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Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey

IEEE INTERNET OF THINGS JOURNAL 2022年第11期9卷 8364-8386页

作者： Bian, Jiang Al Arafat, Abdullah Xiong, Haoyi Li, Jing Li, Li Chen, Hongyang Wang, Jun Dou, Dejing Guo, Zhishan Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Baidu Inc Big Data Lab Beijing 100193 Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Univ Macau Dept Comp & Informat Sci Macau Peoples R China Zhejiang Lab Res Ctr Intelligent Network Hangzhou 311121 Zhejiang Peoples R China

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.

关键词： Task analysis Real-time systems Machine learning algorithms Scheduling algorithms Internet of Things Hardware Sensors Deep learning (DL) Internet of Things (IoT) machine learning (ML) real-time systems (RTSs) scheduling

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Industrial 5.0 Aquaponics System Using Machine Learning techniques 7

Industrial 5.0 Aquaponics System Using Machine Learning Tech...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (纸本)9798350340600

Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. The primary objective of the industry 5.0 initiative is to encourage customization and innovation while simultaneously increasing the productivity, efficiency, and adaptability of manufacturing processes. The industry's coming to terms with the 5.0 vision is underpinned by machine learning. It makes it possible for machines and systems to see patterns in data, make decisions based on that data, and learn without having to be explicitly programmed. algorithms that learn from machines are able to analyze vast amounts of data collected from sensors, machines, and other sources in order to make predictions, improve processes, and acquire insights. This capacity has applications in a variety of domains, including quality control, predictive maintenance, supply chain optimization, process optimization, and autonomous robots. Machine learning-based predictive maintenance reduces downtime and optimizes maintenance schedules by detecting industrial equipment faults. Machine learning-powered quality control systems monitor manufacturing, identify errors, and ensure product quality. U sing machine learning algorithms on supply chain data improves inventory management, logistics, and efficiency. Process optimization uses machine learning to find trends in production data to improve efficiency and energy use. Machine learning-enabled autonomous robots and intelligent decision- making systems help humans complete complicated tasks safely and efficiently. Aquaponics is a major step toward a more connected, intelligent, and data-driven industrial landscape. © 2023 IEEE.

关键词： Decision making

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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IEEE International Conference on Power, intelligent Computing and Systems (ICPICS)

作者： Herun Zhong School of Arts and Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. This article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. The discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks.

关键词： Deep learning computer vision Visualization Image recognition Benchmark testing Visual systems Classification algorithms Object recognition Convolutional neural networks Videos

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IoT-Based Automation System for Vertical Hydroponic Farms 26

IoT-Based Automation System for Vertical Hydroponic Farms

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kumburage, D.K. Hewavitharana, H.V.T.G. Perera, M.N.T. Amarasinghe, Y.W.R. Jayathilaka, W.A.D.M. University of Moratuwa Dept. of Mechanical Engineering Sri Lanka

ISBN: (纸本)9798331521950

Greenhouse vertical rack hydroponic systems offer a sustainable and efficient solution for meeting the increasing global food demand. This paper introduces an IoT-integrated automated system designed to perform labor intensive and repetitive tasks in hydroponic farming. The proposed system integrates a robotic platform for transplanting and inspecting plants, as well as an intelligent controller to control and monitor greenhouse conditions and nutrient solution parameters. To enhance performance and accessibility, the system is integrated with an IoT platform, which includes a user-friendly web interface, enabling remote monitoring and control. Key features such as computer vision techniques and advanced control algorithms are implemented to maximize operational efficiency. Experimental results validate its ability to reduce labor, improve productivity, and ensure consistent crop quality. This solution highlights the potential for scalable and sustainable advancements in modern agriculture. © 2025 IEEE.

关键词： intelligent robots

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Development of computer vision Based Aluminium Scrap Sorting Using Real Time Metal Scrap Dataset

Development of Computer Vision Based Aluminium Scrap Sorting...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Adhithya S J. Kathirvelan School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The aluminium manufacturing industries face numerous challenges when it comes to sorting metal scraps, particularly when scraps are small in size. Traditional methods like flotation and gravity separation are inefficient, energy-intensive, and often fail when dealing with size variations. Recent advancements in computer vision and deep learning algorithms, including intelligent robotic systems, offer promising solutions. However, challenges such as accurate detection in complex environments, varying lighting conditions, and overlapping objects still persist. Newer techniques like YOLO (You Only Look Once) for real-time object detection, and Transfer Learning to improve model accuracy with smaller datasets, have shown potential. This research aims to overcome these challenges by developing an AI-powered, computer vision-based sorting system that can identify and classify aluminium scraps by color and shape at high speed. The authors developed a custom dataset and trained models using CNN architectures such as DenseNet-161, ResNet-152, and VGG 19. By employing ensemble techniques, this research achieved an 75% of accuracy.

关键词： Deep learning YOLO computer vision Accuracy Service robots Computational modeling Aluminum Real-time systems Recycling Sorting

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Reducing Computation Requirements for Autonomous Mobile robots on Low Powered Embedded Systems

Reducing Computation Requirements for Autonomous Mobile Robo...

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Modeling, Simulation & intelligent Computing (MoSICom), International Conference on

作者： Abhinav Pathak Aditya Singhal Adit Tewari Aadish Jain V. Kalaichelvi Department of Computer Science Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai UAE Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.

