Link‐delay and spectrum‐availability aware routing in cognitive sensor networks

Tran-Dang, Hoa; Kim, Dong-Seong

doi:10.1049/iet-com.2019.0994

Cited by 5 publications

(11 citation statements)

References 48 publications

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“…This drop is due to more CR nodes competing for the spectrum. The packet success ratio in the proposed solution is on average 3% more compared to both solutions of Tran-Dang and Kim [23] and Salameh and Khasawne [22].…”

Section: Resultsmentioning

confidence: 87%

“…The packet success ratio in the proposed solution is on average 6% more compared to Tran-Dang and Kim [23] and 4% more compared to Salameh and Khasawne [22]. The integrated strategy of reliable route selection, packet caching for retransmission and flow control has increased the packet delivery ratio in the proposed solution.…”

Section: Resultsmentioning

confidence: 95%

“…Performance comparison is done against following solutions. Spectrum aware routing algorithm by Tran-Dang and Kim [23] and Spectrum availability routing by Salameh and Khasawneh [22].The performance is compared in terms of Packet success ratio, Average latency and Network overhead. The packet success ratio is measured by varying the percentage of nodes generating the packets to sink for 100 CR nodes in the network.…”

Section: Resultsmentioning

confidence: 99%

“…But the solution did not consider the backup for packets when spectrum availability prediction becomes erroneous. To estimate the remaining duration of spectrum availability was also proposed based on the expected idle length of channels, sensing period and communication history on the channel [23]. The estimations are used in routing path construction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(a) Packet success ratio over time (b) Average latencyThe end to end delay is on average 9% lower in the proposed solution compared to Tran-Dang and Kim[23] and 16.87% lower compared to Salameh and Khasawne[22].The use of geographic opportunistic routing and only selective multi path propagation has reduced the end to end delay in the proposed solution compared to existing works.The communication overhead is measured in terms of volume of data packets apart from data payload. It is measured by varying the number of CR nodes and the result are taken as per the data in illustrated in Figure5.…”

mentioning

confidence: 91%

See 4 more Smart Citations

Hybrid strategy for reliable packet transfer and packet loss reduction in cognitive radio Ad-Hoc Network

Reddy¹,

Subramanyam²,

Reddy³

2021

IJATEE

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The proposed solution adopts two strategies of retransmission and reconstruction to compensate for the packet loss due to unexpected link failures and congestion. Reconstruction is given higher preference compared to retransmission in mitigating packet loss. Sufficient redundancy is built into packet for reconstruction. Cooperative caching at secondary user (SU) is done to avoid the hop count for retransmission Re search Article

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Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 91%

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Hybrid strategy for reliable packet transfer and packet loss reduction in cognitive radio Ad-Hoc Network

Reddy¹,

Subramanyam²,

Reddy³

2021

IJATEE

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Congestion centric multi‐objective reptile search algorithm‐based clustering and routing in cognitive radio sensor network

Sunitha¹,

Balmuri

Prado

et al. 2022

Trans Emerging Tel Tech

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In recent trends, Cognitive Radio Sensor Networks (CRSNs) are investigated in‐depth and getting momentum in all types of applications. CRSN can make use of the underutilized frequency spectrum in a suitable manner. Due to the above‐mentioned advantage, the scholars have initiated to study of the domain of cognitive radio routing. Network congestion produces transmission delays and packet loss, as well as time and energy wasted on recovery. In order to fulfill the energy efficiency and network lifetime in CRSN, Congestion Centric Multi‐Objective Reptile Search Algorithm (CC‐MORSA)‐based Clustering and Routing are used. The main objective of proposed CC‐MORSA is to improve the lifetime by minimizing the distance among the designated Cluster Head nodes which creates the fitness function by multiple objectives like energy, distance, and load. This technique is appropriate for common sensor nodes in coordinated communications infrastructure and large networks. The simulation results are analyzed through MATALB in terms of remaining energy (999.5 J), average delay (0.36 s), Packet Delivery Ratio (99.8%), Energy Consumption (24.1 J), Throughput (0.98 Mbps), routing overhead (0.54), and Packet Loss Rate (0.2%). From the outcomes, it shows that the presented CC‐MORSA outperformed conventional Stability‐Aware Cluster‐based Routing and Drop Factor‐Based Energy Efficient Routing technique.

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Routing Based on Spectrum Quality and Availability in Wireless Cognitive Radio Sensor Networks

Gatate¹,

Agarkhed²

2021

Advances in Intelligent Systems and Computing

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Link‐delay and spectrum‐availability aware routing in cognitive sensor networks

Cited by 5 publications

References 48 publications

Hybrid strategy for reliable packet transfer and packet loss reduction in cognitive radio Ad-Hoc Network

Hybrid strategy for reliable packet transfer and packet loss reduction in cognitive radio Ad-Hoc Network

Congestion centric multi‐objective reptile search algorithm‐based clustering and routing in cognitive radio sensor network

Routing Based on Spectrum Quality and Availability in Wireless Cognitive Radio Sensor Networks

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