FBCHS: Fuzzy Based Cluster Head Selection Protocol to Enhance Network Lifetime of WSN
Abstract With enormous evolution in Microelectronics, Wireless Sensor Networks (WSNs) have played a vital role in every aspect of daily life. Technological advancement has led to new ways of thinking and of developing infrastructure for sensing, monitoring, and computational tasks. The sensor network constitutes multiple sensor nodes for monitoring, tracking, and surveillance of remote objects in the network area. Battery replacement and recharging are almost impossible; therefore, the aim is to develop an efficient routing protocol for the sensor network. The Fuzzy Based Cluster Head Selection (FBCHS) protocol is proposed, which partitions the network into several regions based on node energy levels. The proposed protocol uses an artificial intelligence technique to select the Cluster Head (CH) based on maximum node Residual Energy (RE) and minimum distance. The transmission of data to the Base Station (BS) is accomplished via static clustering and the hybrid routing technique. The simulation results of the FBCHS protocol are com- pared to the SEP protocol and show improvement in the stability period and improved overall performance of the network.
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Sharma, G., and Kumar, A., 2018. ‘Improved DV-Hop localization algorithm using teaching learning based optimi- zation for wireless sensor networks’, Telecommunication Systems, 67(2), pp. 163–178.
Shivappa, N., and Manvi, S. S., 2019. ‘Fuzzy-based cluster head selection and cluster formation in wireless sensor networks’, IET Networks, 8(6), pp. 390–397.
Singh, K., and Daniel, A. K., 2015. ‘Load Balancing in Region Based Clustering for Heterogeneous Environment in WSNs Using AI Techniques’, in 2015 Fifth International Conference on Advanced Computing & Communication Technologies, pp. 641–646.
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Tang, W., Zhang, K., and Jiang, D., 2018. ‘Physarum-inspired routing protocol for energy harvesting wireless sensor networks’, Telecommunication Systems, 67(4), pp. 745–762.
Wan, C., and Du, S., 2011. ‘Improvement and simulation of leach in wireless sensor networks’, Jisuanji Yingyong yu Ruanjian, 28(4), pp. 113–116.
Yang, L., et al., 2018. ‘An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor net- works’, Telecommunication Systems, 68(1), pp. 11–26.
Behzad, M., 2018. ‘M-BEHZAD: minimum distance based energy efficiency using hemisphere zoning with ad- vanced divide-and-rule scheme for wireless sensor networks’, arXiv preprint arXiv:1804.00898.
Choudhary, S., et al., 2022. ‘Fuzzy Approach-Based Stable Energy-Efficient AODV Routing Protocol in Mobile Ad hoc Networks’, in Software Defined Networking for Ad Hoc Networks. Springer, pp. 125–139.
Deepa, O., and Suguna, J., 2020. ‘An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks’, Journal of King Saud University-Computer and Information Sciences, 32(7), pp. 763–774.
Dwivedi, A. K., and Sharma, A., 2020. ‘FEECA: Fuzzy based Energy Efficient Clustering Approach in Wireless Sensor Network’, EAI Endorsed Transactions on Scalable Information Systems, 7(27).
Elhoseny, M., et al., 2020. ‘Swarm intelligence--based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks’, International Journal of Distributed Sensor Networks, 16(9), p. 1550147720949133.
Faiz, M., and Daniel, A. K., 2020. ‘Fuzzy Cloud Ranking Model based on QoS and Trust’, in 2020 Fourth Interna- tional Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 1051–1057.
Jayaraman, G., and Dhulipala, V. R., 2021. ‘FEECS: Fuzzy-Based Energy-Efficient Cluster Head Selection Algo- rithm for Lifetime Enhancement of Wireless Sensor Networks’, Arabian Journal for Science and Engineering, pp. 1–11.
Kumar, S., et al., 2021. ‘Division Algorithm Based Energy-Efficient Routing in Wireless Sensor Networks’, Wire-less Personal Communications, pp. 1–20.
