Various Optimization Algorithm used in CRN

Singh, Lolita; Dutta, Nitul

doi:10.1109/iccakm46823.2020.9051553

Cited by 5 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Singh L et al [ 21 ] demonstrated a GA that raised the likelihood of achieving the best weight by optimizing network spectrum prediction with reasonable accuracy. It improves handover by using specPSO.…”

Section: Literature Survey and Problem Statementmentioning

confidence: 99%

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Srivastava

Singh

Malik

et al. 2023

Sensors

View full text Add to dashboard Cite

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.

show abstract

Section: Literature Survey and Problem Statementmentioning

confidence: 99%

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Srivastava

Singh

Malik

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…This study aimed to address these clustering issues and improve the overall performance of CR-WSNs beyond previous attempts. Although optimization methods have demonstrated their effectiveness in WSNs [13][14][15][16][17], adapting such approaches to CR-WSNs could potentially lead to an increase in the computational overhead associated with the clustering process. In contrast, compared to optimization-focused techniques, fuzzy-based solutions [18][19][20], offer a more lightweight and efficient alternative for addressing clustering challenges in CR-WSNs.…”

Section: Introductionmentioning

confidence: 99%

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

Panbude,

Iyer,

Nandgaonkar

et al. 2023

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

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.

show abstract

“…The access of a large number of wireless devices will lead to a sharp shortage of spectrum resources, and the emergence of CRNs can effectively solve the problem. The CRNs improve the utilization of wireless spectrum resources mainly by using spectrum sensing technology [17] [18]. The combination of CRNs and other technologies has been extensively studied by scholars [19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Security for CRNs over Beaulieu-Xie Fading Channels

Zeng

Hua

Fang³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In recent years, the access of massive communication devices leads to the insufficient spectrum resources of wireless networks. One of the practical means to resolve the problem is to build cognitive radio networks (CRNs), which can realize the sharing of spectrum resources between primary and secondary users, thereby improving the utilization rate of wireless spectrum resources. To this end, the CRNs are utilized to establish the Wyner’s eavesdropping model over the Beaulieu-Xie fading channels. We mainly deduce the accurate expressions of secrecy outage probability and strictly positive secrecy capacity to explore the performance of physical layer security. Moreover, the better overlap between the statistical simulation and the theoretical results indicates the correctness of the theoretical analysis equation. The interesting results are that both increasing the P max and decreasing the C t h can improve the security performance. This work is a good reference and guidance for modeling CRNs (Internet of Things, fifth generation, cell phone networks, etc.) and security performance evaluation.

show abstract

Various Optimization Algorithm used in CRN

Cited by 5 publications

References 25 publications

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm

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

Physical Layer Security for CRNs over Beaulieu-Xie Fading Channels

Contact Info

Product

Resources

About