Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique

There are several encryption system applications in 5G networks where rapid response is needed, particularly in the military, health sector, traffic, and vehicular movement. This article presents a proposed data security system for 5G networks that fortifies the security of the network through the use of synchronized chaotic systems to produce pseudo-random numbers. The technique by which random numbers are generated during the encryption procedures is closely associated with 5G network security. Many synchronized chaotic systems are used to produce chaotic random models which are used as encryption bases for a wide variety of data. In this study, the encryption was carried out using a variety of data, including two and three-dimensional color images and audio signals of varying lengths, in addition to the use of Fast Fourier Transform (FFT) for encryption of the ingredient energy wave. The results revealed that the algorithm deployed in the process of encryption performed well. Simulations were performed in MATLAB.

Section: Related Workmentioning

confidence: 99%

Using a Chaotic Digital System to Generate Random Numbers for Secure Communication on 5G Networks

Alrikabi,

Aljazaery,

Alaidi

2024

“…In this study, an energy expenditure model was presented that included spectrum detection and cluster energy consumption before determining the optimal number of clusters for the organization's activities. It is necessary to evaluate the value of direct assets in reducing energy consumption, using the lopsided clustering technique to regulate energy consumption among CHs under multiple bounce transmission conditions [23]. In [24], LEAUCH was proposed, considering channel resources and uneven clustering for balancing energy consumption.…”

Section: A State-of-the-art Solutionsmentioning

confidence: 99%

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Panbude,

Iyer,

Nandgaonkar

et al. 2023

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

A Medical Image Classification Model based on Quantum-Inspired Genetic Algorithm

Ibrahim,

Rokbani,

Wali

et al. 2024

This study used a Quantum-Inspired Genetic Algorithm (QIGA) to select the proper functionality and reduce the dimensions, classification time, and computational cost of a learning dataset. QIGA reduces the complexity of solutions and improves the selection of the best features. The application of quantum principles, in particular the unpredictability of quantum chromosomes, which are represented by qubits, can help in investigating a significantly more extensive solution space. QIGA offers a novel approach to feature selection in optimization problems. Using principles from quantum computing, this algorithm aims to enhance the efficiency and effectiveness of the feature selection process to increase performance. This indicates that features of both exploration and exploitation are embodied by QIGA without requiring massive amounts of data. Considerable gains in classification accuracy were achieved compared to traditional methods. The dynamic design of the models through the evolutionary mechanism in QIGA enables the optimization process to adapt to varying probabilities produced from the qubit overlay via the quantum rotation gate. This is contrary to traditional methods. The model using QIGA offered a more precise classification than the model optimized by Genetic Algorithms (GA). The proposed method achieved superior performance in terms of classification accuracy, with a score of more than 98%, compared to GA, which achieved a classification accuracy of 94%.