An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43717/ http://psasir.upm.edu.my/id/eprint/43717/1/An%20Energy-Efficient%20Spectrum-Aware%20Reinforcement.pdf |
| _version_ | 1848850303745523712 |
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| author | Mustapha, Ibrahim Mohd Ali, Borhanuddin A. Rasid, Mohd Fadlee Sali, Aduwati Mohamad, Hafizal |
| author_facet | Mustapha, Ibrahim Mohd Ali, Borhanuddin A. Rasid, Mohd Fadlee Sali, Aduwati Mohamad, Hafizal |
| author_sort | Mustapha, Ibrahim |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. |
| first_indexed | 2025-11-15T10:04:09Z |
| format | Article |
| id | upm-43717 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:04:09Z |
| publishDate | 2015 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-437172016-08-08T09:37:59Z http://psasir.upm.edu.my/id/eprint/43717/ An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks Mustapha, Ibrahim Mohd Ali, Borhanuddin A. Rasid, Mohd Fadlee Sali, Aduwati Mohamad, Hafizal It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. Multidisciplinary Digital Publishing Institute 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43717/1/An%20Energy-Efficient%20Spectrum-Aware%20Reinforcement.pdf Mustapha, Ibrahim and Mohd Ali, Borhanuddin and A. Rasid, Mohd Fadlee and Sali, Aduwati and Mohamad, Hafizal (2015) An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks. Sensors, 15 (8). pp. 19783-19818. ISSN 1424-8220 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4570397/pdf/sensors-15-19783.pdf 10.3390/s150819783 |
| spellingShingle | Mustapha, Ibrahim Mohd Ali, Borhanuddin A. Rasid, Mohd Fadlee Sali, Aduwati Mohamad, Hafizal An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| title | An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| title_full | An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| title_fullStr | An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| title_full_unstemmed | An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| title_short | An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| title_sort | energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks |
| url | http://psasir.upm.edu.my/id/eprint/43717/ http://psasir.upm.edu.my/id/eprint/43717/ http://psasir.upm.edu.my/id/eprint/43717/ http://psasir.upm.edu.my/id/eprint/43717/1/An%20Energy-Efficient%20Spectrum-Aware%20Reinforcement.pdf |