https://journals.threws.com/index.php/TRDAIoT/issue/feedTransaction on Recent Developments in Industrial IoT2024-12-14T11:11:14+00:00Rita Sahanicontact@triot.comOpen Journal Systems<p>Transactions on Recent Developments in Industrial IoT</p> <p><strong>Scope:</strong></p> <p>The "Transactions on Recent Developments in Industrial IoT" is a peer-reviewed, interdisciplinary journal dedicated to advancing the knowledge and understanding of the rapidly evolving field of Industrial Internet of Things (IIoT). Our journal provides a platform for researchers, engineers, and practitioners to share their innovative contributions and insights, fostering collaboration and exploration in this dynamic domain.</p> <p><strong>Aims and Objectives:</strong></p> <ol> <li> <p><strong>Cutting-Edge Research:</strong> Our journal aims to showcase the latest research, innovations, and developments in the field of Industrial IoT. We are committed to promoting original and high-quality research that pushes the boundaries of knowledge in IIoT.</p> </li> <li> <p><strong>Interdisciplinary Approach:</strong> IIoT is inherently multidisciplinary, combining elements of IoT, data science, industrial automation, and more. We welcome contributions from a wide range of disciplines, encouraging cross-pollination of ideas and expertise.</p> </li> <li> <p><strong>Practical Applications:</strong> We are dedicated to bridging the gap between theoretical research and real-world applications. Our journal seeks contributions that have the potential to drive meaningful advancements in industrial processes, automation, and smart manufacturing.</p> </li> <li> <p><strong>Industry Partnerships:</strong> We actively encourage collaboration between academia and industry. Our goal is to facilitate the exchange of ideas, best practices, and industry insights, ultimately fostering innovation and technological advancements in the industrial sector.</p> </li> </ol> <p><strong>Key Topics and Areas of Interest:</strong></p> <p>The "Transactions on Recent Developments in Industrial IoT" covers a broad spectrum of topics, including but not limited to:</p> <ul> <li>IoT-based Industrial Automation</li> <li>Cyber-Physical Systems (CPS)</li> <li>Smart Manufacturing and Industry 4.0</li> <li>Industrial Data Analytics</li> <li>IoT Security and Privacy in Industrial Contexts</li> <li>Wireless Sensor Networks for Industrial Applications</li> <li>Edge and Fog Computing in IIoT</li> <li>AI and Machine Learning for Industrial Predictive Maintenance</li> <li>Cloud-Based IIoT Solutions</li> <li>Industrial Communication Protocols and Standards</li> <li>Supply Chain Optimization with IIoT</li> <li>Energy Efficiency and Sustainability in Industrial Processes</li> <li>Case Studies and Practical Implementations</li> </ul> <p><strong>Publication Format:</strong></p> <p>The journal publishes research articles, reviews, case studies, and technical notes. We encourage authors to provide practical insights and real-world use cases to make the research more applicable to industry professionals.</p> <p><strong>Review Process:</strong></p> <p>All submissions undergo a rigorous peer-review process, ensuring that published articles meet high standards of quality, originality, and relevance.</p> <p><strong>Audience:</strong></p> <p>Our primary audience includes researchers, academics, industry professionals, and policymakers interested in the latest developments and innovations in Industrial IoT. We aim to provide a platform for knowledge exchange and collaboration among these key stakeholders.</p> <p><strong>Publication Frequency:</strong></p> <p>The journal is published on a quarterly basis, providing readers with a regular influx of the latest research and developments in the field.</p> <p><strong>Join us in exploring the exciting world of Industrial IoT, where the digital and physical realms converge to reshape industrial processes, enhance efficiency, and drive innovation.</strong></p> <div><strong><em><br /></em> <span style="font-size: 0.875rem;">TRDAIoT</span> </strong>does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented.</div> <div> </div> <div>Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.</div> <p><em><strong><span style="font-size: 0.875rem;">TRDAIoT </span><span style="font-size: 0.875rem;">is a double-blind peer-reviewed journal indexed in several databases like google scholar, Wos, Dooj, EI </span></strong></em></p> <div id="journalDescription"> <p> </p> <p>JCR Impact Factor: 4.6 (2019)</p> <p>JCR Impact Factor: 5.6 (2020)</p> <p>JCR Impact Factor: 5.9 (2021)</p> <p>JCR Impact Factor : 6.1 (2022)</p> <p>JCR Impact Factor : Under Evaluation (2023)</p> </div>https://journals.threws.com/index.php/TRDAIoT/article/view/243Adaptive IoT Architectures for Dynamic Smart Home Environments2024-07-03T10:54:34+00:00Mehak KapoorKapoor@gmail.com<p>The evolution of smart home environments necessitates adaptive IoT architectures that can respond to dynamic user needs and environmental conditions. This paper explores the development of adaptive IoT frameworks for smart homes, emphasizing modular and scalable design principles. We present a prototype system that integrates various IoT devices, sensors, and actuators, managed through a central hub with machine learning capabilities. The system's performance is evaluated in real-world scenarios, demonstrating its ability to optimize energy consumption, enhance user comfort, and improve security. The study highlights the potential of adaptive IoT architectures to create responsive and personalized smart home experiences.</p> <p> </p> <p> </p>2024-07-03T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/244IoT-Based Smart Agriculture: Enhancing Crop Management and Yield Prediction2024-07-03T10:56:48+00:00Tajeev Khankhan@gmail.com<p>The application of IoT in agriculture, known as smart agriculture, has the potential to significantly improve crop management and yield prediction. This paper investigates the implementation of an IoT-based smart agriculture system that monitors soil moisture, temperature, humidity, and crop health through a network of sensors. Data collected from the sensors is analyzed using machine learning algorithms to provide farmers with actionable insights and predictive analytics. Field trials conducted over multiple growing seasons demonstrate the system's effectiveness in optimizing irrigation, reducing resource wastage, and increasing crop yields. The paper also discusses the challenges and future directions for IoT in agriculture, including scalability, cost, and data privacy concerns.