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AIoT for sustainable manufacturing: Overview, challenges, and opportunities

INTERNET OF THINGS (2023)

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Article Computer Science, Information Systems

Enabling Ambient Intelligence of Things (AIoT) healthcare system architectures

Anil Pise et al.

Summary: In recent years, there has been significant research in healthcare and administration of cutting-edge ambient intelligence (AmI) technology. Healthcare experts have explored smart gadgets, medical technologies, and ambient intelligence using Internet of Things (IoT) (AIoT). The potential to improve care for individuals living in rural and distant areas by linking these two fields is evident. The healthcare industry has experienced tremendous advances in efficiency, affordability, and usefulness, thanks to new growth opportunities and significant cost reductions. The application of AIoT-H is expected to be covered in the last section, examining relevant approaches for solving healthcare problems and identifying potential issues and inconsistencies.

COMPUTER COMMUNICATIONS (2023)

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Article Computer Science, Software Engineering

An effective resource scheduling model for edge cloud oriented AIoT

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Summary: This study proposes a resource scheduling method based on edge computing and multi-objective algorithms, which can effectively solve the resource load scheduling problem in AIoT and improve energy utilization efficiency.

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Article Automation & Control Systems

A Novel Two-Stage Unsupervised Fault Recognition Framework Combining Feature Extraction and Fuzzy Clustering for Collaborative AIoT

Xufeng Hu et al.

Summary: This article proposes a novel two-stage unsupervised fault recognition algorithm, called the deep adaptive fuzzy clustering algorithm (DAFC), for unsupervised fault clustering analysis. Experimental results show that DAFC can stably extract fault features from unlabeled data and automatically obtain the optimal clustering results without prior knowledge of the number of clusters.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Automation & Control Systems

Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT

Zuobin Xiong et al.

Summary: This article explores innovative approaches to privacy protection in federated learning with non-i.i.d. data. A novel algorithm, 2DP-FL, is designed to achieve differential privacy by adding noise during training local models and when distributing global model. The results of theoretical analysis and real-data experiments validate the advantages of 2DP-FL in privacy protection, learning convergence, and model accuracy.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Computer Science, Information Systems

Energy-Efficient Artificial Intelligence of Things With Intelligent Edge

Sha Zhu et al.

Summary: This article focuses on the energy consumption of AIoT tasks and proposes an optimization method for task scheduling that utilizes intelligent edge computing. The proposed method improves energy efficiency compared to other strategies, as demonstrated through extensive simulation-based performance evaluation.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Using deep learning to value free-form text data for predictive maintenance

Juan Pablo Usuga-Cadavid et al.

Summary: The study explored the use of deep learning models for natural language processing of unstructured data from maintenance logs, mitigating class imbalance and improving interpretability through two methods. Results showed that valuable information can be extracted from maintenance reports using this approach.

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Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT

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Summary: The Artificial Intelligence Internet of Things (AIoT) is an emerging concept that aims to connect intelligent things for efficient intercommunication. This paper presents a prior-dependent graph (PDG) construction method to model and discover complex relations in data, enabling high-efficiency data clustering and dimensionality reduction. Experimental results demonstrate that the PDG model achieves substantial performance compared to existing graph learning models.

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Summary: Supply chain management is an essential issue for businesses, and technological revolution has brought new innovations and challenges. AIoT, as an expected innovation, can integrate complex supply chains. However, the growing technology, the internet, and vast amounts of data also pose challenges, with cybersecurity and insufficient infrastructure being the most important ones.

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Towards intelligent and integrated pest management through an AIoT-based monitoring system

Dan Jeric Arcega Rustia et al.

Summary: The development of an intelligent and integrated pest and disease management system, called IPDM-P-2, has enabled automatic detection and recognition of major greenhouse insect pests, as well as measurement of environmental conditions. The system has been installed in tomato and orchid greenhouses for over 1368 days, leading to significant yearly reductions in insect pest count. This innovative approach showcases the potential benefits of using data-driven IPM strategies to support farm managers in making informed decisions.

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Summary: The Industrial Revolution 4.0 (IR 4.0) offers opportunities to improve solid waste management through digital and machinery applications. The use of IR 4.0 technologies, such as machine learning and RFID, can automate waste segregation and enable traceability in materials, leading to more efficient waste management. However, there is a lack of coherency in applying these technologies on a larger scale, which calls for a comprehensive end-to-end integration for optimizing the entire solid waste management chain.

