Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.298268

Keywords:

cluster head, sensor network, leach algorithm, energy optimization, battery life

Abstract

This research has a research object, namely the optimization of the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm in the context of wireless sensor networks. The problem in this research is the imbalance in energy consumption across clusters, which has an impact on battery life and affects network performance. Other problems include selecting a cluster head that is not focused so that it is difficult to balance network performance as well as computational limitations that require optimization. The results obtained from this research are in the form of optimizing the leaching algorithm by modifying the clustering-based leaching algorithm that will be used in wireless sensor networks. In carrying out modifications, this research uses several stages in the process of selecting sensor nodes that will become members who function as cluster heads in a cluster that will be used in a wireless sensor network. In the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm the cluster head will be selected based on the modified probability value. Modifying the algorithm by considering two factors, namely distance and remaining energy used in the Cluster Head selection process on the network and increasing network usage time must be based on the energy consumption used and then compared with the remaining energy. When modifying the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm, it is necessary to pay attention to the distance factor between the nodes on a sensor and the selected cluster so that it can result in increased network performance. Network lifetime is indicated by the average death time of the first Node in the network. This research is novel in producing a modified leaching algorithm by improving network performance and extending battery life so that it can be used for wireless sensor networks in the context of natural disaster mitigation

Author Biographies

Ferry Fachrizal, Universitas Sumatera Utara

Master of Computer

Department of Computer Science

Muhammad Zarlis, BINUS University

Master of Computer

Department of Information System Management

Poltak Sihombing, Universitas Sumatera Utara

Master of Computer

Department of Computer Science

Suherman Suherman, Universitas Sumatera Utara

Master of Computer

Department of Computer Science

References

  1. Wu, F., Wu, T., Yuce, M. R. (2019). Design and Implementation of a Wearable Sensor Network System for IoT-Connected Safety and Health Applications. 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). https://doi.org/10.1109/wf-iot.2019.8767280
  2. Liu, J., Zhao, Z., Ji, J., Hu, M. (2020). Research and application of wireless sensor network technology in power transmission and distribution system. Intelligent and Converged Networks, 1 (2), 199–220. https://doi.org/10.23919/icn.2020.0016
  3. Swamy, S. N., Jadhav, D., Kulkarni, N. (2017). Security threats in the application layer in IOT applications. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). https://doi.org/10.1109/i-smac.2017.8058395
  4. Shivalingagowda, C., Ahmad, H., Jayasree, P. V. Y., Sah, D. K. (2021). Wireless Sensor Network Routing Protocols Using Machine Learning. Lecture Notes in Networks and Systems, 99–120. https://doi.org/10.1007/978-981-16-0386-0_7
  5. Khutsoane, O., Isong, B., Gasela, N., Abu-Mahfouz, A. M. (2020). WaterGrid-Sense: A LoRa-Based Sensor Node for Industrial IoT Applications. IEEE Sensors Journal, 20 (5), 2722–2729. https://doi.org/10.1109/jsen.2019.2951345
  6. Ertam, F., Kilincer, I. F., Yaman, O., Sengur, A. (2020). A New IoT Application for Dynamic WiFi based Wireless Sensor Network. 2020 International Conference on Electrical Engineering (ICEE). https://doi.org/10.1109/icee49691.2020.9249771
  7. Yahya, O. H., Alrikabi, H., Aljazaery, I. A. (2020). Reducing the Data Rate in Internet of Things Applications by Using Wireless Sensor Network. International Journal of Online and Biomedical Engineering (IJOE), 16 (03), 107. https://doi.org/10.3991/ijoe.v16i03.13021
