On applying firefly algorithm for cognitive radio networks

Saoucha, Naziha Ali; Ghanem, Khalida; Benmammar, Badr

doi:10.1109/scvt.2014.7046711

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Because of its simplicity, proven performance and convergence speed, the lately suggested firefly algorithm (FFA) is expanded and acclimatized to tune the transmission parameters in multicarrier cognitive systems [5]. As a consequence, in terms of convergence time and solution quality, the proposed approach surpasses the commonly used Particle Swarm Optimization (PSO) technique.…”

Section: Related Workmentioning

confidence: 99%

Select the optimal channel based on improved Bat optimization in the cognitive radio network

mustafa

Najm

Al-Yousif

2022

Preprint

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A group of nodes that can progress execution automatically by changing their behavior to adapt to the environment is called cognitive radio networks (CRNs). Though several routing protocols inverting the CRNs decisions are proposed to combine the variable degrees of adaptation, these protocols provide quality of service (QoS) assurance to the networks. This study suggests an optimization algorithm based on one of the inspired approaches (particularly the improved bat swarm algorithm) to expedite the reconnaissance for the best paths. It is to create cognitive routing protocols, transmit data packets effectively, and impose complexities as a result of the evolving climate of the reachable spectrum. The optimization problem is being solved using this algorithm. The transmission mechanism is made scalable, effectual, and adaptive with an increasing number of nodes using the bat swarm method presented in this study. This paper suggests a method that more effectively fulfills QoS requirements and demonstrates how to control residual bandwidth, time delay, and packet loss ratio. Meanwhile, the same algorithm also favors service requirements and highlights some important characteristics of the bat swarm algorithm. Finally, simulation results illustrate that the proposed work offers efficient bandwidth exploitation.

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Section: Related Workmentioning

confidence: 99%

Select the optimal channel based on improved Bat optimization in the cognitive radio network

mustafa

Najm

Al-Yousif

2022

Preprint

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“…They also have the capability to adjust DoS JSA and HDSAs (i.e., congestion and collusion), which include all other forms of attacks, such as DoS JSA, PD, and RCRCO, for optimum user experience [18]. The OAs have also been used to evaluate a real-time data transmission in VANET [19], which utilized IEEE 802.11p for dedicated short-range communication (DSRC) technology. VFC integration with OAs and trust detection of the nodes in VANET, which also utilize authentication/KDE in VANET, can appropriately secure the VANET through the RSU.…”

Section: Background Study Of This Researchmentioning

confidence: 99%

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

Erskine

Elleithy

2019

Electronics

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The vehicular ad hoc network (VANET) is a method through which Intelligent Transportation Systems (ITS) have become important for the benefit of daily life. Real-time detection of all forms of attacks, including hybrid DoS attacks in IEEE 802.11p, has become an urgent issue for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate an enormous amount of messages. This leads to overutilization of the road side unit (RSU) or the central processing unit (CPU) for computation. Therefore, efficient storage and intelligent VANET infrastructure architecture (VIA), which includes trustworthiness, are required. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computation, and communication needs for VANET. This research utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence, including Cuckoo/CSA Artificial Bee Colony (ABC) and Firefly/Genetic Algorithm (GA), to provide real-time detection of DoS attacks in IEEE 802.11p, using VFC for a secure intelligent vehicular network. Vehicles move ar a certain speed and the data is transmitted at 30 Mbps. Firefly Feed forward back propagation neural network (FFBPNN) is used as a classifier to distinguish between the attacked vehicles and the genuine vehicles. The proposed scheme is compared with Cuckoo/CSA ABC and Firefly GA by considering jitter, throughput, and prediction accuracy.

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Various Optimization Algorithm used in CRN

Singh

Dutta

2020

2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)

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On applying firefly algorithm for cognitive radio networks

Cited by 4 publications

References 8 publications

Select the optimal channel based on improved Bat optimization in the cognitive radio network

Select the optimal channel based on improved Bat optimization in the cognitive radio network

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

Various Optimization Algorithm used in CRN

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