Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Yinghua LiThis email address is being protected from spambots. You need JavaScript enabled to view it.

XI’AN MingDe Institute of Technology, Xi’an, Shanxi 710124, China


 

 

Received: July 29, 2023
Accepted: December 16, 2023
Publication Date: February 20, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202412_27(12).0004  


Clustering in Wireless Sensor Networks (WSNs) has emerged as a critical strategy for improving network efficiency and extending the network’s lifespan. Optimizing energy efficiency becomes paramount as the demand for WSNs grows in applications such as underground mining, healthcare, security surveillance, and environmental monitoring. This work introduces a novel hybrid clustering approach that combines the WaterCycle Algorithm (WCA) with a fuzzy logic system to address the inherent challenges in clustering WSNs. The primary motivation for this research is to enhance energy efficiency, prolong network operation, and address the shortcomings of traditional clustering methods. These shortcomings include unbalanced clusters, suboptimal cluster head selection, and limited adaptability to diverse application requirements. The proposed approach aims to overcome these limitations by utilizing the WCA’s inspiration from the natural water cycle coupled with a dynamic fuzzy logic system for cluster head selection. The proposed approach is tested for different network sizes and compared with existing algorithms. The results suggested that the suggested algorithm is superior to its competitors regarding network lifetime and energy consumption.


Keywords: WSN; Routing; Clustering; Energy efficiency; Optimization.


