Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks Article Swipe
Ruby Bhatt
,
Edeh Michael Onyema
,
Khalid K. Almuzaini
,
Celestine Iwendi
,
Shahab S. Band
,
Tripti Sharma
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31219/osf.io/732ea
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31219/osf.io/732ea
Many optimization algorithms have been created to determine the most energy-efficient transmission mode, allowing for lower power consumption during transmission over shorter distances while minimising interference from primary users (PUs). The improved cooperative clustering algorithm (ICCA) performs superior spectrum sensing across groups of multiusers compared to any other method currently available in terms of sensing inaccuracy, power savings, and convergence time than any other method currently available. The proposed ICCA algorithm is employed in this research study to find the optimal numbers of clusters based on its connectivity and the most energy-efficient distributed cluster-based sensing technique available. In this research, many randomly chosen secondary users (SUs) and primary users (PUs) are investigated for potential implementation opportunities. Therefore, as compared to the current optimization strategies, the proposed ICCA algorithm enhanced the convergence speed by integrating the multiuser clustered communication into a single communication channel. Experimental results revealed that the new ICCA algorithm reduced node power by 9.646 percent compared to traditional ways when comparing the novel algorithm to conventional approaches. In a similar vein, as compared to the prior methodologies, the ICCA algorithm reduced the average node power of SUs by 24.23 percent on average. When the SNR is decreased to values below 2 dB, the likelihood of detection improves dramatically, as seen in the figure. ICCA has a low false alarm rate when matched to other optimization algorithms for direct detection, and the proposed method outperforms them all. Following the findings of the simulations, the proposed ICCA technique effectively addresses multimodal optimization difficulties and optimizes network capacity performance in wireless networks. A detailed discussion of SS applications for the IoT and wireless sensor networks, both based on CR, is provided. There is also a thorough discussion of the most recent advancements in spectrum sensing as a facility. IoT or WSN may be essential in feeding the CR networks with spectrum sensing data and the future of spectrum sensing. The use of CR for fifth generation and afar its potential application in frequency allocation are discussed. To stay up with the advancement of communication technology, SS should give additional features to remain competitive, like the capacity to investigate various available channels and accessible places for transmission. Based on present and prospective methods in wireless communications, we highlight the crucial upcoming study paths and difficulty spots in signal processing for cognitive radio and potential solutions (SS-CR).
Related Topics
Concepts
Computer science
Cluster analysis
Cognitive radio
Transmission (telecommunications)
Convergence (economics)
Node (physics)
Wireless sensor network
Interference (communication)
False alarm
Rate of convergence
Energy (signal processing)
Algorithm
Channel (broadcasting)
Real-time computing
Wireless
Artificial intelligence
Computer network
Telecommunications
Mathematics
Engineering
Statistics
Economics
Structural engineering
Economic growth
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31219/osf.io/732ea
- https://osf.io/732ea/download
- OA Status
- gold
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297887274
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297887274Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31219/osf.io/732eaDigital Object Identifier
- Title
-
Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-09-12Full publication date if available
- Authors
-
Ruby Bhatt, Edeh Michael Onyema, Khalid K. Almuzaini, Celestine Iwendi, Shahab S. Band, Tripti SharmaList of authors in order
- Landing page
-
https://doi.org/10.31219/osf.io/732eaPublisher landing page
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https://osf.io/732ea/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://osf.io/732ea/downloadDirect OA link when available
- Concepts
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Computer science, Cluster analysis, Cognitive radio, Transmission (telecommunications), Convergence (economics), Node (physics), Wireless sensor network, Interference (communication), False alarm, Rate of convergence, Energy (signal processing), Algorithm, Channel (broadcasting), Real-time computing, Wireless, Artificial intelligence, Computer network, Telecommunications, Mathematics, Engineering, Statistics, Economics, Structural engineering, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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