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An Energy Optimized Fuzzy Based Clustered Routing Protocol For Wireless Sensor Network


Affiliations
1 Department of Computer Science and Engineering, Dr. K.N. Modi University, India
2 Department of Electronics and Communication Engineering, Government Engineering College, Bharatpur, India
     

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Wireless sensor networks are fast developing technical platforms with a wide range of applications. It’s tough to replace or recharge sensor nodes' power supply because they're battery-powered and may be employed in hazardous or inaccessible areas. In sensor networks, reducing energy consumption has long been a critical concern. Due to the lengthy distance between sensor nodes and the base station, data transmission from sensor nodes to the base station consumes the majority of the energy. Several academics have recently claimed that clustering is an effective approach to reduce energy consumption during data transmission while also extending the lifetime of networks. In this paper, an energy-optimized-fuzzy based clustering (EOFBC) strategy is used to improve the transmission efficiency and network longevity of network. The simulation findings suggest that proposed fuzzy logic-based techniques are superior to the existing clustering-based protocols. The simulation findings also reveal that the proposed scheme outperforms the other existing algorithms and significantly increases the lifetime of sensor networks.

Keywords

Wireless Sensor Network, Fuzzy Logic, Lifetime
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  • I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “A Survey on Sensor Networks”, IEEE Communications Magazine, Vol. 40, No. 8, pp. 102–114, 2002.

  • Turki Ali Alghamdi, “Energy Efficient Protocol in Wireless Sensor Network: Optimized Cluster Head Selection Model”, Telecommunication Systems, Vol. 74, No. 3, pp. 331-345 2020.

  • Biswa Mohan Sahoo, Tarachand Amgoth and Hari Mohan Pandey, “Particle Swarm Optimization based Energy Efficient Clustering and Sink Mobility in Heterogeneous Wireless Sensor Network”, Ad Hoc Networks, Vol. 106, pp. 1-13, 2020.

  • B. Kumar, U.K. Tiwari and S. Kumar, “Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network”, Proceedingss of International Conference on Parallel, Distributed and Grid Computing, pp. 73-77, 2020.

  • A.A. Abbasi and M. Younis, “A Survey on Clustering Algorithms for Wireless Sensor Networks”, Computer Communication, Vol. 30, pp. 14-15, 2007.

  • P.K. Mishra and S.K. Verma, “A Survey on Clustering in Wireless Sensor Network”, Proceedingss of International Conference on Computing, Communication and Networking Technologies, pp. 1-5, 2020.

  • W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp. 660-670, 2002.

  • G. Smaragdakis, “SEP: A Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks”, Master Thesis, Department Computer Science, Boston University, pp. 1-134, 2004.

  • H. El Alami and A. Najid, “ECH: An Enhanced Clustering Hierarchy Approach to Maximize Lifetime of Wireless Sensor Networks”, IEEE Access, Vol. 7, pp. 107142-107153, 2019.

  • L. Kong, J.S. Pan, P.W. Tsai and T.W. Sung, “An Energy Aware Routing Protocol for Wireless Sensor Network based on Genetic Algorithm”, Telecommunication Systems, Vol. 67, No. 3, pp. 451-463, 2018.

  • X. Zhao, H. Zhu, S. Aleksic and Q. Gao, “Energy-Efficient Routing Protocol for Wireless Sensor Networks based on Improved Grey Wolf Optimizer”, KSII Transactions on Internet and Information Systems, Vol. 12, No. 6, pp. 2644-2657, 2018.

  • J. Wang, Y. Gao, W. Liu, A. Sangaiah and H.-J. Kim, “An Improved Routing Schema with Special Clustering using PSO Algorithm for Heterogeneous Wireless Sensor Network”, Sensors, Vol. 19, No. 3, pp. 671-678, 2019.

  • W. Osamy, A. A. El-Sawy and A. Salim, “CSOCA: Chicken Swarm Optimization based Clustering Algorithm for Wireless Sensor Networks”, IEEE Access, Vol. 8, pp. 60676–60688, 2020.

  • I. Gupta, D. Riordan and S. Sampalli, “Cluster-Head Election using Fuzzy Logic for Wireless Sensor Networks”, Proceedingss of Annual Conference on Communication Networks and Services Research, pp. 255-260, 2005.

  • J.M. Kim, S.H. Park, Y.J. Han and T.M. Chung, “Chef: Cluster Head Election Mechanism using Fuzzy Logic in Wireless Sensor Networks”, Proceedingss of International

  • Conference on Advanced Communication Technology, Vol. 1, pp. 654-659, 2008.

  • H. El Alami and A. Najid, “Cffl: Cluster Formation using Fuzzy Logic for Wireless Sensor Networks”, Proceedingss of IEEE/ACS International Conference of Computer Systems and Applications, pp. 1-6, 2015.

