Article
Computer Science, Information Systems
Nan Cheng, Shen Wu, Xiucheng Wang, Zhisheng Yin, Changle Li, Wen Chen, Fangjiong Chen
Summary: With the rapid development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) are widely used to improve the performance of IoT networks. These UAVs can provide wireless access to IoT devices and perform various IoT services and applications. However, the complexity of UAV-assisted IoT networks has led to the use of artificial intelligence (AI)-based methods to optimize, schedule, and coordinate these networks. This article comprehensively analyzes the impact of AI on UAV-assisted IoT networks and discusses potential research directions.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Telecommunications
Rambod Pakrooh, Ali Bohlooli
Summary: Unmanned Aerial Vehicles (UAVs) have been widely developed for military applications, and with the advancement of IoT and smart devices, they are now being used in various domains. The inherent advantages of UAVs, such as high dynamicity and low cost, have motivated researchers to integrate them into IoT systems. Future directions include addressing the challenges associated with designing UAV-assisted IoT systems.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Ravesa Akhter, Shabir Ahmad Sofi
Summary: Modern agricultural science is becoming more accurate, data-driven, and powerful with the advancement of Internet of Things technology. The application of IoT data analytics and machine learning in agriculture brings new benefits and has a positive impact on increasing crop production and quality.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Conghui Hao, Yueyun Chen, Zhiyuan Mai, Guang Chen, Meijie Yang
Summary: This paper aims to further reduce UAV energy consumption while minimizing task completion time. A novel UAV utility function is proposed to describe task completion time and energy consumption. The problem is formulated as a mixed-integer nonconvex utility maximization problem, and a two-layer joint task time and trajectory optimization (JTTTO) iterative algorithm is proposed to solve it. The proposed data acquisition scheme successfully decreases UAV energy consumption while minimizing task completion time.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Na Lin, Yanbo Fan, Liang Zhao, Xiaoming Li, Mohsen Guizani
Summary: This paper proposes a global energy efficiency maximization strategy for multi-UAV enabled communication systems. The strategy optimizes the trajectory control of UAVs to maximize the global energy efficiency, taking into account both communication throughput and total energy consumption.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Xiao-Hui Lin, Su-Zhi Bi, Nan Cheng, Ming-Jun Dai, Hui Wang
Summary: This article discusses how to use drones to collect ground data in IoT systems, and proposes an alpha-fairness approach to balance energy consumption among IoT sensors to improve system longevity. By designing an alpha-utility function to balance the tradeoff between energy efficiency and fairness, maximizing the utility function to optimize bandwidth allocation, transmission power, and UAV trajectory. The article also demonstrates how to properly set the alpha value according to specific application scenarios to achieve different levels of energy fairness.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Nam H. Chu, Dinh Thai Hoang, Diep N. Nguyen, Nguyen Van Huynh, Eryk Dutkiewicz
Summary: This article introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Seyed Mostafa Bozorgi, Mehdi Golsorkhtabaramiri, Samaneh Yazdani, Sahar Adabi
Summary: The Internet of Things (IoT) is a platform for large-scale data collection in the socio-physical space. Wireless Sensor Nodes (WSN) and Unmanned Aerial Vehicles (UAVs) are crucial in reducing costs and improving usability. The Smart Optimizer Approach (SOA) is introduced to tackle energy consumption in UAV-Assisted IoT Wireless Networks. A new clustering protocol, SOAbased Clustering (SOAC), is proposed to address the limitations of traditional protocols in large-scale networks.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Qihao Dong, Xu Zheng, Anmin Fu, Mang Su, Lei Zhou, Shui Yu
Summary: This paper proposes a model usability detection scheme, named Defense against Model-Reuse Attacks (DMRA), suitable for IoT scenarios. Experimental evaluations demonstrate the effectiveness of DMRA in detecting model-reuse attacks, achieving a success rate of up to 80% at a low computational cost.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Ahmed A. Al-Habob, Octavia A. Dobre, Sami Muhaidat, H. Vincent Poor
Summary: This study addresses the problem of minimizing energy consumption by disseminating files to IoT devices using UAV technology. It proposes a framework for selecting optimal paths and utilizes ant colony optimization algorithm for efficient data dissemination, which outperforms the traditional hovering method.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Chemistry, Analytical
Lucas Rodrigues, Andre Riker, Maria Ribeiro, Cristiano Both, Filipe Sousa, Waldir Moreira, Kleber Cardoso, Antonio Oliveira-Jr
Summary: This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs) as data collectors for the Internet of Things (IoT). The proposed model addresses both single and multiple aircraft, as well as a clustering technique to extend the scope of IoT devices visited by UAVs. The flight plan focuses on preventing breakdowns due to a lack of battery charge and maximizing the number of nodes visited, with consideration of data storage limitations and energy consumption of drones. Simulation results show the algorithm's behavior in generating routes, and the model is evaluated using a reliability metric.
