Article
Computer Science, Information Systems
Huifeng Wu, Yi Yan, Baiping Chen, Feng Hou, Danfeng Sun
Summary: This article introduces an ontology and a three-layer cloud-fog-edge architecture (FADA) for large and complex machines to address issues such as data quality and data extraction. Experimental results prove the feasibility and performance advantages of FADA in different scenarios.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Ali Akbar Sadri, Amir Masoud Rahmani, Morteza Saberikamarposhti, Mehdi Hosseinzadeh
Summary: This paper highlights the importance of cloud computing and fog computing in the Internet of Things, the critical role of data management, and the essential techniques for data size reduction in fog computing. The study focuses on classifying and analyzing FDR studies from 2016 to 2022, presenting relevant topics and methods, and identifying open issues and challenges for future research.
INTERNET OF THINGS
(2022)
Article
Computer Science, Information Systems
Carlo Puliafito, Carlo Vallati, Enzo Mingozzi, Giovanni Merlino, Francesco Longo
Summary: The integration of Internet of Things (IoT) and fog computing opens up a plethora of applications, with low latency being a key advantage brought by fog computing. However, IoT device mobility can jeopardize this advantage, and migrating fog services to support device mobility can help maintain low latency. Results from experiments show promising average round-trip latency between mobile devices and fog layer, with minimal occurrences of latency exceeding application limits.
PERVASIVE AND MOBILE COMPUTING
(2021)
Article
Computer Science, Information Systems
Kenneth Li-Minn Ang, Jasmine Kah Phooi Seng
Summary: This article provides a comprehensive survey and review of embedded intelligence research in the field of smart cities, covering enabling technologies, applications, and challenges. The aim is to offer useful insights to researchers and inspire the development of practical EI solutions for smart cities.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Mathematics
Tariq Ahamed Ahanger, Fadl Dahan, Usman Tariq, Imdad Ullah
Summary: IoT-Edge-Fog Computing proposes a decentralized computing model for time-sensitive tasks. However, task allocation among dispersed Edge Computing nodes remains a challenge with existing techniques. This study presents a Quantum Computing-inspired optimization technique for efficient task allocation in an Edge Computing environment and employs a QC-Neural Network Model for predicting optimal computing nodes. Simulations with 6, 10, 14, and 20 Edge nodes were conducted, showing a 5.02% improvement in prediction efficiency and a 2.03% error reduction compared to state-of-the-art techniques.
Review
Computer Science, Interdisciplinary Applications
Rohit Kumar, Neha Agrawal
Summary: Cloud computing is transforming traditional computing methods through various forms and architectural types, such as Edge and Fog computing. These extensions of the basic cloud computing model promise improved network performance. Industrial applications rely on cloud resources to process a large volume of power-sensitive Industrial IoT (IIoT) data, which requires careful analysis to enhance system performance. This paper explores the Edge-Fog-Cloud architectural frameworks, compares their advantages and disadvantages, and delves into the scientific side of multi-dimensional IIoT data. It also highlights the current state-of-the-art and implementation challenges.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Review
Computer Science, Hardware & Architecture
Cristiano Antonio de Souza, Carlos Becker Westphall, Renato Bobsin Machado, Leandro Loffi, Carla Merkle Westphall, Guilherme Arthur Geronimo
Summary: The Internet of Things and fog computing play important roles in smart environments, but security is a major challenge. Therefore, research on intrusion detection and prevention is necessary. This article conducts a systematic literature review to evaluate existing technologies and propose possible directions for future research.
Article
Computer Science, Theory & Methods
Shivananda R. Poojara, Chinmaya Kumar Dehury, Pelle Jakovits, Satish Narayana Srirama
Summary: With the growth of IoT devices, the need for efficient data processing and analytics is increasing. This study explores the benefits of using Serverless data pipelines for IoT data analytics and evaluates different approaches for designing such pipelines. The results reveal the suitability of different design methods for different types of applications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Chemistry, Analytical
Chenbin Huang, Hui Wang, Lingguo Zeng, Ting Li
Summary: Delay-sensitive tasks are becoming more prominent in the Internet of Things (IoT), and finding ways to complete these tasks with minimum energy consumption has become a popular research topic. In this study, we propose a heuristic particle swarm optimization algorithm (LPSO) based on a Lyapunov framework, which effectively balances the energy consumption of IoT nodes, transmission, and fog node computing, leading to improved task completion efficiency compared to original PSO and greedy algorithms.
