Article
Green & Sustainable Science & Technology
Qin Xin, Mamoun Alazab, Ruben Gonzalez Crespo, Carlos Enrique Montenegro-Marin
Summary: This article discusses the importance and challenges of multimedia communications in Internet of Vehicles (IoV) and introduces new algorithms and models to address these issues. The experimental results show that the proposed methods can enhance user experience and have the potential to be promising contenders in IoV implementations.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Computer Science, Information Systems
Amar Khelloufi, Huansheng Ning, Sahraoui Dhelim, Tie Qiu, Jianhua Ma, Runhe Huang, Luigi Atzori
Summary: Social Internet of Things is a new paradigm aiming to solve problems in network discovery and service composition by socializing IoT devices. The issue of services explosion leads to difficulties in service filtering and customization, making the selection of suitable services for applications and devices a challenging task. Incorporating users' social relationships in service recommendation can increase the accuracy and diversity of offered services in IoT scenarios.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Hardware & Architecture
Alireza Safaei, Ramin Nassiri, Amir Masoud Rahmani
Summary: The major emerging trends for future business values are business processes decomposition and smart awareness. In the Internet of Things ecosystems, factors affecting the complexity of decision models have made many recent methods unable to find optimal solutions in a reasonable time. Therefore, improving ESC models can effectively support enterprise business process needs and enhance user satisfaction.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Shaozhong Zhang, Dingkai Zhang, Yaohui Wu, Haidong Zhong
Summary: This article proposes a SIoT services recommendation model based on trust and QoS, which accurately calculates users' trust relations and predicts the QoS of services. Experimental results demonstrate that the proposed model effectively recommends services with high trustworthiness and quality.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Information Systems
Seyedsalar Sefati, Nima Jafari Navimipour
Summary: The Internet of Things (IoT) presents various service composition issues related to QoS parameters, which are typically addressed using metaheuristic methods like HMM and ACO. This research proposes an effective strategy for enhancing QoS in IoT by training HMM to predict QoS and utilizing ACO for QoS estimation. The results demonstrate the efficacy of the approach in improving availability, response time, cost, reliability, and energy consumption compared to previous methods.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Mathematics, Interdisciplinary Applications
Xuemei Fan
Summary: This article explores the solution to the service matching problem in the context of the mobile Internet of Things, proposing a service matching strategy based on dynamic grid QoS and a grid task based on QoS constraints. By considering semantic information, service quality QoS, and user preferences, the proposed strategy not only improves service matching performance and task execution success rate, but also enhances system load balancing effects.
Article
Engineering, Multidisciplinary
Kubilay Demir
Summary: The Internet of Things enables interconnections among computing devices embedded in everyday objects, creating smart environments where users can perform tasks by combining multiple services. To address issues in service discovery and selection, QoS and energy-aware service discovery algorithms, along with an optimization algorithm reflecting QoS, have been proposed. These algorithms aim to explore near-real-time QoS values of services, avoid high message overhead, and overcome shortcomings in availability and scalability of existing approaches.
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2021)
Article
Computer Science, Information Systems
Anuoluwapo A. Adewuyi, Hui Cheng, Qi Shi, Jiannong Cao, Xingwei Wang, Bo Zhou
Summary: This study investigates and models the trust management issues in service compositions in the IoT, proposing a novel model called SC-TRUST. The results demonstrate that SC-TRUST improves the quality of service compositions and effectively mitigates trust-related attacks, enhancing both efficiency and security.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Ako A. Jaafar, Dayang N. A. Jawawi, Mohd Adham Isa, Nor Azizah Saadon
Summary: This study proposes a model for service selection based on user intentions and context, which evaluates user preferences for services by calculating QoS importance based on user behavior history and context. It utilizes a dynamic K-Skyline method and a multi-criteria decision-making technique to efficiently select and rank services. A case study and experiment validate the effectiveness and robustness of the proposed model.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Editorial Material
Automation & Control Systems
Pradip Kumar Sharma, Uttam Ghosh, Lin Cai, Jianping He
Summary: IoT healthcare plays a crucial role in the healthcare industry by reducing costs and improving quality of life. However, security concerns regarding healthcare information need to be addressed continually.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Yuan Tian, Biao Song, Tinghuai Ma, Abdullah Al-Dhelaan, Mohammed Al-Dhelaan
Summary: In this paper, a privacy-aware people-centric IoT service based on a tailored auction approach is investigated using a bi-tier differential privacy methodology. A corresponding pricing scheme is proposed to ensure incentive compatibility and precise service data. Compared to traditional schemes, the proposed auction scheme offers tailored IoT service based on customers' requests, providing a precise assessment of service quality before bidding.
Article
Computer Science, Hardware & Architecture
Metehan Guzel, Suat Ozdemir
Summary: This paper proposes a multi-objective optimization-based IoT service composition framework for fog-based IoT networks, utilizing the NSGA-II. Experimental evaluation results show that the proposed approach can optimize energy consumption and fairness without causing any QoS degradation.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Alireza Souri, Mostafa Ghobaei-Arani
Summary: The new solution for developing IoT applications is cloud manufacturing, which enhances the intelligence of enterprise information systems by increasing the number of actuators, industrial devices, and sensors in industry 4.0 technology. Cloud manufacturing service composition allows for faster response to needs and matching cloud services with modified requirements, providing integrated services according to user QoS requirements in a collaborative environment. Verification using Labeled Transition System and Whale Optimization Algorithm ensures improved Quality of Service (QoS) and satisfaction of logical problems like deadlock-free and reachability in cloud manufacturing for IoT applications.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zheng-yi Chai, Meng-meng Du, Guo-zhi Song
Summary: A fast energy-centered and QoS-aware service composition approach (FSCA-EQ) is proposed in this article for IoT service composition, which utilizes hierarchical optimization, compromise ratio method, and relative dominance concept to better satisfy user's QoS requirements and reduce energy consumption.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Mohammadreza Razian, Mohammad Fathian, Huaming Wu, Ahmad Akbari, Rajkumar Buyya
Summary: Cloud computing and the Internet of Things (CloudIoT) work together to create smart cities and application services. However, previous approaches fail to accurately model QoS values, leading to service-level agreement violations. A proposed anomaly-aware approach significantly improves QoS values by addressing anomalies in QoS records.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
