A Novel Approach for Better QoS in Cognitive Radio Ad Hoc Networks Using Cat Optimization

Singh, Lolita; Dutta, Nitul

doi:10.1007/978-981-32-9949-8_42

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…These days, practically every one of the organizations depends on fixed distributed organizations in an affirmed or unapproved recurrence gathering. In [13], Lolita Singh and Nitul Dutta writing assesses related to CRN and an advancement calculation to upgrade the general presentation of TE under CRN has been talked about. Multitude insight method is utilized in the work.…”

Section: Literature Reviewmentioning

confidence: 99%

QoS constraint Enhanced AODV in Cognitive Radio Ad Hoc Network

Shobana¹,

Vijayanand²,

Santhiya³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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More range is needed for data communication due to distributed computing, versatility, and the Internet of Things. Regrettably, that range has become a limited resource that is underplayed among licensed clients although these unauthorized selections are overflowing. Intellectual communication creativity is emerged as a potential solution to the range's underutilization. This investigation aims to solve the guiding problem by upgrading the DSR consensus using Quality of Service (QoS) criteria. This investigation determines how such variables affecting QoS could be referred to and in terms with related research. The test employs QoS criteria to improve DSR's display and then evaluate its effectiveness in resolving that steering problem. The proposed plot Q-DSR is compared to the DSR, which is currently in use. NS2 is used to lead reproductions. In terms of speed, temporary suspension, and bundle conveyance ratio, the proposed conspire outperforms the DSR agreement. The strategy worked out how to solve the steering problem yet has gradually reduced the factors that influence QoS. This same disadvantage of said proposed conspiracy is because there is no part to repair disrupted communications after a link break that is being addressed through our future studies.

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Section: Literature Reviewmentioning

confidence: 99%

QoS constraint Enhanced AODV in Cognitive Radio Ad Hoc Network

Shobana¹,

Vijayanand²,

Santhiya³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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show abstract

“…In [16], spectrum allocation strategy is used to improve the QoS for Cognitive network, which is not suitable for varying the link reliability Spectrum-aware routing, by introducing a new routing metric to locate the available channel through PUs' activities. In [17], a new routing metric is developed for the link positions by adding extra functionalities of Cat swarm approach to improve energy efficiency. However, the implementation of this approach is not sufficient to support link quality in DSA paradigm due to the limitations of channel movement.…”

Section: Complexitymentioning

confidence: 99%

“…If a data packet arrives at a SU, the SU predicts the availability of the next transmitting channel (or the channel of the receiver) at the starting of the subsequent time slot. By referring to the learning results, when the learning probability is satisfied by the channel as in equation (17) for the transmissions of data, an RTS packet will be sent by the transmitter to the receiver using the same channel at the starting of the subsequent time slot. Upon receiving the RTS packet, the intended SU receiver replies a CTS packet in the same time slot.…”

Section: Complexitymentioning

confidence: 99%

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

Malik

Hasan

2020

Complexity

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In the existing network-layered architectural stack of Cognitive Radio Ad Hoc Network (CRAHN), channel selection is performed at the Medium Access Control (MAC) layer. However, routing is done on the network layer. Due to this limitation, the Secondary/Unlicensed Users (SUs) need to access the channel information from the MAC layer whenever the channel switching event occurred during the data transmission. This issue delayed the channel selection process during the immediate routing decision for the channel switching event to continue the transmission. In this paper, a protocol is proposed to implement the channel selection decisions at the network layer during the routing process. The decision is based on past and expected future routing decisions of Primary Users (PUs). A learning agent operating in the cross-layer mode of the network-layered architectural stack is implemented in the spectrum mobility manager to pass the channel information to the network layer. This information is originated at the MAC layer. The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. This leads to minimizing the channel switching events and user interferences in the Reinforcement Learning- (RL-) based routing protocol. Simulations are conducted using Cognitive Radio Cognitive Network simulator based on Network Simulator (NS-2). The simulation results showed that the proposed routing protocol performed better than all the other comparative routing protocols in terms of number of channel switching events, average data rate, packet collision, packet loss, and end-to-end delay. The proposed routing protocol implies the improved Quality of Service (QoS) of the delay sensitive and real-time networks such as Cellular and Tele Vision (TV) networks.

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A Novel Approach for Better QoS in Cognitive Radio Ad Hoc Networks Using Cat Optimization

Cited by 2 publications

References 13 publications

QoS constraint Enhanced AODV in Cognitive Radio Ad Hoc Network

QoS constraint Enhanced AODV in Cognitive Radio Ad Hoc Network

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

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