Article
Computer Science, Hardware & Architecture
Cristiano Antonio de Souza, Carlos Becker Westphall, Renato Bobsin Machado
Summary: Due to resource limitations in Internet of Things devices, security is often overlooked. This study proposes a two-step approach for intrusion detection and identification, which includes traffic analysis and ensemble methods. The proposed approach is evaluated on multiple intrusion datasets, demonstrating its robustness.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Review
Chemistry, Analytical
Nancy A. Angel, Dakshanamoorthy Ravindran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Yuh-Chung Hu
Summary: Cloud computing is important due to the expanding IoT network, but it has limitations when it comes to processing vast data from novel IoT applications. The recent trend is to move computational and storage resources to the network edge to overcome these limitations and optimize computing applications and services.
Article
Chemistry, Analytical
Xavi Masip-Bruin, Eva Marin-Tordera, Sergi Sanchez-Lopez, Jordi Garcia, Admela Jukan, Ana Juan Ferrer, Anna Queralt, Antonio Salis, Andrea Bartoli, Matija Cankar, Cristovao Cordeiro, Jens Jensen, John Kennedy
Summary: The cloud continuum, formed by the combination of fog computing, edge computing, and cloud computing, requires novel management strategies to coordinate and efficiently manage resources from the edge to the cloud. This management framework design poses various research challenges and has spurred many global initiatives.
Article
Computer Science, Theory & Methods
Cleber Lira, Ernando Batista, Flavia C. Delicato, Cassio Prazeres
Summary: The Internet has evolved into a complex ecosystem integrating various devices, enabling the amalgamation of the physical and virtual worlds. The adoption of microservices in IoT applications has been increasingly common. However, the comprehensive assessment of the performance impact of reactive microservices on IoT applications is still missing in the literature.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Ernando Batista, Gustavo Figueiredo, Cassio Prazeres
Summary: The Internet of Things enables the coordination and orchestration of numerous physical and virtual objects connected to the Internet. This paper proposes a solution for load balancing in IoT applications using Software-Defined Networks. It addresses the challenges posed by unstable network infrastructure and processing overload on IoT devices.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Wanli Yu, Ardalan Najafi, Yanqiu Huang, Alberto Garcia-Ortiz
Summary: The study aimed to address the gap of task allocation approaches that do not consider approximate computing, proposing a method that simultaneously considers approximate computing and task allocation to maximize network lifetime. By allocating tasks and selecting execution modes, a centralized and distributed algorithm were proposed to solve the problem for resource-limited IoT devices, with the distributed algorithm achieving comparable results to the centralized one and outperforming previous approaches.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Theory & Methods
Sandeep Kumar Sood, Vaishali Sood, Isha Mahajan, Sahil
Summary: This paper proposes an intelligent healthcare system that uses cloud computing, internet of things, and fog computing to diagnose and monitor dengue virus infection in real-time, as well as provide risk assessment and alerts.
Article
Engineering, Industrial
Patricia Baudier, Galina Kondrateva, Chantal Ammi, Victor Chang, Francesco Schiavone
Summary: The COVID-19 pandemic has accelerated the digital transformation of healthcare services, particularly the use of teleconsultation for medical care. This study examines the factors that influence the intention to use medical teleconsultation, incorporating trusting beliefs and self-efficacy into the unified theory of acceptance and use of technology (UTAUT2). A survey was conducted among patients who utilized teleconsultation platforms during the pandemic, with a sample size of 1233 respondents. The findings underscore the importance of trusting beliefs and self-efficacy in the adoption of digital healthcare services, contributing to both theory and practice in COVID-19 research.
