A Fuzzy Control System for Performance Optimization in Wireless Sensor Networks

https://doi.org/10.26594/register.v11i1.5431

Authors

  • Abdelwahed Motwakel Prince Sattam bin Abdulaziz University (Saudi Arabia)
  • Refan Mohamed Almohamedh Prince Sattam bin Abdulaziz University (Saudi Arabia)
  • Hayfaa Tajelsier Ahmed Abdalrahman Prince Sattam Ibn Abdel Aziz University (Saudi Arabia)

Keywords:

Fuzzy Logic, Wireless Sensor Networks (WSNs), Energy Efficiency, Packet Delivery Ratio, Network Lifetime

Abstract

Wireless Sensor Networks (WSNs) play a vital role in numerous domains such as environmental monitoring, healthcare, industrial automation, and smart city infrastructures. Despite their growing significance, WSNs face persistent challenges, including limited energy resources, high data loss, network instability, and latency issues. To address these concerns, this study explores the integration of fuzzy logic to optimize WSN performance under uncertain and dynamic conditions. A fuzzy logic-based control system was designed to adaptively regulate key parameters, such as node energy, packet loss, and connectivity. Simulations were conducted with varying node densities (100, 200, and 300 nodes) to assess the effectiveness of the approach. The results revealed notable improvements: energy consumption was reduced by up to 0.65%, network lifetime extended by up to 0.28%, packet delivery ratio increased by up to 3.10%, and average latency decreased by up to 43.8%. These outcomes underscore the potential of fuzzy logic to enhance the adaptability, efficiency, and reliability of WSNs, offering a practical and scalable solution for performance optimization in real-world deployments.

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Author Biographies

Abdelwahed Motwakel, Prince Sattam bin Abdulaziz University

Department of Management Information Systems, College of Business Administration in Hawtat bani Tamim

Refan Mohamed Almohamedh , Prince Sattam bin Abdulaziz University

Department of Management Information Systems, College of Business Administration in Hawtat bani Tamim

Hayfaa Tajelsier Ahmed Abdalrahman, Prince Sattam Ibn Abdel Aziz University

Unit of Common Preparatory Year

References

[1] Godbole, Vaibhav. "Performance analysis of clustering protocol using fuzzy logic for wireless sensor network." IAES International Journal of Artificial Intelligence (IJ-AI) 1.3 (2012): 103-111.
[2] Elqeblawy, Noha, Ammar Mohammed, and Hesham A. Hefny. "A Proposed Fuzzy Logic Approach for Conserving the Energy of Data Transmission in the Temperature Monitoring Systems of Internet of Things." arXiv preprint arXiv:2204.05739 (2022).
[3] Xia, Feng, et al. "Fuzzy logic control based QoS management in wireless sensor/actuator networks." Sensors 7.12 (2007): 3179-3191.
[4] Singh, Ashutosh Kumar, N. Purohit, and S. Varma. "Fuzzy logic based clustering in wireless sensor networks: a survey." International Journal of Electronics 100.1 (2013): 126-141.
[5] Mendel, Jerry M. "Type-1 fuzzy sets and fuzzy logic." Explainable Uncertain Rule-Based Fuzzy Systems. Cham: Springer International Publishing, 2024. 17-73.
[6] Mastan Vali, Shaik. "Enhancing Coverage and Efficiency in Wireless Sensor Networks: A Review of Optimization Techniques." Advances in Engineering and Intelligence Systems 3.03 (2024): 39-52.
[7] Buratti, Chiara, et al. "An overview on wireless sensor networks technology and evolution." Sensors 9.9 (2009): 6869-6896.
[8] Hamzah, Abdulmughni, et al. "Energy-efficient fuzzy-logic-based clustering technique for hierarchical routing protocols in wireless sensor networks." Sensors 19.3 (2019): 561.
[9] Al Dallal, Haroon Rashid Hammood. "Clustering protocols for energy efficiency analysis in WSNS and the IOT." Problems of Information Society (2024): 18-24.
[10] Bagwari, Ashish, et al. "An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning." IEEE Access (2023).
[11] Gupta, Amit, and Rakesh Kumar Yadav. "Mathematical Modeling and Statistical Analysis of Efficient Cluster Head Selection Based LEACH Protocol for Wireless Sensor Networks."
[12] Shalu, S. Berin, and M. Vergin Raja Sarobin. "An optimized clustering approach for wireless sensor networks using improved squirrel search algorithm (ISSA)." IEEE Access (2024).
[13] Bhimshetty, Sampoorna, and Agughasi Victor Ikechukwu. "Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things." Indonesian Journal of Electrical Engineering and Computer Science 33.2 (2024): 971-980.

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Published

2025-06-27

How to Cite

[1]
A. Motwakel, R. M. Almohamedh, and H. T. A. Abdalrahman, “A Fuzzy Control System for Performance Optimization in Wireless Sensor Networks”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 11, no. 1, pp. 41–53, Jun. 2025.

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