Blockchain Integration in IoT Systems: Studying the integration of blockchain technology to enhance security and transparency in IoT
Keywords:
Blockchain, Case StudiesAbstract
Blockchain technology has emerged as a promising solution for enhancing security and transparency in various domains, including the Internet of Things (IoT). The integration of blockchain in IoT systems offers several advantages, such as decentralized data storage, immutable data records, and secure transactions. This paper provides a comprehensive review of the integration of blockchain technology in IoT systems, focusing on its impact on security, privacy, and data integrity. We discuss various blockchain platforms suitable for IoT integration, highlight key challenges and solutions, and present case studies illustrating successful implementations. Our analysis shows that blockchain integration in IoT systems can significantly improve security and transparency, making it a valuable technology for the future of IoT applications.
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References
Pulimamidi, Rahul. "Leveraging IoT Devices for Improved Healthcare Accessibility in Remote Areas: An Exploration of Emerging Trends." Internet of Things and Edge Computing Journal 2.1 (2022): 20-30.
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. "Advanced Artificial Intelligence Techniques for Real-Time Predictive Maintenance in Industrial IoT Systems: A Comprehensive Analysis and Framework." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 271-313.
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.