Article
Computer Science, Information Systems
Victor Chang, Yeqing Mou, Qianwen Ariel Xu, Yue Xu
Summary: This paper proposes the importance of work-life balance, compensation, career opportunity, and culture and management style in improving job satisfaction. A turnover risk prediction model based on the random forest algorithm is constructed to understand the characteristics and identify risks of turnover. Empirical analysis using a sample of 17,724 online reviews from Glassdoor confirms the positive effects of antecedents, with job satisfaction as a mediator and the unemployment rate as a moderator. Finally, job satisfaction is identified as the most crucial feature for predicting turnover.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Article
Information Science & Library Science
Victor Chang, Ou Liu, Kiran Vijay Barbole, Qianwen Ariel Xu, Xianghuaa Jason Gao, Wendy Tabrizi
Summary: The evolution of online shopping was sparked by major players like Amazon, and customers quickly realized the convenience it offered, leading to its growing popularity. Therefore, it is important to study the usage and perception of online shopping during COVID-19, particularly in the grocery sector. This study surveyed approximately 28 respondents from 50 targeted groups using a structured questionnaire and analyzed the collected data through regression analysis. Additionally, 5 interviews were conducted to validate the findings. Customers showed a clear preference for online grocery shopping during COVID-19 due to safety, convenience, and government restrictions, with factors like delivery times, discounts, and product quality playing a significant role. Online grocery services also proved to be more stable and adhered to government rules and restrictions during the pandemic, resulting in high customer satisfaction.
JOURNAL OF GLOBAL INFORMATION MANAGEMENT
(2023)
Article
Computer Science, Cybernetics
Mandeep Kaur, Pankaj Deep Kaur, Sandeep Kumar Sood
Summary: During emergencies, donation is seen as a moral responsibility globally. Lack of transparency and oversight in charity donations hampers people's enthusiasm to donate. Blockchain technology offers a solution to make donation procedures more viable, ensuring a secure and transparent environment without third-party involvement.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Information Systems
Victor Chang, Dan Lawrence, Le Minh Thao Doan, Ariel Qianwen Xu, Ben S. C. Liu
Summary: This paper introduces Aboleth, a tool for rapid prototyping. We propose enhancing the API framework and provide details on the design process and implementation of the project. The prototype is developed using Computer-Assisted Designs and Xamarin, integrating software engineering into game design to improve Human-Computer Interaction and user experiences. Additionally, we discuss the relevance of using this tool for rapid prototyping in enterprise mobile applications and Metaverse, and explore opportunities to improve the project design.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Review
Computer Science, Information Systems
Victor Chang, Lewis Golightly, Qianwen Ariel Xu, Thanaporn Boonmee, Ben S. S. Liu
Summary: This paper discusses cybersecurity issues for children, especially teenagers, with a focus on the impact of social media. Many social media users lack awareness of cybersecurity and digital privacy, and fail to understand the importance of developing privacy measures. The paper identifies seven categories of hacking motivations through multimedia platforms, and explores various hacking methods, such as sexting and influence on buying advertisements. The findings highlight the importance of understanding the digital footprint and its consequences for protection.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Article
Automation & Control Systems
Mohamed Abdel-Basset, Hossam Hawash, Victor Chang
Summary: This article proposes a novel fully volumetric segmentation network called FV-Seg-Net, which effectively addresses the precise segmentation of small-size lesions in CT scans. The network utilizes a computationally efficient recalibrated anisotropic convolution module and a multilevel multiscale pyramid aggregation module to capture local and global spatial information. The introduction of stacked data augmentation further improves the generalizability of FV-Seg-Net. Experimental results show that FV-Seg-Net achieves excellent segmentation performance, outperforming current cutting-edge studies.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Operations Research & Management Science
Xiaoting Dai, Jie Zhang, Victor Chang
Summary: This paper investigates the long-term survival of noise traders and their influence on financial markets by proposing an agent-based artificial stock market. The market consists of noise traders, informed and uninformed traders. Informed and uninformed traders can learn from information using Genetic Programming, while noise traders cannot. The system is calibrated to real financial markets and replicates several stylized facts. The findings suggest that noise traders either cannot survive in the long run or transform into other types of traders, and they increase market volatility, price distortion, noise trader risk, and trading volume. Regulatory interventions, such as price limits, transaction tax, and longer settlement cycles, can affect the survival period of noise traders and their influence on markets.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Information Systems
Yihong Wen, Mingxi Liu, Xiwen Yang, Tailong Yang, Victor Chang
Summary: This paper presents a blockchain-based unlinkable authentication scheme that reduces the computational load of Certificate Authorities and compresses blockchain data using vector commitment. It also minimizes redundancy in vector commitment verification with a binary tree auxiliary index. The scheme achieves its security goals and improves performance by 13.24%, surpassing similar proposals.
