Email this article Exploring the Intersection of Fog Computing and Responsible AI Governance: A Bibliometric Analysis
Abstract
The existing research lacks comprehensive frameworks and methodologies personalized to the exclusive challenges of fog-adding, such as resource constraints and data privacy concerns. This research training grants a complete bibliometric study of the integration of Responsible AI Governance (RAG) principles within the domain of fog computing. Leveraging data from 800 academic articles sourced from prominent files such as IEEE-Xplore, ACM Digital-Library, Elsevier, and others, the research employs the VOSviewer tool to analyze publication trends, citation patterns, and keyword co-occurrence. The analysis uncovers significant research trends, influential contributors, and key thematic areas at the intersection of fog computing and RAG. Key findings highlight the growing emphasis on ethical considerations such as transparency, fairness, Accountability, and privacy in fog computing. The study also identifies critical research gaps in adaptive RAG models tailored for resource-constrained fog environments, the potential of federated learning for privacy-preserving RAG integration, and hybrid approaches combining RAG with edge AI. These visions offer a foundation for upcoming study directions to address the challenges of applying responsible AI in decentralized fog-calculating environments.
Full Text:
PDFReferences
Abbas, A., Khan, S. U., & Zomaya, A. Y. (2020). Fog computing: Theory and practice. Retrieved from https://books.google.com/books?hl=en&lr=&id=AbjWDwAAQBAJ&oi=fnd&pg=PR23&dq=fog+computing+edge+computing+fog+architecture+fog+network+responsible+ai+ai+governance&ots=TlNbe7TEbp&sig=FN5gD2R2FqJxrJOCHqqBjb9j4AE
Abedi, M., & Pourkiani, M. (2020). Resource allocation in combined fog-cloud scenarios by using artificial intelligence. IEEE International Conference on Fog and Mobile Edge Computing. Retrieved from https://ieeexplore.ieee.org/abstract/document/9144693/
Alli, A. A., & Alam, M. M. (2020). The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet of Things. Elsevier. https://www.sciencedirect.com/science/article/pii/S2542660520300172
Al-Khafajiy, M., Baker, T., Asim, M., Guo, Z., & Ranjan, R. (2020). COMITMENT: A fog computing trust management approach. Future Generation Computer Systems. Elsevier. https://www.sciencedirect.com/science/article/pii/S0743731519302965
Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things. Elsevier. https://www.sciencedirect.com/science/article/pii/S2542660520301062
Badidi, E., Mahrez, Z., & Sabir, E. (2020). Fog computing for smart cities' big data management and analytics: A review. Future Internet. MDPI. https://www.mdpi.com/1999-5903/12/11/190
Hammoud, A., Sami, H., Mourad, A., & Otrok, H. (2020). AI, blockchain, and vehicular edge computing for smart and secure IoV: Challenges and directions. IEEE Internet of Things Journal. IEEE. https://ieeexplore.ieee.org/abstract/document/9125437/
Hernández-Nieves, E., & Hernández, G. (2020). Fog computing architecture for personalized recommendation of banking products. Expert Systems with Applications. Elsevier. https://www.sciencedirect.com/science/article/pii/S0957417419306189
Huang, L., Rihan, M., Elwekeil, M., & Yang, Y. (2020). Deep-VFog: When artificial intelligence meets fog computing in V2X. IEEE Systems Journal. IEEE. https://ieeexplore.ieee.org/abstract/document/9171442/
Jiang, X., Guo, Y., Ren, H., & Sun, L. (2020). Edge-cloud computing and artificial intelligence in internet of medical things: Architecture, technology and application. IEEE Access. IEEE. https://ieeexplore.ieee.org/abstract/document/9099795/
Liao, S., Wu, J., Mumtaz, S., Li, J., & Morello, R. (2020). Cognitive balance for fog computing resource in Internet of Things: An edge learning approach. IEEE International Conference on Mobile Computing. IEEE. https://ieeexplore.ieee.org/abstract/document/9205577/
Ma, H., Yang, K., & Dou, S. (2020). Fog intelligence for network anomaly detection. IEEE Network. IEEE. https://ieeexplore.ieee.org/abstract/document/9055742/
Mahmud, R., Ramamohanarao, K., & Buyya, R. (2020). Application management in fog computing environments: A taxonomy, review and future directions. ACM Computing Surveys. ACM. https://dl.acm.org/doi/abs/10.1145/3403955
Mutlag, A. A., Khanapi Abd Ghani, M., & Mohammed, M. A. (2020). MAFC: Multi-agent fog computing model for healthcare critical tasks management. Sensors. MDPI. https://www.mdpi.com/1424-8220/20/7/1853
Moura, J., & Hutchison, D. (2020). Fog computing systems: State of the art, research issues and future trends, with a focus on resilience. Journal of Network and Computer Applications. Elsevier. https://www.sciencedirect.com/science/article/pii/S1084804520302587
Peng, M., Zhao, Z., & Sun, Y. (2020). System architecture of fog radio access networks. Fog Radio Access Networks. Springer. https://link.springer.com/chapter/10.1007/978-3-030-50735-0_2
Rihan, M., Elwekeil, M., Yang, Y., & Huang, L. (2020). Deep-VFog: When artificial intelligence meets fog computing in V2X. IEEE Systems Journal. IEEE. https://ieeexplore.ieee.org/abstract/document/9171442/
