NETWORK AUTOMATION IN IOT: A TECHNICAL REVIEW OF CHALLENGES, OPPORTUNITIES, AND FUTURE DIRECTIONS
Keywords:
Network Automation, Internet Of Things, Software-Defined Networking, Edge Computing, Artificial IntelligenceAbstract
This comprehensive article examines the evolution and current state of network automation in Internet of Things (IoT) environments, focusing on the challenges, opportunities, and future directions. The article analyzes the transformation from traditional network management approaches to modern automation frameworks, highlighting the critical role of Software-Defined Networking (SDN) and Network Function Virtualization (NFV). It explores the ETSI Generic Autonomic Network Architecture (GANA) framework and its multi-level abstraction model for network automation. The article also investigates key enabling technologies, including machine learning, microservices architecture, multi-agent systems, containerization, and cloudification. Through detailed analysis of current research and implementations, the article identifies technical challenges, research opportunities, and emerging trends in network automation, particularly focusing on the convergence of edge computing and artificial intelligence.
References
IoTforall, "IoT 2022: Connected Devices Growing 18% to 14.4 Billion Globally," IoT For All, Dec. 2024. [Online]. Available: https://www.iotforall.com/state-of-iot-2022
Redowan Mahmud et al., "Quality of Experience (QoE)-aware placement of applications in Fog computing environments," ACM Digital Library Volume 132, Issue C, Oct. 2019. [Online]. Available: https://dl.acm.org/doi/10.1016/j.jpdc.2018.03.004
Qiao Yan et al., "Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey”, IEEE Communications Surveys & Tutorials, Volume 18, Issue 1, Oct. 2015. [Online]. Available: https://ieeexplore.ieee.org/document/7289347
Rashid Mijumbi et al., "Network Function Virtualization: State-of-the-Art and Research Challenges," IEEE Communications Surveys & Tutorials, Volume 18, Issue 1, Sep. 2015. [Online]. Available: https://ieeexplore.ieee.org/document/7243304
Ranganai Chaparadza et al., "Implementation Guide for the ETSI AFI GANA model: A Standardized Reference Model for Autonomic Networking, Cognitive Networking and Self-Management," Research Gate, Dec. 2013. [Online]. Available: https://www.researchgate.net/publication/271461609_Implementation_Guide_for_the_ETSI_AFI_GANA_model_A_Standardized_Reference_Model_for_Autonomic_Networking_Cognitive_Networking_and_Self-Management
Cristian Cleder Machado et al., "Towards SLA Policy Refinement for QoS Management in Software-Defined Networking," 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, June 2014. [Online]. Available: https://ieeexplore.ieee.org/document/6838692
Nick Feamster and Jennifer Rexford, "Why (and How) Networks Should Run Themselves," ANRW '18: Proceedings of the 2018 Applied Networking Research Workshop, July 2018. [Online]. Available: https://dl.acm.org/doi/10.1145/3232755.3234555
Raouf Boutaba et al., "A comprehensive survey on machine learning for networking: evolution, applications and research opportunities," Journal of Internet Services and Applications volume 9, Article number 16, 2018. [Online]. Available: https://jisajournal.springeropen.com/articles/10.1186/s13174-018-0087-2
Xiaofei Wang et al., "Convergence of Edge Computing and Deep Learning: A Comprehensive Survey," IEEE Communications Surveys & Tutorials, Volume 22, Issue 2, Jan 2020. [Online]. Available: https://ieeexplore.ieee.org/document/8976180
Mingzhe Chen et al., "Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial," IEEE Communications Surveys & Tutorials, Volume 21, Issue 4, July 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8755300
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Copyright (c) 2025 Sai Charan Madugula (Author)
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