Article
Construction & Building Technology
Jiajie Xu, Dejuan Li, Wei Gu, Ying Chen
Summary: This paper investigates the UAV-assisted offloading problem for IoT in smart buildings and environment. The paper proposes the UAV-assisted task offloading (UTO) approach based on deep reinforcement learning techniques. The performance of the proposed UTO approach is validated through a series of comparison experiments.
BUILDING AND ENVIRONMENT
(2022)
Article
Computer Science, Information Systems
Tran Viet Khoa, Dinh Thai Hoang, Nguyen Linh Trung, Cong T. Nguyen, Tran Thi Thuy Quynh, Diep N. Nguyen, Nguyen Viet Ha, Eryk Dutkiewicz
Summary: Federated learning is an effective approach for cyberattack detection systems in IoT networks, which can improve learning efficiency, reduce overheads, and enhance privacy. However, the challenge lies in the unavailability of labeled data and dissimilarity in data features. This article proposes a collaborative learning framework that leverages transfer learning to overcome these challenges.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Di Lin, Weiwei Wu
Summary: This article proposes a lightweight RF fingerprinting recognition method and a resource allocation scheme, taking into account the limited computing power and resources of UAVs, to ensure the security of UAV-based IoT.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Jinya Su, Xiaoyong Zhu, Shihua Li, Wen-Hua Chen
Summary: Precision Agriculture (PA) is gaining increasing attention in academia and industry due to its potential to enhance crop productivity, reduce costs, and minimize environmental impact. This survey paper provides a comprehensive review of the recent use of UAV sensing systems and AI algorithms in PA applications throughout the crop life-cycle, as well as the challenges and prospects for future development in the agriculture sector.
Article
Computer Science, Information Systems
Sridevi Subbiah, Kalaiarasi Sonai Muthu Anbananthen, Saranya Thangaraj, Subarmaniam Kannan, Deisy Chelliah
Summary: This article introduces an intrusion detection system for wireless sensor networks and proposes a new framework that improves accuracy. By utilizing Boruta feature selection with grid search random forest algorithm, the framework performs well in detecting attacks and outperforms other existing algorithms.
JOURNAL OF COMMUNICATIONS AND NETWORKS
(2022)
Article
Telecommunications
G. Ananthi, S. Sridevi
Summary: This paper proposes a novel approach of applying stacking dilated convolutional autoencoder beamforming for Terahertz wave Vehicular ad-hoc networks. The vehicle's position is determined using big data techniques and deep learning algorithms. The research results obtained through this approach can serve as a benchmark for analyzing and developing TeraHertz Vehicular-Adhoc Networks.
WIRELESS PERSONAL COMMUNICATIONS
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Abdul Qayyum, M. K. A. Ahamed Khan, Abdesslam Benzinou, Moona Mazher, Manickam Ramasamy, Kalaiselvi Aramugam, C. Deisy, S. Sridevi, M. Suresh
Summary: In recent neuroscience and clinical research, non-invasive imaging tools are commonly used to diagnose brain diseases and observe brain activity. This paper proposes a deep learning model that utilizes functional magnetic resonance imaging (fMRI) data for the classification and diagnosis of brain disorders. The model achieves high performance by employing automated feature extraction and leveraging standard atlases.
INNOVATIVE DATA COMMUNICATION TECHNOLOGIES AND APPLICATION, ICIDCA 2021
(2022)
Proceedings Paper
Automation & Control Systems
Duc Chung Tran, M. K. A. Ahamed Khan, S. Sridevi
2020 11TH IEEE CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM (ICSGRC)
(2020)
Proceedings Paper
Automation & Control Systems
M. K. A. Ahamed Khan, Siti Hajar, Manickam Ramasamy, Chun Kit Ang, Lim Wei Hong, Tran Duc Chung, Kalaiselvi Aramugam, S. Sridevi
2020 IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INTELLIGENT SYSTEMS (I2CACIS 2020)
(2020)
Proceedings Paper
Engineering, Industrial
Abdul Qayyum, Chun Kit Ang, S. Sridevi, M. K. A. Ahamed Khan, Lim Wei Hong, Moona Mazher, Tran Duc Chung
2020 INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING, APPLICATIONS AND MANUFACTURING (ICIEAM)
(2020)
Article
Management
Sudhaman Parthasarathy, C. Sridharan, Thangavel Chandrakumar, S. Sridevi
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY PROJECT MANAGEMENT
(2020)
Proceedings Paper
Computer Science, Theory & Methods
M. Yalini, S. Sridevi
PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT)
(2018)
Article
Engineering, Industrial
S. Sridevi, S. Parthasarathy, S. Rajaram
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
S. Sridevi, P. Saranya, S. Rajaram
ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY ALGORITHMS IN ENGINEERING SYSTEMS, VOL 1
(2015)
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)