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

Towards a Reference Architecture for Intelligent Anomaly Detection in Software-Defined Networks

Emerging technologies such as Cloud Computing, 5G, the Internet of Things (IoT), and Edge Computing demand the management of large-scale and highly dynamic network infrastructures. Traditional network configuration does not scale efficiently, whereas Software-Defined Networking (SDN) enables centralized control and simplified management. Despite these benefits, SDN environments still face significant challenges related to security and fine-grained anomaly detection. Several studies have demonstrated the effectiveness of computational intelligence (CI) techniques for anomaly detection in SDN. However, the diversity of network anomalies and CI-based solutions introduces substantial heterogeneity, making model selection and integration challenging. This paper proposes a reference architecture designed to validate, promote, and explain the suitability of different CI techniques for distinct network anomaly scenarios. The proposed architecture adopts a hexagonal microservices design and a unified information model aligned with the application, information, and process layers of the TM Forum Open Digital Architecture (ODA). Validation was performed through a proofof-concept prototype using two datasets and seven machine learning algorithms. The results demonstrate the importance of architectural flexibility, enabling the dynamic integration and replacement of CI models to support adaptive and scalable SDN anomaly detection.

Intelligent Placement of 5G Network Functions on Edge-Based Infrastructures

A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.

AI-Driven Automation and Self-Healing Mechanisms in 5G Core Networks for Reliable Telecom Operations under Vision 2030

The study presents an overview of automation technologies that are self-healing methods applied in stand-alone 5G Core telecommunications systems required for providing reliable communications in Saudi Vision 2030 conditions. Literature surveys covering the period 2020-2025 have been done in research domains like cloud-native 5G Core design, SBA, NDAF roles, closed-loop assurance, AI-based anomalies detection and localization, root cause analysis, policy management, edge computing, and automation. It is shown in the paper that the 5G Core is much more than a pure packet router and, in fact, represents a state-of-the-art platform which allows predicting network performance degradation, localize faults, and execute repair processes without any impact on user services and industrial operations. The focus of the review is on Saudi Arabia, where the country's oil and gas industry, mining sector, transport, manufacturing industries, smart city initiatives, and giga projects require reliable connectivity, deterministic performance, and secure digital infrastructure. Based on the findings of the literature survey, it is possible to conclude that the self-healing feature of stand-alone 5G Core can be achieved with the help of the following three capabilities being implemented jointly: telemetry trustworthiness, explainable AI for decision-making, and automation policies enforcement. In the proposed Saudi Arabia-specific solution, the relationship between NWDAF, cloud-native NFs, MEC, slicing assurance, and operation management will be discussed.

Lifelong Learning-Based SDN Design for Dynamic Configuration and Resource Allocation in Satellite–Terrestrial Networks

Satellite-airborne-terrestrial edge computing networks (SATECNs) emerge as a global solution for Internet of Things (IoT) since they can provide global coverage even in remote areas and under natural disasters. However, their dynamic and non-stationary nature makes control and resource allocation more challenging. Preserving data freshness is crucial in many IoT applications and requires timely decisions. To address these challenges, we present a knowledge-base software-defined networking architecture for satellite–airborne–terrestrial networks (KB-SAT-SDN) that enables collaboration between SDN controllers to optimize SAT configurations. A shared knowledge base (KB) is built through lifelong learning (LL) to continuously adapt and efficiently manage computing and networking resources to minimize the age of information (AoI) and energy consumption. To further accelerate learning, we exploit the heterogeneity of nodes and offloading decisions by defining different learning domains and designing a cross-domain lifelong learning (CDLL-SATECN) algorithm. With domain-specific projections, knowledge is shared between domains. Numerical results show that CDLL reduces average AoI and energy by up to 70% and converges $8\times $ faster than existing baselines. It achieves the lowest or near-lowest penalty across all RL domains, nearly halving Natural Actor-Critic (NAC)’s penalty in the most complex domains. LEO assistance lowers penalty/AoI from 61.9/49.4 to 48.5/45.1 relative to a domain without LEO, while reducing UAV energy and queues. The sensitivity analysis confirms that CDLL maintains a stable AoI–energy tradeoff over a broad range of weighting parameters.

DATA SCIENCE AND IOT MANAGEMENT SYSTEM

Dual-Layer Over-the-Air Federated Learning in LEO Satellite Networks: Architecture, Key Technologies and Applications

This article outlines the fundamental principles of the dual-layer OTA model and introduces the adaptive BH mechanism designed for time-varying topologies, aiming to provide insights for the evolution of ubiquitous non-terrestrial intelligence.