms to the IEEE standards. The design leverages machine learning, AI, and its hyper-scale infrastructure for massive-scale problem-solving. Major categories of data access control techniques are attribute-based, role-based, location-based, static, and non-contextual access control policies. Location-based access control is highly needed for IoV. The access control systems should satisfy the major parameters associated with security, such as confidentiality, integrity, and availability. Some of the drawbacks of conventional access control techniques are a failure in centralized management, non-adaptable security policies, and lack of dynamicity. Hence a transformation is required towards artificial intelligence (AI) based dynamic control policies. AI methods include the use of machine learning and deep learning techniques. The categories of machine learning techniques are supervised, unsupervised, and reinforcement learning. A few deep learning techniques include convolutional neural networks, recurrent neural networks, etc. Blockchain-based dynamic access control policies are an innovation in this field. It provides dynamic and distributed access control for the IoV application. The objective of this special issue is to explore a novel dynamic access control model for IoV using artificial intelligence (AI) techniques. It is more trivial that AI techniques provide more robust authentication and authorization mechanisms in IoV than traditional models. This special issue further explores the security evaluation and the design principles and characteristics of new methods, focusing on their feasibility and applicability. List of topics: AI-based dynamic access control policies for Internet of Vehicles Blockchain for trusted and reliable Internet of Vehicles Trust based dynamic access control architectures for Internet of Vehicles Trust-driven privacy framework on Internet of Vehicles Access control policies to prevent cyberthreats in Internet of Vehicles AI-based cyber-attacks prediction model and dynamic access control for Internet of Vehicles AI-based dynamic access control policies for Internet of Vehicles Light-weight authentication protocols for Internet of Vehicles Intelligent access control techniques for autonomous connected cars Enabling intelligence embedded fine-grained access control models for smart vehicular communications Privacy-aware and fuzzy-extended role-based access control technique for smart transportation Deep learning for dynamic access control in internet of vehicles Manuscript submission information: Submission Guidelines: New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting VSI-diov under the Issues tab. For additional questions, contact the Main Guest Editor. Schedule: Submission deadline: December. 15, 2022 First notification: May 03, 2023 Submission of revised manuscript: June 05, 2023 Notification of the re-review: July 28, 2023 Final notification: September 01, 2023 Final paper due: October 31, 2023 Publication: January 2024 Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Last updated by Dou Sun in 2022-01-29
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