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Review Engineering, Manufacturing

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The implementation to intelligent linkage service over AIoT hierarchical for material flow management

Joy Iong-Zong Chen

Summary: The article introduces a total solution implemented by AIoT techniques, focusing on intelligent linkage devices. It includes improving material flow management efficiency through flow tracking technology, strengthening autonomous management ability in factories with spatial distribution tools for chemical substances, and simplifying reporting management mechanisms in colleges and universities with a chemical substances management platform.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

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Waste-to-energy conversion technologies in the UK: Processes and barriers - A review

William Foster et al.

Summary: This paper reviews the waste-to-energy sector, emphasizing the processes and feedstock involved, particularly highlighting the potential of anaerobic digestion in reducing pollution from manure and slurry. The significant role of manure in producing biogas for electricity generation is discussed, with the possibility of producing over 1.615 TWh of electricity in the UK if most manure and slurry were processed through biogas harvesting.

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Energy-Efficient Industrial Internet of Things: Overview and Open Issues

Wenliang Mao et al.

Summary: This article presents a comprehensive survey on energy-efficient communications and computation mechanisms in IIoT systems, categorizing, reviewing, discussing, and comparing existing works to explore their pros and cons. The open issues and research challenges in the context of recent 5G communications and edge computing trends are also discussed.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Technology evolution from self-powered sensors to AIoT enabled smart homes

Bowei Dong et al.

Summary: The increasing demand for self-powered sensors and self-sustainable micro-/nano-systems in smart home applications is addressed in this review. The integration of triboelectric nanogenerators with energy storage components for more general application scenarios is highlighted, along with the development of novel materials and configurations to improve system performance. The synergy between wearable sensors and AI technology is emphasized for analyzing data, recognizing events, and making decisions at a system level.

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Chao-Tung Yang et al.

Summary: Air pollution is a major concern for public health and the environment, and monitoring with low-cost devices can help collect data for various applications. The integration of IoT-based systems and AI can improve precision in sensor networks and provide better resolution in PM monitoring and control.

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Summary: This article proposes a new expected advantage learning method to moderate the maximum value of expectation-based deep reinforcement learning for industrial applications. By replacing the sigmoid function with the tanh function as the softmax activation value, the proposed method successfully reduces the issue of numerical overfitting in cognitive computing.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Andrew Glaeser et al.

Summary: The study investigated the feasibility of using deep learning techniques for fault detection in industrial cold forging. Vibration data collected was classified using a Convolutional Neural Network, showing potential for fault detection but requiring further validation.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2021)

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Review Computer Science, Information Systems

Blockchain-Envisioned Secure Authentication Approach in AIoT: Applications, Challenges, and Future Research

Mohammad Wazid et al.

Summary: AIoT is the fusion of AI methods and IoT infrastructure to enhance system performance, while blockchain integration improves security. The proposed secure authentication framework and adversary model are applicable to various AIoT applications.

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An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

Tao Han et al.

Summary: The article emphasizes on an intelligent energy management framework based on deep learning, focusing on efficient communication between edge devices and smart grids for short-term energy consumption prediction. By applying various data preprocessing techniques and decision-making algorithms, the mean-square error and root mean-square error of residential and commercial datasets were significantly reduced.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Haifei Yu et al.

Summary: Digital twinning has become a hot topic in the manufacturing industry, providing new methods for intelligent manufacturing. Solving job shop scheduling problems through digital twin technology, an intelligent scheduling platform is proposed.

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Article Green & Sustainable Science & Technology

Weather radar echo prediction method based on convolution neural network and Long Short-Term memory networks for sustainable e-agriculture

Lei Zhang et al.

Summary: Weather radar echo images are crucial for the stability of agricultural production, predicting different disastrous weather patterns can help improve field safety. Deep learning methods show great potential in radar echo prediction, effectively trained models can accurately predict changes in echo shapes.

JOURNAL OF CLEANER PRODUCTION (2021)

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Article Business

Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management

Yubing Yu et al.

Summary: Our research shows that under Industry 4.0, supply chain relationship capital positively affects enterprise green management, leading to improved financial performance. Additionally, supply chain relationship capital indirectly enhances companies' financial performance by leveraging their green management initiatives, providing practical guidance for implementation.

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IEEE INTERNET OF THINGS JOURNAL (2021)

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Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self-Powered Sensor Enhanced Soft Robotic Manipulator

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ADVANCED SCIENCE (2021)

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IEEE INTERNET OF THINGS JOURNAL (2021)

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An edge-cloud collaborative computing platform for building AIoT applications efficiently

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