Particle Swarm Optimization (PSO)

In order to resolve the issues with Intrusion Detection Systems (IDS), a preprocessing step known as feature selection is utilized. The main objectives of this step are to enhance the accuracy of classification, improve the clustering operation on imbalance dataset and reduce the storage space required. During feature selection, a subset of pertinent and non-duplicative features is chosen from the original set. In this paper, a novel approach for feature selection in intrusion detection is introduced, leveraging an enhanced k-means clustering algorithm. The clustering operation is further improved using the combination of Gravity Search Algorithm (GSA) and Particle Swarm Optimization (PSO) techniques. Additionally, Biogeography Based Optimization (BBO) technique known for its successful performance in addressing classification problems is also employed. To evaluate the proposed approach, it is tested on the UNSW-NB15 intrusion detection dataset. Finally, a comparative analysis is conducted, and the results demonstrate the effectiveness of the proposed approach, in such a way that the value of the detection accuracy parameter in the proposed method was 99.8% and in other methods it was a maximum of 99.2%.

Background: The rapid development of fifth-generation (5G) networks highlights challenges in extending coverage to remote and underserved areas due to infrastructure limitations and cost constraints. UAVs (drones) equipped with 5G base stations emerge as an innovative solution to this problem. Objective: This study aims to analyze the potential of drones as mobile 5G base stations to enhance connectivity in remote regions, addressing challenges like optimal deployment, energy efficiency, and user coverage. Methods: The research utilizes algorithms like Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for placement and energy management of drone-based 5G stations. Simulation models were employed to test these algorithms, with key metrics including coverage efficiency and energy consumption. Results: The study shows that drone-based stations can significantly improve coverage in remote areas, achieving up to 95% user coverage with optimized algorithms. Tethered drones and advanced energy management strategies were instrumental in enhancing endurance. Conclusion: Drones as mobile 5G base stations present a feasible and scalable approach to bridging the digital divide in remote regions. However, energy and regulatory challenges remain critical areas for future research.

  Background: Unmanned Aerial Vehicles (UAVs) utilizing and active interface with 5G networks has become the new frontier to tackling problems of latency and energy efficiency, interference, and resource management. Although prior researches explained the benefits of UAV integrated networks; overall assessment of various parameters and cases is still scarce. Objective: The article seeks to assess the performance of UAV integrated 5G network in terms of latency, power, signal quality, task coordination and coverage optimization and to ascertain the efficiency of optimization algorithms in the improvement of the integrated 5G network. Methods: Emulations were done in MATLAB and NS3 platforms in urban / suburban / emergency call settings. Latency, power consumption, SINR, and completion time were the performance indicator chosen in the paper. Optimization algorithms: Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), and the Multi-Objective Evolutionary Algorithm (MOEA) is evaluated in terms of Convergence time and Solution quality. Results : UAV-aided networks showed 36.7% and 29.2 % improvement in latency and energy consumption, while 33.6 % enhancement in SINR. MOEA offered the best results with 98.3% solution quality, and the PSO being the most convergence oriented. Minor deviations between simulation and real results highlight the need for adaptive mechanisms. Conclusion: The results presented focus on the enough potential of UAV-assisted 5G networks and their potential influence on improving performances in case of different criteria. Further research should focus on successfully implementing and deploying the proposed solutions and broadening the context of study to include 6G technologies.