  • P. Nayak and A. Devulapalli, “A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime”, Sensors, Vol. 16, No. 1, pp. 137-144, 2016.

  • A. Junpei, B. Leonard, X. Fatos and A. Durresi, “A Cluster Head Selection Method for Wireless Sensor Networks based on Fuzzy Logic”, Proceedings of IEEE International Conference on Region 10, pp. 1-4, 2007.

  • M. Asma, E. Rabiaa, H. Abdelhamid and R. Bouallegue, “Distributed Fuzzy Logic based Routing Protocol for Wireless Sensor Networks”, Proceedings of International Conference on Software, Telecommunications and Computer Networks, pp. 1-7, 2016.

  • N.A. Torghabeh, M.R.A. Totonchi and M.H.Y. Moghaddam, “Cluster Head Selection using a Two-Level Fuzzy Logic in Wireless Sensor Networks”, Proceedings of International Conference on Computer Engineering and Technology, Vol. 2., pp. 327-357, 2010.

  • Z. Siqing, T. Yang and Y. Feiyue, “Fuzzy Logic-Based Clustering Algorithm for Multi-Hop Wireless Sensor Networks”, Procedia Computer Science, Vol. 131, pp. 1095-1103, 2018.

  • P.S. Mehra, M.N. Doja and B. Alam, “Fuzzy based Enhanced Custer Head Selection (FBECS) for WSN”, Journal of King Saud University Science, Vol. 89, No. 1, pp. 1-15, 2018.

  • N. Shivappa and S.S. Manvi, “Fuzzy-Based Cluster Head Selection and Cluster Formation in Wireless Sensor Networks”, IET Networks, Vol. 8, No. 6, pp. 390-397, 2019.

  • S. Singh, S. Chand and B. Kumar, “Energy Efficient Clustering Protocol using Fuzzy Logic for Heterogeneous WSNs”, Wireless Personal Communications, Vol. 86, No. 2, pp. 451-475, 2016.

  • H. Taheri, P. Neamatollahi, O.M. Younis, S. Naghibzadeh and M.H. Yaghmaee, “An Energy-Aware Distributed Clustering Protocol in Wireless Sensor Networks using Fuzzy Logic”, Ad Hoc Networks, Vol. 10, No. 7, pp. 1469-1481, 2012.

  • S.A. Sert, H. Bagci and A. Yazici, “MOFCA: Multi-Objective Fuzzy Clustering Algorithm for Wireless Sensor Networks”, Applied Soft Computing, Vol. 30, pp. 151-165, 2015.

  • M. Singh, S. Soni and V. Kumar, “Clustering using Fuzzy Logic in Wireless Sensor Network”, Proceedings of International Conference on Computing Sustainability Global Development, pp. 1669-1674, 2016.

  • H. El Alami and A. Najid, “Fuzzy Logic Based Clustering Algorithm for Wireless Sensor Networks”, Sensor Technology: Concepts, Methodologies, Tools, and Applications, 2020.

  • S.A. Sert, A. Alchihabi and A. Yazici, “A Two-Tier Distributed Fuzzy Logic based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks”,’ IEEE Transactions on Fuzzy Systems, Vol. 26, No. 6, pp. 3615-3629, 2018.

  • M. Adnan, L. Yang, T. Ahmad and Y. Tao, “An Unequally Clustered Multi-hop Routing Protocol Based on Fuzzy Logic for Wireless Sensor Networks”, IEEE Access, Vol. 9, pp. 38531-38545, 2021.


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  • An Energy Optimized Fuzzy Based Clustered Routing Protocol For Wireless Sensor Network

Abstract Views: 148  |  PDF Views: 1

Authors

Poonam Tiwari
Department of Computer Science and Engineering, Dr. K.N. Modi University, India
Sandeep Kumar Gupta
Department of Computer Science and Engineering, Dr. K.N. Modi University, India
Aruna Pathak
Department of Electronics and Communication Engineering, Government Engineering College, Bharatpur, India

Abstract


Wireless sensor networks are fast developing technical platforms with a wide range of applications. It’s tough to replace or recharge sensor nodes' power supply because they're battery-powered and may be employed in hazardous or inaccessible areas. In sensor networks, reducing energy consumption has long been a critical concern. Due to the lengthy distance between sensor nodes and the base station, data transmission from sensor nodes to the base station consumes the majority of the energy. Several academics have recently claimed that clustering is an effective approach to reduce energy consumption during data transmission while also extending the lifetime of networks. In this paper, an energy-optimized-fuzzy based clustering (EOFBC) strategy is used to improve the transmission efficiency and network longevity of network. The simulation findings suggest that proposed fuzzy logic-based techniques are superior to the existing clustering-based protocols. The simulation findings also reveal that the proposed scheme outperforms the other existing algorithms and significantly increases the lifetime of sensor networks.

Keywords


Wireless Sensor Network, Fuzzy Logic, Lifetime

References