https://oldena.lpnu.ua/handle/ntb/52528| Title: | Energy Efficient Clustering Protocol for Heterogeneous Wireless Sensor Network: A Hybrid Approach using GA and K-means |
| Authors: | Bhushan, Shashi Pal, Raju Antoshchuk, Svetlana |
| Affiliation: | School of Computer and Information Science Jaypee Institute of Information Technology ONPU |
| Bibliographic description (Ukraine): | Bhushan S. Energy Efficient Clustering Protocol for Heterogeneous Wireless Sensor Network: A Hybrid Approach using GA and K-means / Shashi Bhushan, Raju Pal, Svetlana Antoshchuk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 381–385. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Bhushan S. Energy Efficient Clustering Protocol for Heterogeneous Wireless Sensor Network: A Hybrid Approach using GA and K-means / Shashi Bhushan, Raju Pal, Svetlana Antoshchuk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 381–385. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | Optimal clustering WSN Genetic Algorithm Kmeans |
| Number of pages: | 5 |
| Page range: | 381-385 |
| Start page: | 381 |
| End page: | 385 |
| Abstract: | —A hybrid approach combining genetic algorithm(GA) and K-means algorithm, called KGA is proposed in this paper for design of clustering protocol with energy efficiency for non-homogeneous wireless sensor network. The problem of optimal clustering can be considered as a problem for searching for an optimal number of clusters in a big search space such that WSN metrics are optimized. In the proposed protocol, distance between clusters, distance within clusters and a number of cluster heads are employed to search for optimal number of clusters and cluster heads. Maximization of energy saving and lifetime of a network are the two important metrics. The KGA is designed with a hybrid approach to population initialization scheme and objective function. The superiority of the protocol over other heuristic and meta-heuristic techniques is extensively demonstrated on several parameters: energy efficiency, network life time and throughput. |
| URI: | https://ena.lpnu.ua/handle/ntb/52528 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] Ian F. Akyildiz, et al., "A survey on sensor networks," IEEE Communications magazine, vol. 40.8, pp. 102-114, 2002. [2] J. Han, and M. Kamber, Data Mining: Concepts and Techniques, 2 ed. Morgan Kaufman Publishers, 2006 [3] W. B. Heinzelman, A. P. Chandrakasan, H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on wireless communications, vol. 1(4) pp. 660-670, Oct. 2002 [4] Georgios Smaragdakis, Ibrahim Matta, and Azer Bestavros, “SEP: A stable election protocol for clustered heterogeneous wireless sensor networks,” Boston University Computer Science Department, May 31 2004. [5] A. W. Matin, and S. Hussain, “Intelligent hierarchical cluster-based routing,” in: Proceedings of the international workshop on mobility and scalability in wireless sensor networks (MSWSN) in IEEE international conference on Distributed Computing in Sensor Networks (DCOSS), pp. 165–172, 2006. [6] Zbigniew Michalewicz, Genetic algorithms + data structures = evolution programs. Springer, 2009 [7] Bara’a A. Attea, and Enan A. Khalil. “A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks,” Applied Soft Computing vol. 12.7, pp. 1950-1957, 2012. [8] Mohammed Abo-Zahhad, et al. "A new energy-efficient adaptive clustering protocol based on genetic algorithm for improving the lifetime and the stable period of wireless sensor networks," International Journal of Energy, Information and Communications, vol. 5.3 pp. 47-72, 2014. [9] Pratyay Kuila, and Prasanta K. Jana, “Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach,” Engineering Applications of Artificial Intelligence, vol. 33 pp. 127-140, 2014. [10] James Kennedy, "Particle swarm optimization." Encyclopedia of machine learning. Springer US, 2011. 760-766 [11] Suneet K. Gupta, and Prasanta K. Jana. “Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach,” Wireless Personal Communications, vol. 83.3 pp. 2403-2423, 2015. [12] Stefano Basagni, et al., “A generalized clustering algorithm for peerto-peer networks,” in Workshop on Algorithmic Aspects of Communication. 1997. |
| References (International): | [1] Ian F. Akyildiz, et al., "A survey on sensor networks," IEEE Communications magazine, vol. 40.8, pp. 102-114, 2002. [2] J. Han, and M. Kamber, Data Mining: Concepts and Techniques, 2 ed. Morgan Kaufman Publishers, 2006 [3] W. B. Heinzelman, A. P. Chandrakasan, H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on wireless communications, vol. 1(4) pp. 660-670, Oct. 2002 [4] Georgios Smaragdakis, Ibrahim Matta, and Azer Bestavros, "SEP: A stable election protocol for clustered heterogeneous wireless sensor networks," Boston University Computer Science Department, May 31 2004. [5] A. W. Matin, and S. Hussain, "Intelligent hierarchical cluster-based routing," in: Proceedings of the international workshop on mobility and scalability in wireless sensor networks (MSWSN) in IEEE international conference on Distributed Computing in Sensor Networks (DCOSS), pp. 165–172, 2006. [6] Zbigniew Michalewicz, Genetic algorithms + data structures = evolution programs. Springer, 2009 [7] Bara’a A. Attea, and Enan A. Khalil. "A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks," Applied Soft Computing vol. 12.7, pp. 1950-1957, 2012. [8] Mohammed Abo-Zahhad, et al. "A new energy-efficient adaptive clustering protocol based on genetic algorithm for improving the lifetime and the stable period of wireless sensor networks," International Journal of Energy, Information and Communications, vol. 5.3 pp. 47-72, 2014. [9] Pratyay Kuila, and Prasanta K. Jana, "Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach," Engineering Applications of Artificial Intelligence, vol. 33 pp. 127-140, 2014. [10] James Kennedy, "Particle swarm optimization." Encyclopedia of machine learning. Springer US, 2011. 760-766 [11] Suneet K. Gupta, and Prasanta K. Jana. "Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach," Wireless Personal Communications, vol. 83.3 pp. 2403-2423, 2015. [12] Stefano Basagni, et al., "A generalized clustering algorithm for peerto-peer networks," in Workshop on Algorithmic Aspects of Communication. 1997. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
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| 2018_Bhushan_S-Energy_Efficient_Clustering_381-385.pdf | 272.72 kB | Adobe PDF | View/Open | |
| 2018_Bhushan_S-Energy_Efficient_Clustering_381-385__COVER.png | 621.9 kB | image/png | View/Open |
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