Latif, K., et al., 2015. ‘Energy hole minimization with field division for energy efficient routing in WSNs’, Interna-tional Journal of Distributed Sensor Networks, 11(10), p. 953134.
Latif, K., et al., 2016. ‘Energy consumption model for density controlled divide-and-rule scheme for energy efficient routing in wireless sensor networks’, International Journal of Ad Hoc and Ubiquitous Computing, 21(2), pp. 130–139.
Maurya, S., and Daniel, A. K., 2015. ‘Rbhr: Region-based hybrid routing protocol for wireless sensor networks using ai technique’, in Proceedings of Fourth International Conference on Soft Computing for Problem Solving, pp. 37–52.
Mehra, P. S., Doja, M. N., and Alam, B., 2020. ‘Fuzzy based enhanced cluster head selection (FBECS) for WSN’, Journal of King Saud University-Science, 32(1), pp. 390–401.
Narayan, V., and Daniel, A. K., 2019. ‘Novel protocol for detection and optimization of overlapping coverage in wireless sensor networks’, Int. J. Eng. Adv. Technol, 8.
Narayan, V., and Daniel, A. K., 2020. ‘Multi-Tier Cluster Based Smart Farming Using Wireless Sensor Network’, in 2020 5th International Conference on Computing, Communication and Security (ICCCS), pp. 1–5.
Narayan, V., and Daniel, A. K., 2021. ‘RBCHS: Region-Based Cluster Head Selection Protocol in Wireless Sensor Network’, in Proceedings of Integrated Intelligence Enable Networks and Computing. Springer, pp. 863–869.
Narayan, V., and Daniel, A. K., 2021. ‘A novel approach for cluster head selection using trust function in wsn’, Scalable Computing, 22(1), pp. 1–13. doi: 10.12694:/scpe.v22i1.1808.
Narayan, V., and Daniel, A. K., 2022. ‘CHHP: coverage optimization and hole healing protocol using sleep and wake-up concept for wireless sensor network’, International Journal of System Assurance Engineering and Manage- ment, pp. 1–11.
Nazari Talooki, V., Rodriguez, J., and Marques, H., 2014. ‘Energy efficient and load balanced routing for wireless multihop network applications’, International Journal of Distributed Sensor Networks, 10(3), p. 927659.
Sharma, G., and Kumar, A., 2018. ‘Improved DV-Hop localization algorithm using teaching learning based optimi- zation for wireless sensor networks’, Telecommunication Systems, 67(2), pp. 163–178.
Shivappa, N., and Manvi, S. S., 2019. ‘Fuzzy-based cluster head selection and cluster formation in wireless sensor networks’, IET Networks, 8(6), pp. 390–397.
Singh, K., and Daniel, A. K., 2015. ‘Load Balancing in Region Based Clustering for Heterogeneous Environment in WSNs Using AI Techniques’, in 2015 Fifth International Conference on Advanced Computing & Communication Technologies, pp. 641–646.
Sirsikar, S., and Chandak, M., 2018. ‘Efficient Clustering using Concentric Rings and Rectangular Region For- mations in Wireless Sensor Networks’, International Journal of Applied Engineering Research, 13(6), pp. 3483–3491.
Tang, W., Zhang, K., and Jiang, D., 2018. ‘Physarum-inspired routing protocol for energy harvesting wireless sensor networks’, Telecommunication Systems, 67(4), pp. 745–762.
Wan, C., and Du, S., 2011. ‘Improvement and simulation of leach in wireless sensor networks’, Jisuanji Yingyong yu Ruanjian, 28(4), pp. 113–116.
Yang, L., et al., 2018. ‘An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor net- works’, Telecommunication Systems, 68(1), pp. 11–26.
Narayan, V., & A. K., D. (2023). FBCHS: Fuzzy Based Cluster Head Selection Protocol to Enhance Network Lifetime of WSN. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 285–307. https://doi.org/10.14201/adcaij.27885
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