Adaptive Fog Computing Framework (AFCF): Bridging IoT and Blockchain for Enhanced Data Processing and Security
International Journal of Electronics and Communication Engineering |
© 2024 by SSRG - IJECE Journal |
Volume 11 Issue 3 |
Year of Publication : 2024 |
Authors : K. Vinod Kumar Reddy, Vasavi Bande, Novy Jacob, A. MallaReddy, Sk Khaja Shareef, Sriharsha Vikruthi |
How to Cite?
K. Vinod Kumar Reddy, Vasavi Bande, Novy Jacob, A. MallaReddy, Sk Khaja Shareef, Sriharsha Vikruthi, "Adaptive Fog Computing Framework (AFCF): Bridging IoT and Blockchain for Enhanced Data Processing and Security," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 3, pp. 160-175, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I3P116
Abstract:
This research introduces an Adaptive Fog Computing Framework (AFCF) aimed at enhancing the efficiency and scalability of IoT ecosystems through blockchain technology integration, addressing task allocation, resource management, and task offloading challenges within fog and cloud computing paradigms. Employing simulations, the study utilized task distribution strategies, blockchain stability assessment, and cloud server workload management, demonstrating the framework’s capacity to maintain performance across diverse IoT settings. Quantitative results revealed a cent percent success rate in task processing, with a balanced load distribution at 50% and an average task complexity of 6.47893 in arbitrary units. The system demonstrated a latency of 48.479 milliseconds and a throughput of 2.469697 tasks per timestep, showcasing high scalability (97.8%) and energy efficiency (15.774194 in arbitrary efficiency units), emphasizing the AFCF’s robustness in varied tasks and resource dynamics. The study concludes the AFCF’s potential for real-world IoT applications, highlighting its implications for future research and practical deployment, underscoring its contribution to fog and cloud computing literature and paving the way for further exploration into adaptive computing frameworks.
Keywords:
Fog computing, IoT ecosystem, Blockchain technology, Task allocation, Resource management, Task offloading, Simulation, Cloud computing, Edge computing, Data integrity, Scalability.
References:
[1] Qianqian Liu et al., “Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1-13, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Fausto Vizcaino Naranjo, Jorge L. Acosta Espinoza, and Silvio Machuca Vivar, “Exploring the Fusion of Blockchain and AI for Enhanced Practices in IoT Ecosystems: Opportunities and Challenges,” Fusion: Practice and Applications, vol. 13, no. 2, pp. 52-61, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Rayikanti Anasurya, “Internet of Things (IoT) in Mining: Security Challenges and Best Practices,” International Journal of Computer Engineering in Research Trends, vol. 9, no. 5, pp. 93-98, 2022.
[Publisher Link]
[4] Lynnet Alice Ezra, “Big Data Analytics in Cyber Threat Intelligence: A Comprehensive Literature Survey on Methodologies, Challenges, and Future Directions,” International Journal of Computer Engineering in Research Trends, vol. 10, no. 2, pp. 77-89, 2023.
[Publisher Link]
[5] Galia Novakova Nedeltcheva, and Elena Shoikova, “Models for Innovative IoT Ecosystems,” Proceedings of the International Conference on Big Data and Internet of Thing, pp. 164-168, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Petar Radanliev et al., “COVID-19 what have we Learned? The Rise of Social Machines and Connected Devices in Pandemic Management Following the Concepts of Predictive, Preventive, and Personalised Medicine,” EPMA Journal, vol. 11, pp. 311-332, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Dario De Domenico et al., “Optimal Design and Seismic Performance of Multi-Tuned Mass Damper Inerter (MTMDI) Applied to Adjacent High-Rise Buildings,” Structural Design of Tall and Special Buildings, vol. 29, no. 14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Perry G. An, “Constructing and Dismantling Frameworks of Disease Etiology: The Rise and Fall of Sewer Gas in America, 1870-1910,” Yale Journal of Biology and Medicine, vol. 77, no. 3-4, pp. 75-100, 2004.
[Google Scholar] [Publisher Link]
[9] Zixuan Wang, Haoyang Li, and Fengyuan Yan, “Wink Lens Smart Glasses in Communication Engineering: Catalyst for Metaverse and Future Growth Point,” The Frontiers of Society, Science and Technology, vol. 5, no. 10, pp. 68-76, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Gautham Nayak Seetanadi, and Karl-Erik Årzén, “Routing Using Safe Reinforcement Learning,” 2nd Workshop on Fog Computing and the Internet of Things, pp. 1-10, 2020.
[Google Scholar] [Publisher Link]
[11] G.Chandra Sekhar, and P. Balamurugan, “Block-Chain Compliance for IoT Security: A Survey,” International Journal of Computer Engineering in Research Trends, vol. 7, no. 9, pp. 23-33, 2020.
[Google Scholar] [Publisher Link]
[12] Ahmed Douik et al., “Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications,” IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1201-1224, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yuvraj Sahni et al., “Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things,” IEEE Access, vol. 5, pp. 16441-16458, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nihel Benzaoui, “Beyond Edge Cloud: Distributed Edge Computing,” Optical Fiber Communication Conference (OFC), 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Plabon Bhandari Abhi et al., “A Novel Lightweight Cryptographic Protocol for Securing IoT Devices,” International Journal of Computer Engineering in Research Trends, vol. 10, no. 10, pp. 24-30, 2023.
[CrossRef] [Publisher Link]
[16] John S. Quarlerman, and Smoot Carl‐Mitchell, “The Computing Paradigm Shift,” Journal of Organizational Computing, vol. 3, no. 1, pp. 31-50, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Karen Hammer Thurston, and Daniel Conte de Leon, “The Healthcare IoT Ecosystem: Advantages of Fog Computing Near the Edge,” 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, USA, pp. 51-56, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Kemal Cagri Serdaroglu, Şebnem Baydere, and Boonyarith Saovapakhiran, “Real Time Air Quality Monitoring with Fog Computing Enabled IoT System: An Experimental Study,” 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, pp. 147-152, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Taj-Aldeen Naser Abdali et al., “Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues,” IEEE Access, vol. 9, pp. 75961-75980, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Hoa Tran-Dang, and Dong-Seong Kim, “A Many-to-One Matching Based Task Offloading (MATO) Scheme for Fog Computing-Enabled IoT Systems,” 2022 International Conference on Advanced Technologies for Communications (ATC), Ha Noi, Vietnam, pp. 239-244, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Samodha Pallewatta, Vassilis Kostakos, and Rajkumar Buyya, “Placement of Microservices-Based IoT Applications in Fog Computing: A Taxonomy and Future Directions,” ACM Computing Surveys, vol. 55, no. 14s, pp. 1-43, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Vinay Kumar Calastry Ramesh, Yoohwan Kim, and Ju-Yeon Jo, “Secure IoT Data Management in a Private Ethereum Blockchain,” 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, pp. 369-375, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hongyan Cui et al., “IoT Data Management and Lineage Traceability: A Blockchain-Based Solution,” 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops), Changchun, China, pp. 239-244, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] T. Joshva Devadas, S. Thayammal, and A. Ramprakash, “IoT Data Management, Data Aggregation and Dissemination,” Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, pp. 385-411, 2019.
[CrossRef] [Google Scholar] [Publisher Link]