Shamseddine, H., Nizam, J., & Hammoud, A. (2020). A novel federated fog architecture embedding intelligent formation. IEEE Network. IEEE. https://ieeexplore.ieee.org/abstract/document/9220179/
Songhorabadi, M., Rahimi, M., & Farid, A. M. M. (2020). Fog computing approaches in smart cities: A state-of-the-art review. arXiv preprint arXiv. Retrieved from https://arxiv.org/abs/2011.14732
Sunyaev, A., & Sunyaev, A. (2020). Fog and edge computing. Internet Computing: Principles of Distributed Systems. Springer. https://link.springer.com/chapter/10.1007/978-3-030-34957-8_8
Vilela, P. H., Rodrigues, J. J. P. C., Righi, R. R., & Kozlov, S. (2020). Looking at fog computing for e-health through the lens of deployment challenges and applications. Sensors. MDPI. https://www.mdpi.com/1424-8220/20/9/2553
Wang, H., Liu, T., Kim, B. G., & Lin, C. W. (2020). Architectural design alternatives based on cloud/edge/fog computing for connected vehicles. IEEE Surveys & Tutorials. IEEE. https://ieeexplore.ieee.org/abstract/document/9184917/
Wu, Y. (2020). Cloud-edge orchestration for the Internet of Things: Architecture and AI-powered data processing. IEEE Internet of Things Journal. IEEE. https://ieeexplore.ieee.org/abstract/document/9162084/
Xu, Z., Zhang, Y., Li, H., Yang, W., & Qi, Q. (2020). Dynamic resource provisioning for cyber-physical systems in cloud-fog-edge computing. Journal of Cloud Computing. Springer. https://link.springer.com/article/10.1186/s13677-020-00181-y
Yang, Y., Huang, J., Zhang, T., & Weinman, J. (2020). Fog and fogonomics: Challenges and practices of fog computing, communication, networking, strategy, and economics. Retrieved from https://books.google.com/books?hl=en&lr=&id=jl3IDwAAQBAJ&oi=fnd&pg=PR17&dq=fog+computing+edge+computing+fog+architecture+fog+network+responsible+ai+ai+governance&ots=Zz-Fr4E-FD&sig=Itpkr2SggHuZxdcUVeEv52XXbFI
Zhang, C. (2020). Design and application of fog computing and Internet of Things service platform for smart city. Future Generation Computer Systems. Elsevier. https://www.sciencedirect.com/science/article/pii/S0167739X19331024
Yang, Y., Luo, X., Chu, X., Zhou, M. T., & Luo, X. (2020). Fog computing architecture and technologies. In Fog-enabled intelligent systems (pp. 15-45). Springer. https://link.springer.com/chapter/10.1007/978-3-030-23185-9_2
Zhang, C., & Yang, Y. (2020). The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet of Things. Elsevier. https://www.sciencedirect.com/science/article/pii/S2542660520300172
Hamdan, S., Ayyash, M., & Almajali, S. (2020). Edge-computing architectures for internet of things applications: A survey. Sensors. MDPI. https://www.mdpi.com/1424-8220/20/15/4311
Saeed, S., & Liu, H. (2020). AI-driven fog computing for autonomous driving: Challenges and opportunities. IEEE Transactions on Intelligent Transportation Systems. IEEE. https://ieeexplore.ieee.org/abstract/document/9247641/
Diao, X., Wang, M., Zheng, J., & Cai, Y. (2020). Fairness-aware offloading and trajectory optimization for multi-UAV enabled multi-access edge computing. IEEE Access, 8, 124359-124370.
Alli, A. A., & Alam, M. M. (2020). The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet of Things, 9, 100177.
de Moura Costa, H. J., da Costa, C. A., da Rosa Righi, R., & Antunes, R. S. (2020). Fog computing in health: A systematic literature review. Health and Technology, 10(5), 1025-1044.
Singh, S. K., & Dhurandher, S. K. (2020, December). Architecture of fog computing, issues and challenges: A review. In 2020 IEEE 17th India Council International Conference (INDICON) (pp. 1-6). IEEE.
Minoli, D., & Occhiogrosso, B. (2020). Blockchain concepts, architectures, and Smart city applications in fog and edge computing environments. In Blockchain-enabled Fog and Edge Computing: Concepts, Architectures and Applications (pp. 31-78). CRC Press.
Wang, Q., Zhao, H., Wang, Q., Cao, H., Aujla, G. S., & Zhu, H. (2020). Enabling secure wireless multimedia resource pricing using consortium blockchains. Future Generation Computer Systems, 110, 696-707.
Markus, A., & Kertesz, A. (2020). A survey and taxonomy of simulation environments modelling fog computing. Simulation Modelling Practice and Theory, 101, 102042.
Rastogi, R., Saxena, M., Chaturvedi, D. K., Satya, S., Arora, N., Gupta, M., & Singhal, P. (2020). Fog Data Based Statistical Analysis to Check Effects of Yajna and Mantra Science: Next Generation Health Practices. Fog Data Analytics for IoT Applications: Next Generation Process Model with State of the Art Technologies, 145-172.
Hassan, T., Akram, M. U., Werghi, N., & Nazir, M. N. (2020). RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology. IEEE journal of biomedical and health informatics, 25(1), 108-120.
Khan, Z., & Yang, J. (2020). Bottom-up unsupervised image segmentation using FC-Dense u-net based deep representation clustering and multidimensional feature fusion based region merging. Image and Vision Computing, 94, 103871.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 International Journal of Sustainable Development in Computing Science
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
A Double-Blind Peer Reviewed Journal