Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network

Document Type : Original Article


1 Faculty of Engineering, Department of Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran

2 Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran


Wireless sensor networks contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these networks is the limited lifetime of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the lifetime. Consequently, the primary concern is achieving optimal energy utilization to extend the network's lifetime over a logical duration. Depleting the batteries of the sensors means stopping the operation of the network, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased network lifetime. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the network, and the remaining energy is at least 11% higher than the best other algorithms.

Graphical Abstract

Improvement of Low Energy Adaptive Clustering Hierarchical Protocol Based on Genetic Algorithm to Increase Network Lifetime of Wireless Sensor Network


Main Subjects

  1. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. A survey on sensor networks. IEEE Communications magazine. 2002;40(8):102-14. 10.1109/MCOM.2002.1024422
  2. Mohammed FAB, Mekky N, Suleiman HH, Hikal NA. Sectored LEACH (S‐LEACH): An enhanced LEACH for wireless sensor network. IET Wireless Sensor Systems. 2022;12(2):56-66. 10.1049/wss2.12036
  3. Yadav A, Kohli N. Prolong Stability Period in Node Pairing Protocol for Wireless Sensor Networks. International Journal of Engineering, Transactions C: Aspects. 2021;34(12):2679-87. 10.5829/IJE.2021.34.12C.14
  4. Singh SK, Singh M, Singh DK. Energy-efficient homogeneous clustering algorithm for wireless sensor network. International Journal of Wireless & Mobile Networks (IJWMN). 2010;2(3):49-61.
  5. Shende MSS. A review on wireless sensor network: Its applications and challenges. International Journal of Computational Research in Engineering and Science. 2023;1(01):18-25.
  6. Hoang DB, Kamyabpour N, editors. Energy-constrained paths for optimization of energy consumption in Wireless Sensor Networks. 2013 Fourth International Conference on Networking and Distributed Computing; 2013: IEEE. 10.1109/ICNDC.2013.17
  7. Dhouib S. Hierarchical Coverage Repair Policies Optimization by Dhouib-Matrix-4 Metaheuristic for Wireless Sensor Networks using Mobile Robot. International Journal of Engineering, Transactions C: Aspects. 2023;36(12):2153-60. 10.5829/IJE.2023.36.12C.03
  8. Yaro AS, Malý F, Malý K. A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization. Emerging Science Journal. 2023;7(5):1762-70.
  9. Heinzelman WR, Chandrakasan A, Balakrishnan H, editors. Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd annual Hawaii international conference on system sciences; 2000: IEEE. 10.1109/HICSS.2000.926982
  10. Liu J-L, Ravishankar CV. LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks. International Journal of Machine Learning and Computing. 2011;1(1):79.
  11. Rahmanian A, Omranpour H, Akbari M, Raahemifar K, editors. A novel genetic algorithm in LEACH-C routing protocol for sensor networks. 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE); 2011: IEEE. 0.1109/CCECE.2011.6030631
  12. Peiravi A, Mashhadi HR, Hamed Javadi S. An optimal energy‐efficient clustering method in wireless sensor networks using multi‐objective genetic algorithm. International Journal of Communication Systems. 2013;26(1):114-26. 10.1002/dac.1336
  13. Abo-Zahhad M, Ahmed SM, Sabor N, Sasaki S. 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. 2014;5(3):47-72. 10.14257/ijeic.2014.5.3.05
  14. Zhang H, Zhang S, Bu W. A clustering routing protocol for energy balance of wireless sensor network based on simulated annealing and genetic algorithm. International Journal of Hybrid Information Technology. 2014;7(2):71-82. 10.14257/ijhit.2014.7.2.08
  15. Miao H, Xiao X, Qi B, Wang K, editors. Improvement and application of LEACH protocol based on genetic algorithm for WSN. 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD); 2015: IEEE. 10.1109/CAMAD.2015.7390517
  16. Hatamian M, Barati H, Movaghar A, Naghizadeh A. CGC: centralized genetic-based clustering protocol for wireless sensor networks using onion approach. Telecommunication systems. 2016;62:657-74. 10.1007/s11235-015-0102-x
  17. Bhatia T, Kansal S, Goel S, Verma AK. A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers & Electrical Engineering. 2016;56:441-55. 10.1016/j.compeleceng.2016.09.016
  18. Annushakumar G, Padmathilagam V. Analysis and Implementation of Q-Leach Protocol Based on Genetic Algorithm for WSN. International Journal of Scientific Research in Science, Engineering and Technology. 2018;5(3):1-12.
  19. Khunteta A, Bajpai A. Genetic algorithm with leach protocol for cluster head selection in wireless sensor networks. ICTACT J Commun Technol. 2020;11(2):2182-6. 10.21917/ijct.2019.0322
  20. Al Rasyid MUH, Mubtadai NR, Abdulrokhim J, editors. Performance Analysis LEACH Based Genetic Algoritm In Wireless Sensor Network. 2019 International Seminar on Application for Technology of Information and Communication (iSemantic); 2019: IEEE. 10.1109/ISEMANTIC.2019.8884332
  21. Bhola J, Soni S, Cheema GK. Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing. 2020;11:1281-8. 10.1007/s12652-019-01382-3
  22. Kumari M, Kaur G. A genetic algorithm based leach protocol for cluster head selection to enhance the network lifetime of wireless sensor network. ICTACT J Commun Technol. 2020;11:2182-6. 10.21917/ijct.2021.0371
  23. Harun HB, Islam MS, Hanif M, editors. Genetic algorithm for efficient cluster head selection in LEACH protocol of wireless sensor network. 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE); 2022: IEEE. 10.1109/ICAEEE54957.2022.9836352
  24. Sohail A. Genetic algorithms in the fields of artificial intelligence and data sciences. Annals of Data Science. 2023;10(4):1007-18. 10.1007/s40745-021-00354-9
  25. Singh SK, Singh M, Singh D. A survey of energy-efficient hierarchical cluster-based routing in wireless sensor networks. International Journal of Advanced Networking and Application (IJANA). 2010;2(02):570-80.