Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.4018/ijssci.335898
Transmit time and power optimisation increase secondary network energy efficiency (EE). The optimum resource allocation strategy in cognitive radio networks is the enhanced chimp optimisation algorithm (OFCOA) since the EE maximising problem is a nonlinear fractional programming problem. To control resources and energy, this research offers an energy-efficient CRN opposition function-based chimpanzee optimisation algorithm (OFCOA) solution. Combining the opposition function (OF) with the chimpanzee optimisation technique is recommended. OF in COAs improves decision-making. Spectrum measurements in energy management provide energy-efficient CRN operation. The suggested technique was evaluated using channel occupancy, CRN data, and four major and secondary user scenarios. CPU power, network life, transmission rate, latency, flush, power consumption, and overhead are utilized to evaluate the proposed approach in MATLAB. The proposed method is compared to existing approaches like Particle Swarm Optimisation (PSO), Chimpanzee Optimisation Algorithm (COA), and Whale Optimisation Algorithm.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4018/ijssci.335898
- https://www.igi-global.com/ViewTitle.aspx?TitleId=335898&isxn=9781668479346
- OA Status
- hybrid
- Cited By
- 2
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390705334
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390705334Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4018/ijssci.335898Digital Object Identifier
- Title
-
Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource AllocationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-10Full publication date if available
- Authors
-
Dilip Kumar Jang Bahadur Saini, Anupama Mishra, Dhirendra Siddharth, Pooja Joshi, Ritika Bansal, Shavi Bansal, Kwok Tai ChuiList of authors in order
- Landing page
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https://doi.org/10.4018/ijssci.335898Publisher landing page
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https://www.igi-global.com/ViewTitle.aspx?TitleId=335898&isxn=9781668479346Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.igi-global.com/ViewTitle.aspx?TitleId=335898&isxn=9781668479346Direct OA link when available
- Concepts
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Computer science, Particle swarm optimization, Cognitive radio, Mathematical optimization, MATLAB, Energy consumption, Overhead (engineering), Fitness function, Algorithm, Genetic algorithm, Wireless, Telecommunications, Machine learning, Electrical engineering, Operating system, Engineering, MathematicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.problem | 31 |
| abstract_inverted_index.provide | 78 |
| abstract_inverted_index.Particle | 129 |
| abstract_inverted_index.Spectrum | 73 |
| abstract_inverted_index.Transmit | 0 |
| abstract_inverted_index.approach | 117 |
| abstract_inverted_index.compared | 124 |
| abstract_inverted_index.enhanced | 22 |
| abstract_inverted_index.evaluate | 114 |
| abstract_inverted_index.existing | 126 |
| abstract_inverted_index.function | 59 |
| abstract_inverted_index.improves | 71 |
| abstract_inverted_index.increase | 5 |
| abstract_inverted_index.latency, | 105 |
| abstract_inverted_index.networks | 19 |
| abstract_inverted_index.overhead | 110 |
| abstract_inverted_index.problem. | 37 |
| abstract_inverted_index.proposed | 116, 121 |
| abstract_inverted_index.research | 44 |
| abstract_inverted_index.resource | 13 |
| abstract_inverted_index.strategy | 15 |
| abstract_inverted_index.utilized | 112 |
| abstract_inverted_index.Algorithm | 135 |
| abstract_inverted_index.Combining | 56 |
| abstract_inverted_index.algorithm | 25, 53 |
| abstract_inverted_index.cognitive | 17 |
| abstract_inverted_index.evaluated | 86 |
| abstract_inverted_index.nonlinear | 34 |
| abstract_inverted_index.resources | 40 |
| abstract_inverted_index.secondary | 6, 96 |
| abstract_inverted_index.solution. | 55 |
| abstract_inverted_index.suggested | 83 |
| abstract_inverted_index.technique | 65, 84 |
| abstract_inverted_index.Algorithm. | 140 |
| abstract_inverted_index.Chimpanzee | 133 |
| abstract_inverted_index.allocation | 14 |
| abstract_inverted_index.approaches | 127 |
| abstract_inverted_index.chimpanzee | 51, 63 |
| abstract_inverted_index.efficiency | 9 |
| abstract_inverted_index.fractional | 35 |
| abstract_inverted_index.management | 77 |
| abstract_inverted_index.maximising | 30 |
| abstract_inverted_index.occupancy, | 89 |
| abstract_inverted_index.operation. | 81 |
| abstract_inverted_index.opposition | 49, 58 |
| abstract_inverted_index.scenarios. | 98 |
| abstract_inverted_index.programming | 36 |
| abstract_inverted_index.Optimisation | 131, 134, 139 |
| abstract_inverted_index.consumption, | 108 |
| abstract_inverted_index.measurements | 74 |
| abstract_inverted_index.optimisation | 4, 24, 52, 64 |
| abstract_inverted_index.recommended. | 67 |
| abstract_inverted_index.transmission | 103 |
| abstract_inverted_index.function-based | 50 |
| abstract_inverted_index.decision-making. | 72 |
| abstract_inverted_index.energy-efficient | 47, 79 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.64072575 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |