https://journals.threws.com/index.php/TRDAIoT <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> The research world en-US Transaction on Recent Developments in Industrial IoT https://journals.threws.com/index.php/TRDAIoT/article/view/350 <p>Climate change poses a global challenge, and AI-driven solutions are playing a crucial role in mitigating its impact. This review examines ML applications in climate science, including predictive climate modeling, carbon footprint reduction, and energy optimization. We discuss recent advancements in AI-based satellite imagery analysis, renewable energy forecasting, and climate risk assessment. Finally, we outline the limitations and potential of AI in driving sustainable environmental policies and decision-making.</p> Prof. Bhim Shing Copyright (c) 2025 2025-01-01 2025-01-01 17 17 https://journals.threws.com/index.php/TRDAIoT/article/view/351 <p>Generative AI, powered by deep learning techniques such as GANs, VAEs, and Transformers, has revolutionized content creation in areas like text generation, image synthesis, and music composition. This paper reviews key advancements in generative models, including GPT, Stable Diffusion, and StyleGAN, and explores their applications in entertainment, marketing, and education. We also discuss ethical concerns such as deepfakes, intellectual property issues, and bias in AI-generated content, along with potential solutions for responsible AI deployment.</p> <p>&nbsp;</p> Prof. Sandeep Makan Copyright (c) 2025 2025-01-02 2025-01-02 17 17 https://journals.threws.com/index.php/TRDAIoT/article/view/387 <p>Multi-tenancy is a defining feature of cloud computing, allowing multiple customers to share resources within the same infrastructure. While this enhances resource utilization and cost efficiency, it also introduces significant security concerns, such as data leakage, unauthorized access, and resource contention. This paper investigates the key security challenges associated with multi-tenancy in cloud environments and proposes a novel framework for secure tenant isolation. The framework leverages cryptographic techniques, secure hypervisors, and policy-based access controls to ensure data confidentiality and integrity. Through a series of simulations and real-world case studies, the proposed solution demonstrates improved resistance to cross-tenant attacks and enhanced overall security posture. The study concludes with recommendations for cloud service providers to strengthen multi-tenant security.</p> Somnath Banerjee Sunil Kumar Parisa Copyright (c) 2025 2025-01-15 2025-01-15 17 17 https://journals.threws.com/index.php/TRDAIoT/article/view/396 <p>The exponential growth of AI applications has raised concerns about their environmental impact, particularly in terms of energy consumption and carbon emissions. This paper investigates the concept of "Green AI"—developing energy-efficient AI models while maintaining high performance. It explores techniques such as model pruning, federated learning, and sustainable data centers that reduce AI’s carbon footprint. The study also evaluates trade-offs between AI advancements and ecological responsibility, providing insights into optimizing AI frameworks for sustainability. Case studies of tech companies implementing Green AI initiatives are discussed, along with future directions for eco-friendly AI development.</p> Pawan Whig Copyright (c) 2025 2025-01-17 2025-01-17 17 17