A method of managing resources for networks that makes use of dynamic software-defined networking (SDN) that allows administrators to regulate resources in real-time. However, traditional models have met challenges, including high packet drop, energy consumption, and low performance. To overcome these challenges, a novel solution called hyena sequence intrusion detection (HSID) is proposed. This involves the creation of required nodes in the network, and the hyena function continually monitors node status. It effectively eliminates high-energy nodes, ensuring the sustainability of the routing process. The framework is implemented and rigorously tested in the Python platform, with a thorough evaluation of network efficiency parameters. In comparison, various metrics are considered, including energy consumption, throughput, packet drop, detection accuracy, and confidentiality rate. The results demonstrate higher performance satisfaction with the proposed model, emphasising its effectiveness in addressing the identified challenges and enhancing overall network security and efficiency.
Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G and beyond 5G mobile networks. Integration of NOMA with multiple input multiple output (MIMO) technology improves spectral efficiency and user services. This paper provides a comprehensive review of the MIMO-NOMA system in future wireless networks. Initially, the basic concepts of the MIMO-NOMA system are explained, then reviewed single and multi-user MIMO-NOMA systems along with their limitations. The other enabling technologies of the MIMO-NOMA system, such as backscatter communication, mobile edge computing, intelligent reflecting surfaces (IRSs), integrated terrestrial satellite networks, and underwater communication, were also investigated. Finally, discussed future research directions of the proposed review, such as integration of better signal processing techniques, optimal resource management, analyse security and privacy in MIMO, etc.
Cognitive Radio (CR) is an emerging solution to spectrum scarcity caused by rapid telecommunication growth. This technology operates in frequency bands unused by primary users (PUs), enhancing overall spectrum utilisation. In cognitive radio networks (CRNs), the unused spectrum bands, also called as spectrum holes, are assigned to secondary users (SUs) to attain effective communication amongst SUs. CRNs' spectrum sensing, essential for identifying spectrum gaps, is prone to security risks. Moreover, routing in the network layer relies on accurate physical-layer sensing, which is compromised by these vulnerabilities. Ensuring secure spectrum sensing and routing is vital, as their compromise can affect overall CRN performance. Both traditional wireless network vulnerabilities and CRN-specific vulnerabilities can affect CRNs. In light of this, this paper provides a comprehensive survey of current vulnerability problems and related defences for cooperative spectrum sensing (CSS) and CR routing, as well as important unaddressed research challenges that require further investigation.
In today's digitally driven landscape, network security is critical as decision-making relies heavily on reliable, high-quality data. This study utilises the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to improve traffic classification and data quality evaluation through state-of-the-art techniques. By employing advanced methods such as feature engineering, ensemble learning, and machine learning (ML) algorithms, the research enhances classification precision and accuracy. The primary goal is to develop an optimised classifier capable of detecting anomalous network traffic indicative of security intrusions. Using a preprocessed dataset, the study identifies significant features and optimises hyperparameters to refine performance. A decision tree classifier was evaluated using unseen data, integrating stages of data preparation, anomaly detection, training, and visualisation. The resulting model demonstrated high effectiveness for intrusion detection systems, achieving an accuracy of 95.36%, alongside 97% precision, a 95% F1-score, and 95% recall. This comprehensive approach ensures robust detection of potential network threats.
Fifth Generation (5G) Vehicle-to-Everything (V2X) communication requires efficient allocation of resource blocks (RBs) by a base station (BS) to the vehicles it is serving. To that end, this work presents a resource allocation algorithm that exploits the fact that vehicles in an environment typically have different velocities and velocity distributions. The algorithm uses a Velocity-Based User Splitting approach that partitions users into discrete (low/high) velocity categories to utilise the difference in coherence intervals between the different vehicles. The channel conditions of low velocity users remain constant for longer than those of high velocity users, and the algorithm uses this difference to perform optimal RB allocation to maximise capacity. The performance of the algorithm is evaluated with respect to many parameters and factors including transmitted power, number of RBs, and channel conditions. The results are compared against random allocation and simple greedy allocation methods, and show a 5% improvement in low signal-to-noise ratio (SNR) conditions.
Exploration into cooperative intelligent traffic systems has yielded improvements in ground transportation's efficiency, safety, and comfort. This work focuses on the resource allocation challenge within Vehicle-to-Everything (V2X) communications. To address this problem, we suggested and compared the performance of two distinct meta-heuristic algorithms. The first technique, variable neighborhood search (VNS), belongs to the category of solution-based meta-heuristics. The second technique hybridises particle swarm optimisation (PSO), a population-based meta-heuristic, with a proposed local search approach, trying to leverage the strengths and mitigate the weaknesses of both algorithms. The two proposed methods seek to optimise the system's overall throughput while ensuring minimal latency and reliability for both cellular user equipment (CUEs) and vehicle user equipment (VUEs). The algorithms proposed in this paper improve the system throughput and demonstrate its feasibility and utility for V2X communications.
The wireless sensor network (WSN) contains a huge number of cost-effective and small energy-constrained nodes for network communication. Moreover, the clustering is considered as a major part of WSNs routing. Hence, this paper develops the Cosine Lotus Effect Algorithm (CLEA) for Cluster Head (CH) selection, and the Fractional Cosine Lotus Effect Algorithm (FCLEA) for routing. Initially, the network simulation is carried out, and the CH is done using the proposed CLEA with the fitness components such as link lifetime (LLT), delay, inter, and cluster distance, trust factor, and energy, in which, the radial basis function network (RBFN) is employed for energy prediction. The proposed FCLEA is utilised for routing, where fitness factors such as energy, delay, distance, and trust factors are utilised. Moreover, the FCLEA-based routing obtained the better average residual energy, distance, and throughput of 1.719 J, 7.068 m, and 431.8.