ENTERPRISE INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Victor Chang, Qianwen Ariel Xu, Karl Hall, Olojede Theophilus Oluwaseyi, Jiabin Luo
Summary: Traffic flow detection is crucial in freeway traffic surveillance systems. Despite significant investment in monitoring and analyzing traffic congestion, autonomous traffic analysis remains challenging due to the complexity of traffic delays. This study presents an intelligent analytic method based on machine-learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom, achieving high accuracy and demonstrating the practical insights in traffic analysis.
Article
Computer Science, Artificial Intelligence
Muhidin Mohamed, Mourad Oussalah, Victor Chang
Summary: Query-focused multi-document summarization (Qf-MDS) is a sub-task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. In this work, a semantic diversity feature based query-focused extractive summarizer (SDbQfSum) is proposed to address the challenges of query-relevance, centrality, redundancy and diversity. The summarizer combines semantically parsed document text with knowledge-based vectorial representation to extract effective sentence importance and query-relevance features. Evaluation results on the DUC2006 dataset show that the proposed summarizer outperforms most state-of-the-art approaches on ROUGE measures.
Article
Computer Science, Artificial Intelligence
Peichao Lai, Feiyang Ye, Yanggeng Fu, Zhiwei Chen, Yingjie Wu, Yilei Wang, Victor Chang
Summary: The study proposes a CogNLG framework based on the dual-process theory in cognitive science for KG-to-text generation tasks, which shows excellent performance in both explainability and capability.
Review
Business
Sandeep Kumar Sood, Pooja
Summary: Quantum computing has the potential to revolutionize various fields by leveraging principles of quantum mechanics, providing exponential speedup. This study analyzes the scientific literature in the computer science discipline from the Web of Science database to identify key research domains, publication patterns, collaboration, citation patterns, and research frontiers in quantum computing. The findings offer valuable insights for information scientists to understand the field and identify relevant applications, research topics, and challenges.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Business
Vitor Jesus, Balraj Bains, Victor Chang
Summary: Cyber threat intelligence (CTI) is important but often limited to large organizations, creating barriers to effective sharing. This article reviews the challenges of open, crowd-sourced CTI and analyzes the confidentiality threat in existing sharing architectures. By proposing a reference architecture and addressing key requirements, the article aims to strengthen the case for open, crowd-based sharing of CTI and mitigate confidentiality concerns.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Computer Science, Information Systems
Kashan Ahmed, Syed Khaldoon Khurshid, Sadaf Hina
Summary: This paper mainly introduces the construction of the cyber threat intelligence knowledge graph and the information extraction technique. By using joint extraction technique, it solves the problem of traditional techniques becoming ineffective due to the increasing size of CTI data. Experimental results show that this technique outperforms state-of-the-art models in knowledge triple extraction on CTI data and improves the F1 score.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Xinlong He, Yang Xu, Sicong Zhang, Weida Xu, Jiale Yan
Summary: This paper proposes a new membership inference attack method in federated learning, which utilizes data poisoning and sequence prediction confidence. The attack is effective and results in minimal overall model performance degradation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Tieming Chen, Huan Zeng, Mingqi Lv, Tiantian Zhu
Summary: In this paper, the authors propose a deep learning based dynamic malware detection method called CTIMD, which integrates threat knowledge from CTIs into the learning process of API call sequences with runtime parameters. Experimental results show that CTIMD outperforms existing methods in terms of performance.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wonwoo Choi, Minjae Seo, Seongman Lee, Brent Byunghoon Kang
Summary: This paper proposes SUM, a backward-edge control flow protection scheme for ARM Cortex-M processors. It combines MPU and the overlooked hardware feature FaultMask to achieve efficient and robust protection. The empirical evaluation shows minimal runtime overhead for the proposed solution.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Liliana Ribeiro, Ines Sousa Guedes, Carla Sofia Cardoso
