NEXT-GENERATION NETWORK MANAGEMENT IN INDIA: AN AI-DRIVEN FRAMEWORK INTEGRATING 5G, SDN, AND EDGE COMPUTING

Authors

  • Praveen Kumar Department of Computer Science and Engineering, Meerut Institute Of Technology, Meerut, UP, India
  • Saba Hashmi Faculty of Management and Commerce, Meerut Institute Of Technology, Meerut, UP, India
  • Preeti Singhwal Faculty of Management and Commerce, Swami Vivekanand Subharti University, Meerut, UP, India
  • Sonam Tomar Department of Computer Application , Swami Vivekanand Subharti University, Meerut, UP, India
  • Kriti Aggarwal Department of Applied Science, Meerut Institute Of Technology, Meerut, UP, India
  • Neha Kumari Department of Computer Science and Engineering, Meerut Institute Of Technology, Meerut, UP, India

DOI:

https://doi.org/10.70917/ijcisim-2026-5003

Keywords:

Software-Defined Networking, Edge Computing, Artificial Intelligence, Network Orchestration, Network Slicing, Smart Infrastructure, India, Quality of Service

Abstract

The telecommunication industry in India is undergoing a structural change, with the deployment of the 5G network, the widespread deployment of smart devices for the Digital India initiative and the need for ultra-low latencies, high-speed services for smart cities, healthcare, agriculture and industrial automation. Traditional network management methods, which are mostly based on static configuration and human intervention are increasingly proving to be insufficient for the scale, diversity and dynamism of these networks. This paper introduces an Artificial Intelligence (AI) centric network management framework that combines 5G radio access and core capabilities, Software-Defined Networking (SDN) for centralized and programmable control and Multi-access Edge Computing (MEC) for localized and low latency processing. This framework includes a traffic prediction module (based on Long Short-Term Memory (LSTM)) and a resource orchestration engine (Deep Q-Network (DQN)), both of which allow for closed-loop self-optimizing network actions. Mininet-WiFi and Ryu SDN controller were used to simulate a representative Indian metropolitan network topology and assess the proposed framework in comparison to a baseline configuration with conventional SDN. Experimental results show significant gains in end-to-end latency (53.3 %), throughput (53.2 %), jitter (60.7 %), packet loss (72.4 %) and resource-utilization efficiency (42.6 %). The results indicate that 5G-SDN-edge continuum orchestration can positively impact the Quality of Service (QoS) in India-specific use cases such as dense urban areas and rural areas with limited infrastructure. The paper also elaborates the deployment challenges pertinent to the Indian context like spectrum availability, fiber backhaul penetration, cost of edge infrastructure, and regulatory considerations, while proposing the directions for future research like federated learning in orchestrating with privacy constraints and upcoming 6G research initiatives.

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Published

2026-08-23

How to Cite

Praveen Kumar, Saba Hashmi, Preeti Singhwal, Sonam Tomar, Kriti Aggarwal, & Neha Kumari. (2026). NEXT-GENERATION NETWORK MANAGEMENT IN INDIA: AN AI-DRIVEN FRAMEWORK INTEGRATING 5G, SDN, AND EDGE COMPUTING. International Journal of Computer Information Systems and Industrial Management Applications, 18(19s), 104–116. https://doi.org/10.70917/ijcisim-2026-5003

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Section

Original Articles