Edge Computing for Industrial IoT: Investigating how edge computing can enhance productivity and efficiency in industrial settings
Keywords:
Edge ComputingAbstract
This research paper explores the integration of edge computing in industrial Internet of Things (IoT) environments to enhance productivity and efficiency. Edge computing enables data processing and analysis closer to the data source, reducing latency and bandwidth usage. The paper discusses the key benefits of edge computing in industrial settings, including improved real-time decision-making, enhanced data security, and increased operational efficiency. Various edge computing architectures and technologies suitable for industrial IoT applications are analyzed, highlighting their strengths and limitations. Case studies and examples are presented to illustrate the practical implementation and benefits of edge computing in different industrial sectors. Overall, this paper aims to provide insights into how edge computing can revolutionize industrial IoT operations, leading to smarter and more efficient industrial processes.
Downloads
References
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Navigating the Modernization of Legacy Applications and Data: Effective Strategies and Best Practices." Asian Journal of Research in Computer Science 16.4 (2023): 239-256.
Shahane, Vishal. "Serverless Computing in Cloud Environments: Architectural Patterns, Performance Optimization Strategies, and Deployment Best Practices." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 23-43.
Muthusubramanian, Muthukrishnan, and Jawaharbabu Jeyaraman. "Data Engineering Innovations: Exploring the Intersection with Cloud Computing, Machine Learning, and AI." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 76-84.
Tillu, Ravish, Bhargav Kumar Konidena, and Vathsala Periyasamy. "Navigating Regulatory Complexity: Leveraging AI/ML for Accurate Reporting." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 149-166.
Sharma, Kapil Kumar, Manish Tomar, and Anish Tadimarri. "Optimizing sales funnel efficiency: Deep learning techniques for lead scoring." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 261-274.
Abouelyazid, Mahmoud. "Machine Learning Algorithms for Dynamic Resource Allocation in Cloud Computing: Optimization Techniques and Real-World Applications." Journal of AI-Assisted Scientific Discovery 1.2 (2021): 1-58.
Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.
Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.
