Cover
Vol. 17 No. 2 (2021)

Published: December 31, 2021

Pages: 190-197

Original Article

E-FLEACH: An Improved Fuzzy Based Clustering Protocol for Wireless Sensor Network

Enaam A. Al-Husain 1 Ghaida A. Al-Suhail
1Department of Computer Engineering, University of Basrah, Basrah, Iraq
History
  • Received: August 31, 2021
  • Revised: October 4, 2021
  • Accepted: October 4, 2021
  • Available Online: October 9, 2021
DOI

https://doi.org/10.37917/ijeee.17.2.21

Abstract

Clustering is one of the most energy-efficient techniques for extending the lifetime of wireless sensor networks (WSNs). In a clustered WSN, each sensor node transmits the data acquired from the sensing field to the leader node (cluster head). The cluster head (CH) is in charge of aggregating and routing the collected data to the Base station (BS) of the deployed network. Thereby, the selection of the optimum CH is still a crucial issue to reduce the consumed energy in each node and extend the network lifetime. To determine the optimal number of CHs, this paper proposes an Enhanced Fuzzy-based LEACH (E-FLEACH) protocol based on the Fuzzy Logic Controller (FLC). The FLC system relies on three inputs: the residual energy of each node, the distance of each node from the base station (sink node), as well as the node's centrality. The proposed protocol is implemented using the Castalia simulator in conjunction with OMNET++, and simulation results indicate that the proposed protocol outperforms the traditional LEACH protocol in terms of network lifetime, energy consumption, and stability.

Keywords

References

  1. K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios,” IEEE Access, vol. 8. pp. 23022-23040, 2020, doi: 10.1109/ACCESS.2020.2970118.
  2. N. Islam, M. M. Rashid, F. Pasandideh, B. Ray, S. Moore, and R. Kadel, “A Review of Applications and Communication Technologies for Internet of Things (Iot) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming,” Sustain., vol. 13, no. 4, 2021, doi: 10.3390/su13041821.
  3. M. A. Mahdi and S. T. Hasson, “A Contribution to the Role of the Wireless Sensors in the IoT Era,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2–11, pp.1-6, 2017.
  4. A. A. H. Hassan, W. M. Shah, M. F. Iskandar, and A. A. J. Mohammed, “Clustering Methods for Cluster-based Routing Protocols in Wireless Sensor Networks: Comparative Study,” Int. J. Appl. Eng. Res., vol. 12, no. 21, pp. 11350-11360, 2017.
  5. O. S. Kwon, K. D. Jung, and J. Y. Lee, “WSN Protocol based on LEACH Protocol using Fuzzy,” Int. J. Appl. Eng. Res., vol. 12, no. 20, pp. 10013-10018, 2017. Al-Husain & Al-Suhail | 197
  6. J. S. Lee and C. L. Teng, “An Enhanced Hierarchical Clustering Approach for Mobile Sensor Networks Using Fuzzy Inference Systems,” IEEE Internet Things J., vol. 4, no. 4, pp. 1095-1103, 2017.
  7. Z. Cui, Y. Cao, X. Cai, J. Cai, and J. Chen, “Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things,” J. Parallel Distrib. Comput., vol. 132, pp. 217-229, 2019, , doi: 10.1016/j.jpdc.2017.12.014.
  8. G. Devika, D. Ramesh, and A. G. Karegowda, “Swarm Intelligence–Based Energy‐Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications,” in Swarm Intelligence Optimization, pp. 207-261, 2020.
  9. M. A. Tamtalini, A. E. B. El Alaoui, and A. El Fergougui, “ESLC-WSN: A Novel Energy Efficient Security Aware Localization and Clustering in Wireless Sensor Networks,” IEEE, pp. 1-6, 2020, doi: 10.1109/IRASET48871.2020.9092203.
  10. N. Sharma and V. Gupta, “Meta-heuristic based optimization of WSNs energy and lifetime-A Survey,” IEEE, pp. 369-374, 2020, doi: 10.1109/Confluence 47617.2020.9058294.
  11. T. Shankar, T. James, R. Mageshvaran, and A. Rajesh, “Lifetime Improvement in WSN using Flower Pollination Meta Heuristic Algorithm Based Localization Approach,” Indian J. Sci. Technol., vol. 9, no. 37, 2016, doi: 10.17485/ijst/2016/v9i37/102117.
  12. Deepshikha, P. Arora, and Varsha, “ENHANCED NN BASED RZ LEACH USING HYBRID ACO/PSO BASED ROUTING FOR WSNs,” IEEE, pp. 1-7, 2017, doi: 10.1109/ICCCNT.2017.8203901.
  13. C. Patra, S. Biswas, and S. Mullick, “Effect of Different Optimization Methods in Determining the Clusterheads in Wireless Sensor Network,” IEEE, pp. 497-502, 2018, doi: 10.1109/SPIN.2018.8474124.
  14. K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy, and A. Kannan, “Energy Aware Cluster and Neuro-Fuzzy Based Routing Algorithm for Wireless Sensor Networks in IoT,” Comput. Networks, vol. 151, pp. 211-223, 2019, doi: 10.1016/ j.comnet .2019.01.024.
  15. Y. S. Thakur and D. K. Sakravdia, “An improved reliability on wireless sensor network for energy irregularity models using fuzzy deep learning protocol: Fdlp,” Int. J. Sci. Technol. Res., vol. 9, no. 1, pp. 4384-4389, 2020.
  16. H. A. A. Al-Kashoash, Z. A. S. A. Rahman, and E. Alhamdawee, “Energy and RSSI Based Fuzzy Inference System for Cluster Head Selection in Wireless Sensor Networks,” pp. 102-105, 2019, doi: 10.1145/ 3321289 .3321319.
  17. S. Lata, S. Mehfuz, S. Urooj, and F. Alrowais, “Fuzzy Clustering Algorithm for Enhancing Reliability and Network Lifetime of Wireless Sensor Networks,” IEEE Access, vol. 8, pp. 66013-66024, 2020, doi: 10.1109/ ACCESS.2020.2985495.
  18. S. H. Abbas and I. M. Khanjar, “Fuzzy Logic Approach for Cluster-Head Election in Wireless Sensor Network,” Int. J. Eng. Res. Adv. Technol., vol. 5, no. 7, pp. 14-25, 2019, doi: 10.31695/ijerat.2019.3460.
  19. R. Logambigai and A. Kannan, “Fuzzy logic based unequal clustering for wireless sensor networks,” Wirel. Networks, vol. 22, no. 3, pp. 945-957, 2016, doi: 10.1007/s11276-015-1013-1.
  20. K. A. Ngo, T. T. Huynh, and D. T. Huynh, “Simulation Wireless Sensor Networks in Castalia,” pp. 39-44, 2018, doi: 10.1145/3193063.3193066.
  21. A. Rajput and V. B. Kumaravelu, “FCM clustering and FLS based CH selection to enhance sustainability of wireless sensor networks for environmental monitoring applications,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 1, PP. 1139-1159, 2021, doi: 10.1007/s12652-020-02159-9.