  1. [1] B. Pourghebleh and N. J. Navimipour, (2017) “Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research" Journal of Network and Computer Applications 97: 23–34. DOI: 10.1016/j.jnca.2017.08.006.
  2. [2] R. Singh, A. Mehbodniya, J. L. Webber, P. Dadheech, G. Pavithra, M. S. Alzaidi, and R. Akwafo, (2022) “Analysis of network slicing for management of 5G networks using machine learning techniques" Wireless Communications and Mobile Computing 2022: DOI: 10.1155/2022/9169568.
  3. [3] S. J. Bhat and S. KV, (2022) “A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm" Peer-to-Peer Networking and Applications 15: 1473–1485. DOI: 10.1007/s12083-022-01302-x.
  4. [4] B. Pourghebleh and V. Hayyolalam, (2020) “A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things" Cluster Computing 23: 641–661. DOI: 10.1007/s10586-019-02950-0.
  5. [5] P. He, N. Almasifar, A. Mehbodniya, D. Javaheri, and J. L. Webber, (2022) “Towards green smart cities using Internet of Things and optimization algorithms: A systematic and bibliometric review" Sustainable Computing: Informatics and Systems 36: 100822. DOI: 10.1016/j.suscom.2022.100822.
  6. [6] S. Palanisamy, S. Sankar, R. Somula, and G. G. Deverajan, (2021) “Communication trust and energy-aware routing protocol for WSN using DS theory" International Journal of Grid and High Performance Computing (IJGHPC) 13: 24–36.
  7. [7] J. Chen, D. Zhang, J. Zhang, T. Zhang, H. Zhu, and J. Qiu, (2020) “New approach of energy-efficient hierarchical clustering based on neighbor rotation for RWSN" IEEE Access 8: 123123–123134. DOI: 10.1109/ACCESS.2020.3007478.
  8. [8] B. Pourghebleh, N. Hekmati, Z. Davoudnia, and M. Sadeghi, (2022) “A roadmap towards energy-efficient data fusion methods in the Internet of Things" Concurrency and Computation: Practice and Experience 34: e6959. DOI: 10.1002/cpe.6959.
  9. [9] S. S. Sefati and S. Halunga, (2022) “A hybrid service selection and composition for cloud computing using the adaptive penalty function in genetic and artificial bee colony algorithm" Sensors 22: 4873. DOI: 10.3390/s22134873.
  10. [10] S. Aghakhani, A. Larijani, F. Sadeghi, D. Martín, and A. A. Shahrakht, (2023) “A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems" Electronics 12: 2263. DOI: 10.3390/electronics12102263.
  11. [11] B. Pourghebleh, A. A. Anvigh, A. R. Ramtin, and B. Mohammadi, (2021) “The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments" Cluster Computing 24: 2673–2696. DOI: 10.1007/s10586-021-03294-4.
  12. [12] S. Mahmoudinazlou, A. Alizadeh, J. Noble, and S. Eslamdoust, (2023) “An improved hybrid ICA-SA metaheuristic for order acceptance and scheduling with time windows and sequence-dependent setup times" Neural Computing and Applications: 1–19. DOI: 10.1007/s00521-023-09030-w.
  13. [13] T. Gera, J. Singh, A. Mehbodniya, J. L. Webber, M. Shabaz, and D. Thakur, (2021) “Dominant feature selection and machine learning-based hybrid approach to analyze android ransomware" Security and Communication Networks 2021: 1–22. DOI: 10.1155/2021/7035233.
  14. [14] S. N. H. Bukhari, J. Webber, and A. Mehbodniya, (2022) “Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates" Scientific Reports 12: 7810. DOI: 10.1038/s41598-022-11731-6.
  15. [15] J. Webber, A. Mehbodniya, Y. Hou, K. Yano, and T. Kumagai. “Study on idle slot availability prediction for WLAN using a probabilistic neural network”. In: IEEE, 2017, 1–6. DOI: 10.23919/APCC.2017.8304030.
  16. [16] C. Han and X. Fu, (2023) “Challenge and opportunity: deep learning-based stock price prediction by using Bidirectional LSTM model" Frontiers in Business, Economics and Management 8: 51–54.
  17. [17] B. M. Jafari, M. Zhao, and A. Jafari, (2022) “Rumi: An intelligent agent enhancing learning management systems using machine learning techniques" Journal of Software Engineering and Applications 15: 325–343.
  18. [18] M. Sabet and H. Naji, (2016) “An energy efficient multilevel route-aware clustering algorithm for wireless sensor networks: A self-organized approach" Computers & Electrical Engineering 56: 399–417. DOI: 10.1016/j.compeleceng.2016.07.009.
  19. [19] R. Yarinezhad and M. Sabaei, (2021) “An optimal cluster-based routing algorithm for lifetime maximization of Internet of Things" Journal of Parallel and Distributed Computing 156: 7–24. DOI: 10.1016/j.jpdc.2021.05.005.
  20. [20] Y. U. Xiu-Wu, Y. U. Hao, L. Yong, and X. Ren-rong, (2020) “A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks" Computer Networks 167: 106994. DOI: 10.1016/j.comnet.2019.106994.
  21. [21] A. Ghosal, S. Halder, and S. K. Das, (2020) “Distributed on-demand clustering algorithm for lifetime optimization in wireless sensor networks" Journal of Parallel and Distributed Computing 141: 129–142. DOI: 10.1016/j.jpdc.2020.03.014.
  22. [22] M. Mohseni, F. Amirghafouri, and B. Pourghebleh, (2023) “CEDAR: A cluster-based energy-aware data aggregation routing protocol in the internet of things using capuchin search algorithm and fuzzy logic" Peer-toPeer Networking and Applications 16: 189–209. DOI: 10.1007/s12083-022-01388-3.
  23. [23] H. A. Shayanfar, O. Abedinia, N. Amjady, and S. Rajaei. “Improved ABC and fuzzy controller based on consonant FACTS devices”. In: The Steering Committee of The World Congress in Computer Science, Computer . . ., 2016, 60.
  24. [24] I. Ahmadian, O. Abedinia, and N. Ghadimi, (2014) “Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization" Frontiers in Energy 8: 412–425. DOI: 10.1007/s11708- 014-0315-9.
  25. [25] O. Abedinia and N. Amjady, (2014) “Optimal Design Of Fuzzy Power System Stabilizer In Multi-Machine Environment By Harmony Search Algorithm" Journal of Modeling in Engineering 12: 1–15.
  26. [26] O. Abedinia, M. S. Naderi, and A. Ghasemi. “Robust LFC in deregulated environment: Fuzzy PID using HBMO”. In: IEEE, 2011, 1–4. DOI: 10.1109/EEEIC.2011.5874843.
  27. [27] M. Nasir, A. Sadollah, Y. H. Choi, and J. H. Kim, (2020) “A comprehensive review on water cycle algorithm and its applications" Neural Computing and Applications 32: 17433–17488. DOI: 10.1007/s00521-020-05112-1.


    



 

2.1
2023CiteScore
 
 
69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.