Journal of Telecommunication, Switching Systems and Networks

Software-defined networking (SDN) separates control logic from packet-forwarding devices and provides a programmable substrate for traffic engineering. Machine learning (ML), deep learning, and reinforcement learning have consequently been explored for route selection, congestion mitigation, load balancing, and adaptive network control.

The rapid growth of connected devices, heterogeneous applications, immersive services, and intelligent edge systems has increased the complexity of resource management in fifth generation (5G) and emerging sixth generation (6G) wireless networks. Radio spectrum, transmit power, bandwidth, computing resources, network slices, and user association must be dynamically allocated while satisfying conflicting requirements for throughput, latency, reliability, fairness, and energy efficiency.

Cloud storage has become a basic service for enterprises, educational institutions, digital platforms and electrical/electronic systems that continuously generate data. Its convenience, scalability and remote accessibility also increase exposure to credential theft, ransomware, accidental sharing, malicious insiders, misconfiguration and large-scale data leakage.

Security Operations Centers (SOCs) must interpret large volumes of alerts, logs, endpoint events, identity signals, threat intelligence, and cloud activity while responding quickly to incidents.

Rapid urbanisation has created a situation in which traffic congestion has become one of the major problems affecting many aspects such as, mobility, safety and environmental quality.

The rapid evolution of sixth-generation (6G) wireless communication networks demands intelligent and adaptive network management mechanisms capable of supporting ultra-low-latency, massive connectivity, high reliability, and heterogeneous service requirements.