Summary: Phishing susceptibility is influenced by individual and contextual factors. The study found that individuals who perceive themselves as capable of detecting phishing and those who use online services more frequently are more susceptible to phishing. However, technology competencies and other individual variables do not predict phishing susceptibility.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Wenjie Wang, Yuanhai Shao, Yiju Wang
Summary: In this paper, we investigate the adversarial perturbations of twin support vector machines (TWSVMs) and propose an optimization framework, which provides explicit solutions to increase the interpretability of the conclusion and convenience for calculation.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Snofy D. Dunston, V. Mary Anita Rajam
Summary: This paper proposes a novel adversarial attack technique that can synthesize adversarial images to mislead deep learning models, and also studies interpretability plots. The research findings show that the proposed attack technique influences the interpretability plots, regardless of the success of the attack.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Junchen Li, Guang Cheng, Zongyao Chen, Peng Zhao
Summary: Protocol Reverse Engineering (PRE) is a direct approach for analyzing unknown traffic. This paper proposes a method for clustering unknown traffic based on private protocol labels, and the experimental results demonstrate its advantages on real-world network traffic.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Rafal Kozik, Massimo Ficco, Aleksandra Pawlicka, Marek Pawlicki, Francesco Palmieri, Michal Choras
Summary: The inclusion of Explainability of Artificial Intelligence (xAI) has become a mandatory requirement for designing and implementing reliable, interpretable, and ethical AI solutions. However, it has been shown that xAI can enable successful adversarial attacks in the domain of fake news detection, leading to a decrease in AI security. This paper presents an attack scheme that uses an explainable solution to reshape the structure of the original message, allowing the adversary to manipulate the model's prediction while keeping the message's meaning intact.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Benyuan Yang, Lili Luo, Zhimeng Wang
Summary: Interoperation is widely used in practical industrial applications, but merging local access control policies may lead to security violations. Dealing with these issues in a multidomain environment is critical, but finding the maximum secure interoperation among individual systems poses a challenge due to the large number of entities and access involved.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Binghui Zou, Chunjie Cao, Longjuan Wang, Sizheng Fu, Tonghua Qiao, Jingzhang Sun
Summary: The ongoing struggle between security researchers and malware has led to the exploration of using convolutional neural networks and capsule networks for classification and identification of malware. However, training these networks requires a significant amount of data and parameters, and the research on capsule networks is still in its early stages, posing challenges.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Hongsong Chen, Xingyu Li, Wenmao Liu
Summary: Multivariate time-series anomaly detection is crucial for maintaining normal operation of physical equipment. Recent advances have been made in this field, but two challenges have limited the model's ability to generalize. To address these challenges, a multivariate time-series anomaly detection model consisting of a characterization network and a forecasting network is proposed. Experimental results demonstrate that this method outperforms baseline methods in terms of detection performance and robustness.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Roberto Doriguzzi-Corin, Domenico Siracusa
Summary: This paper discusses the application of federated learning in the field of cybersecurity and proposes an adaptive mechanism-based federated learning solution for DDoS attack detection in dynamic cybersecurity scenarios. Through experiments, it is demonstrated that the proposed solution outperforms state-of-the-art federated learning algorithms in terms of convergence time and accuracy.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Antonio Giovanni Schiavone
Summary: The usage of HTTPS protocol is crucial for secure communication with websites, ensuring the confidentiality, integrity, and authenticity of online data transmissions. The Municipality2HTTPS research project analyzed the implementation of HTTPS in Italian municipalities' websites and identified areas for improvement.
COMPUTERS & SECURITY
(2024)
Article
Computer Science, Information Systems
Domna Bilika, Nikoletta Michopoulou, Efthimios Alepis, Constantinos Patsakis
Summary: Voice Assistants (VAs) are widely used in smart devices, but are vulnerable to attacks, as shown by experiments with popular VAs revealing successful attack rates exceeding 30% and statistical variations among vendors, calling for additional countermeasures to protect user information.
COMPUTERS & SECURITY
(2024)