  8. Mejjaouli, S., Babiceanu, R. F. (2015). RFID-wireless sensor networks integration: Decision models and optimization of logistics systems operations. Journal of Manufacturing Systems, 35, 234–245. https://doi.org/10.1016/j.jmsy.2015.02.005
  9. You, G., Zhu, Y. (2020). Structure and Key Technologies of Wireless Sensor Network. 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC). https://doi.org/10.1109/csrswtc50769.2020.9372727
  10. Zhihui, H. (2015). Research on WSN Routing Algorithm Based on Energy Efficiency. 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). https://doi.org/10.1109/isdea.2015.178
  11. Jin, Z., Jian-Ping, Y., Si-Wang, Z., Ya-Ping, L., Guang, L. (2009). A Survey on Position-Based Routing Algorithms in Wireless Sensor Networks. Algorithms, 2 (1), 158–182. https://doi.org/10.3390/a2010158
  12. Bendjeddou, A., Laoufi, H., Boudjit, S. (2018). LEACH-S: Low Energy Adaptive Clustering Hierarchy for Sensor Network. 2018 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/isncc.2018.8531049
  13. Mittal, N., Singh, U., Salgotra, R. (2019). Tree-Based Threshold-Sensitive Energy-Efficient Routing Approach For Wireless Sensor Networks. Wireless Personal Communications, 108 (1), 473–492. https://doi.org/10.1007/s11277-019-06413-y
  14. Fallo, K., Wibisono, W., Pamungkas, K. N. P. (2019). Pengembangan mekanisme grid based clustering untuk peningkatan kinerja LEACH pada lingkungan Wireless Sensor Network. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 5 (2), 164. https://doi.org/10.26594/register.v5i2.1708
  15. Bhola, J., Soni, S., Cheema, G. K. (2019). Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 11 (3), 1281–1288. https://doi.org/10.1007/s12652-019-01382-3
  16. Jan, B., Farman, H., Javed, H., Montrucchio, B., Khan, M., Ali, S. (2017). Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey. Wireless Communications and Mobile Computing, 2017, 1–14. https://doi.org/10.1155/2017/6457942
  17. Palan, N. G., Barbadekar, B. V., Patil, S. (2017). Low energy adaptive clustering hierarchy (LEACH) protocol: A retrospective analysis. 2017 International Conference on Inventive Systems and Control (ICISC). https://doi.org/10.1109/icisc.2017.8068715
  18. Kumar, V., Malik, N., Dhiman, G., Lohani, T. K. (2021). Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network. Wireless Communications and Mobile Computing, 2021, 1–11. https://doi.org/10.1155/2021/5512879
  19. Cheikh, M., Simpson, O., Sun, Y. (2017). Energy efficient relay selection method for clustered wireless sensor network. In Proceedings of European Wireless 2017.
  20. Wu, M., Li, Z., Chen, J., Min, Q., Lu, T. (2022). A Dual Cluster-Head Energy-Efficient Routing Algorithm Based on Canopy Optimization and K-Means for WSN. Sensors, 22 (24), 9731. https://doi.org/10.3390/s22249731
  21. Cho, J. H., Lee, H. (2020). Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment. Applied Sciences, 10 (24), 9037. https://doi.org/10.3390/app10249037
  22. Sharmin, S., Ahmedy, I., Md Noor, R. (2023). An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO. Energies, 16 (5), 2487. https://doi.org/10.3390/en16052487
  23. Tadros, C. N., Shehata, N., Mokhtar, B. (2023). Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring. Sensors, 23 (12), 5733. https://doi.org/10.3390/s23125733
  24. Khalifeh, A., Abid, H., Darabkh, K. A. (2020). Optimal Cluster Head Positioning Algorithm for Wireless Sensor Networks. Sensors, 20 (13), 3719. https://doi.org/10.3390/s20133719
  25. Wang, J., Zhang, Z., Xia, F., Yuan, W., Lee, S. (2013). An Energy Efficient Stable Election-Based Routing Algorithm for Wireless Sensor Networks. Sensors, 13 (11), 14301–14320. https://doi.org/10.3390/s131114301
  26. Koyuncu, H., Tomar, G. S., Sharma, D. (2020). A New Energy Efficient Multitier Deterministic Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks. Symmetry, 12 (5), 837. https://doi.org/10.3390/sym12050837
Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks

Downloads

Published

2024-02-28

How to Cite

Fachrizal, F., Zarlis, M., Sihombing, P., & Suherman, S. (2024). Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks. Eastern-European Journal of Enterprise Technologies, 1(9 (127), 14–21. https://doi.org/10.15587/1729-4061.2024.298268

Issue

Section

Information and controlling system