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Application of Keypoint Recognition for Industrial Human-Robot Safe Collaboration Scenarios

Application of Keypoint Recognition for Industrial Human-Rob...

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International Symposium on Autonomous Systems (ISAS)

作者： Zhixiang Ma Wenhua Jiao Lijuan Li Shiping Yang XiaoWei Xu College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

With the advent of Industry 5.0 and the rise of human-centered intelligent manufacturing, people have paid increasing attention to the issue of security in human-machine collaboration. Developing safe human-robot cooperation in constrained environments has emerged as the primary area of research interest. vision systems with deep learning have gradually supplanted more conventional approaches, such electronic wearables, electronic fences, and lidar techniques, to ensure safe collaboration. Object identification and posture estimation are two techniques that are currently in use to forecast distances in space more accurately. These techniques can track the approximate locations of humans and robots in real-time, significantly lowering the likelihood of safety incidents. Still, more accurate evaluation of the relative positions of humans and robots is needed for effective collaboration. This paper suggests SCC-HRNet, an efficient key point recognition technique. SCC-HRNet is able to find the important feature points of both humans and robots more precisely in dual-camera human-robot safe collaboration scenarios. Using our human-robot collaboration dataset, SCC-HRNet outperforms other algorithms with an average precision gain of 1.6%, correctly identifying key points.

关键词： Accuracy Service robots Collaboration Predictive models Rendering (computer graphics) Real-time systems Safety

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Novel 6D Pose Estimation for Pick-and-Place Tasks using Model-based Learning

Novel 6D Pose Estimation for Pick-and-Place Tasks using Mode...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Logesh G S. Vinoth Nagarajan Pitchandi A.G. Karthikeyan Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Mechatronics Akshaya College of Engineering and Technology Coimbatore India Department of Mechatronics Bannari Amman Institute of Technology Sathyamangalam India School of Mechanical Engineering REVA University Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The paper discusses the problem of creating an autonomous SCARA robot that can precisely carry out pick-and-place operations in unstructured settings. The suggested method combines computer vision and deep learning algorithms to provide accurate 6D pose estimation and reliable object detection. Combining YOLOv8 for object detection, hand-eye calibration for precise transformation to robot coordinates, and Principal Component Analysis (PCA) for orientation estimation within the generated point cloud, a novel method for estimating object pose is presented. Monocular depth estimation techniques are used to extract depth information from RGB images to create point clouds. The results demonstrate the potential of combining advanced algorithms with user-friendly design for robotics automation, showing notable gains in pose estimation accuracy and task execution efficiency.

关键词： Point cloud compression Accuracy Service robots Robot kinematics Pose estimation Object detection Manuals Calibration Reliability Principal component analysis

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A Comprehensive Survey of Image Augmentation techniques for Deep Learning

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PATTERN RECOGNITION 2023年第1期137卷

作者： Xu, Mingle Yoon, Sook Fuentes, Alvaro Park, Dong Sun Jeonbuk Natl Univ Dept Elect Engn Jeonbuk 54896 South Korea Mokpo Natl Univ Dept Comp Engn Jeonnam 58554 South Korea Jeonbuk Natl Univ Core Res Inst Intelligent Robots Jeonbuk 54896 South Korea

Although deep learning has achieved satisfactory performance in computer vision, a large volume of im-ages is required. However, collecting images is often expensive and challenging. Many image augmenta-tion algorithms have been proposed to alleviate this issue. Understanding existing algorithms is, therefore, essential for finding suitable and developing novel methods for a given task. In this study, we perform a comprehensive survey of image augmentation for deep learning using a novel informative taxonomy. To examine the basic objective of image augmentation, we introduce challenges in computer vision tasks and vicinity distribution. The algorithms are then classified among three categories: model-free, model-based, and optimizing policy-based. The model-free category employs the methods from image process-ing, whereas the model-based approach leverages image generation models to synthesize images. In con-trast, the optimizing policy-based approach aims to find an optimal combination of operations. Based on this analysis, we believe that our survey enhances the understanding necessary for choosing suitable methods and designing novel algorithms.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Image augmentation Deep learning Image variation Vicinity distribution Data augmentation computer vision

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AI-based vision system for collision detection in HRC applications 9

AI-based vision system for collision detection in HRC applic...

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9th CIRP Conference on Assembly Technology and Systems, CATS 2022

作者： Makris, Sotiris Aivaliotis, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece

Human-Robot Collaboration (HRC) enabling mechanisms require real-time detection of potential collisions among human and robots. Taking under consideration the already existing standards and the literature, most of collision detection techniques require the integration of sensorial systems on the robot aiming to identify the contact events. This paper deals with a novel approach for the identification of human and robot collision based on vision systems. Moreover, Artificial intelligent (AI) algorithms are required to classify the captured data near real-time and to provide a score about the collision status (contact or non-contact) between a human and the robot. Accordingly, the AI models should be trained using the appropriate image data enabling an accurate classification. The proposed system has been developed in a lab environment. A detailed presentation of the system implementation, its performance and the potential integration in a real industrial environment are discussed in this paper. © 2022 Elsevier B.V.. All rights reserved.

关键词： computer vision

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