</p> <p> </p>2024-07-03T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/245 Improving Predictive Analytics with Ensemble Machine Learning Models2024-07-03T11:06:11+00:00Dr. Mahesh Varmavarma@gmail.com<p>Predictive analytics has become a cornerstone in various domains, including finance, healthcare, and marketing, leveraging historical data to forecast future trends. This paper explores the enhancement of predictive analytics using ensemble machine learning models, which combine multiple learning algorithms to improve accuracy and robustness. We review different ensemble methods, including bagging, boosting, and stacking, and their applications in real-world scenarios. The study presents a comparative analysis of these methods using datasets from different sectors, highlighting their strengths and limitations. Our findings demonstrate that ensemble models significantly outperform individual models, providing more reliable predictions and greater generalizability.</p> <p> </p>2024-07-01T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/246Deep Learning Techniques for Natural Language Processing: Advances and Applications2024-07-03T11:09:05+00:00Prof. Dinesh Mehramehar@gmail.com<p>The advent of deep learning has revolutionized natural language processing (NLP), enabling significant advancements in tasks such as machine translation, sentiment analysis, and text generation. This paper provides a comprehensive review of state-of-the-art deep learning techniques in NLP, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. We discuss the theoretical foundations of these models, their architectural innovations, and their impact on NLP performance. Through case studies, we illustrate the applications of deep learning in various NLP tasks and highlight the challenges, such as data scarcity and computational requirements. The paper concludes with an outlook on future trends and research directions in deep learning for NLP.</p> <p> </p>2024-07-01T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/298Blockchain for Securing Artificial Intelligence Training Data in Healthcare2024-12-09T09:00:35+00:00Prof. Rajni Singhrajni@gmail.com<p>This paper explores the integration of blockchain technology to secure training datasets used in developing AI models for healthcare applications. The immutable nature of blockchain ensures the integrity and provenance of training data, mitigating risks such as poisoning and tampering. A prototype system is evaluated in the context of AI-powered radiology, demonstrating its potential to uphold data authenticity and improve model performance. The study advocates for the adoption of blockchain as a standard for secure AI development.</p> <p> </p>2024-12-05T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/299AI and Blockchain-Enabled Cyber Risk Scoring for Healthcare Enterprises2024-12-09T09:01:57+00:00Prof. Kishan Singhsingh@gmail.com<p>This paper presents a comprehensive cyber risk scoring system powered by AI and blockchain, designed for healthcare organizations. AI algorithms assess potential vulnerabilities based on historical threat data, while blockchain secures the scoring process and provides a transparent record for compliance purposes. The system’s efficacy is tested in real-world healthcare scenarios, showing its ability to deliver precise risk assessments and actionable recommendations. The research emphasizes the importance of adopting innovative tools to strengthen cybersecurity frameworks in the healthcare sector.</p>2024-12-05T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/310Generative Adversarial Networks for Data Augmentation in Low-Resource Machine Learning Applications2024-12-14T11:09:45+00:00Dr. Koul Sharmakour@gmail.com<p>The performance of machine learning models is often limited by the availability of labeled data, particularly in low-resource domains like healthcare, agriculture, and disaster management. This paper investigates the use of Generative Adversarial Networks (GANs) for synthetic data generation to augment training datasets. By learning the underlying data distribution, GANs produce realistic and diverse samples that enhance model generalization. The proposed approach is evaluated on multiple low-resource datasets, demonstrating significant improvements in classification and prediction tasks. Additionally, the study highlights the role of GANs in mitigating class imbalance, a common issue in real-world datasets. This research underscores the potential of GANs as a cost-effective and efficient solution for addressing data scarcity in machine learning.</p>2024-12-12T00:00:00+00:00Copyright (c) 2024 https://journals.threws.com/index.php/TRDAIoT/article/view/311Multi-Agent Reinforcement Learning for Autonomous Traffic Management in Smart Cities2024-12-14T11:11:14+00:00Prof. Radhima Sharmasharma@gmail.com<p>The increasing urban population has led to significant traffic congestion, necessitating intelligent solutions for efficient traffic management. This paper presents a multi-agent reinforcement learning framework for autonomous traffic signal control in smart cities. The proposed system enables traffic lights to collaborate and adapt to real-time conditions, optimizing traffic flow and reducing delays. Each agent employs a reward mechanism based on vehicle throughput and waiting times, ensuring a balance between individual intersections and the overall network. Simulations on urban traffic datasets reveal that the framework significantly outperforms traditional fixed-timing and adaptive control systems, reducing congestion and emissions. The results demonstrate the potential of multi-agent systems to transform urban mobility and contribute to sustainable smart city development.</p>2024-12-11T00:00:00+00:00Copyright (c) 2024