Accurate internet traffic prediction is a key challenge in managing next-generation networks such as 6G. This paper presents a novel approach based on Graph Neural Networks (GNNs) for predicting internet traffic in 6G networks. The proposed model integrates Graph Attention Networks (GAT) and Transformer architectures to learn spatial and temporal dependencies in traffic data. A K-Nearest Neighbors (KNN)-based graph construction method is utilized to represent spatial relationships between network cells. The model’s performance is enhanced by leveraging a Genetic Algorithm (GA) for hyperparameter optimization. Experimental results demonstrate the effectiveness of the proposed model in achieving superior prediction accuracy, as evidenced by improvements in RMSE, and MAE compared to baseline models. This work offers a scalable solution for traffic prediction in 6G networks.

5G networks have vitally contributed to meeting quality of service (QoS) requirements for IoT and traditional communication networks. Radio Access Networks in the 5G Infrastructure comprise radio base stations that communicate over wireless radio links. Wireless radio links have been found to be prone to weather changes. This can lead to link failure and interrupt communication. In this paper, we used Graph Attention Networks, a type of Graph Neural Networks on historical weather data and radio link site characteristics to predict radio link failure on a real telecom dataset. We compare our model with commonly used machine learning models such as support vector machines (SVM), logistic regression (LR) and Long Short-term Memory (LSTM). Our model achieves an F1-score of 0.717 performing significantly better than the other models.

Network survivability is a major and critical con-cern in the design and operation of Wavelength Division Multi-plexing (WDM) networks. The vulnerability of these networks to various external (e.g. natural disaster, human accidents) or internal (e.g. equipment aging, power failure) disruptions necessitates effective mechanisms for quick service restoration. To efficiently restore the affected services has been a major research problem for many years. Several solutions such as pre-computed restoration paths or using heuristic algorithms have been proposed. These approaches have limitations in terms of adaptability to unforeseen network topologies and prolonged outages. In this paper, we introduce an approach to improve resilience in WDM networks by integrating Deep Reinforcement Learning (DRL) with Graph Neural Networks (GNN). Our proposed DRL+GNN-based solution leverages the capabilities of DRL in decision-making and the inherent ability of GNNs to generalize over graphs of varying sizes and structures. By considering the current and future state of the network, our solution intelligently selects pre-computed restoration paths that is viable. The results demonstrate the superior performance of our DRL+GNN agent in comparison to existing algorithms across a wide range of failure scenarios and network loading.

Thanks to the Software-Defined Networking (SDN) paradigm, which segregates the control and data layers of traditional networks, large and scalable networks can now be dynamically configured and managed. It is a game-changing networking technology that provides increased flexibility and scalability through centralized management. The Controller Placement Problem (CPP), however, poses a crucial problem in SDN because it directly impacts the efficiency and performance of the network. The CPP attempts to determine the most ideal number of controllers for any network and their corresponding relative positioning. This is to generally minimize communication delays between switches and controllers and maintain network reliability and resilience. In this paper, we present a modified Genetic Algorithm (GA) technique to solve the CPP efficiently. Our approach makes use the GA's capabilities to obtain the best controller placement correlation based on important factors such as network delay, reliability and availability. We further optimize the process by means of certain deduced constraints to allow faster convergence.

Wireless sensor networks (WSN) are majorly applied in recent times. Sensors are deployed in several areas to collect different kinds of data. Sensor nodes are low power and run out of energy quickly. When a sensor node runs out of energy, depending on the deployed environment, it can be quite difficult to replace it. Therefore, we need to prolong the lifetime of the WSN as long as possible. Genetic algorithm (GA) is a meta-heuristic algorithm that has been applied in many optimization problems. This paper presents a nested GA approach that provides the sink node location, selects cluster heads and computes the inter-cluster heads routing simultaneously significantly improving the network lifetime. We compare our algorithm with four other recent works conducted in the research space, and our method achieves average 15% better performance than the best in normal conditions and 30% better performance in more lossy environments.

Due to speedy increase in IoT devices, bandwidth intensive applications, voice and video streaming services as well as high speed gaming services on the internet, providing QoS-based solutions has become a major issue for service providers and has merited a lot of research from academia. Software defined networking is one of the most current interesting development in the field of research. Also, application of Artificial Intelligence (AI) in SDN for traffic engineering is widely researched. In this work, we present a framework based on SDN and VNF that identify the class of traffic real time and computes the appropriate route to meet the QoS demands of the traffic using a Deep neural network. The simulation results show that our proposed solution performs very well with an accuracy of 99.95% in comparison with its counterpart in the same area.