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Jalili, Ahmad, Babakordi, Fatemeh. (1404). A New Energy-Efficient Clustering in Wireless Sensor Networks Using an Adaptive Fuzzy Neural Network Approach. فصلنامه علمی زیست فناوری گیاهان زراعی, 10(2), 75-102. doi: 10.30473/coam.2025.74415.1303
Ahmad Jalili; Fatemeh Babakordi. "A New Energy-Efficient Clustering in Wireless Sensor Networks Using an Adaptive Fuzzy Neural Network Approach". فصلنامه علمی زیست فناوری گیاهان زراعی, 10, 2, 1404, 75-102. doi: 10.30473/coam.2025.74415.1303
Jalili, Ahmad, Babakordi, Fatemeh. (1404). 'A New Energy-Efficient Clustering in Wireless Sensor Networks Using an Adaptive Fuzzy Neural Network Approach', فصلنامه علمی زیست فناوری گیاهان زراعی, 10(2), pp. 75-102. doi: 10.30473/coam.2025.74415.1303
Jalili, Ahmad, Babakordi, Fatemeh. A New Energy-Efficient Clustering in Wireless Sensor Networks Using an Adaptive Fuzzy Neural Network Approach. فصلنامه علمی زیست فناوری گیاهان زراعی, 1404; 10(2): 75-102. doi: 10.30473/coam.2025.74415.1303

A New Energy-Efficient Clustering in Wireless Sensor Networks Using an Adaptive Fuzzy Neural Network Approach

Control and Optimization in Applied Mathematics
مقاله 3، دوره 10، شماره 2 - شماره پیاپی 20، مهر 2025، صفحه 75-102    اصل مقاله (1.09 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2025.74415.1303
نویسندگان
Ahmad Jalili* 1؛ Fatemeh Babakordi2
1Department of Computer Engineering‎, ‎Faculty of Basic Sciences and Engineering‎, ‎Gonbad Kavous University‎, ‎Gonbad Kavous‎, ‎Gonbad Kavous‎, ‎Iran.
2Department of Mathematics and Statistics‎, ‎Gonbad Kavous University‎, ‎Gonbad Kavous‎, ‎Iran‎.
چکیده
Energy constraint is the most critical challenge in Wireless Sensor Networks (WSNs)‎, ‎particularly in dynamic environments with mobile nodes‎. ‎This paper proposes an intelligent clustering protocol based on Fuzzy Neural Networks (FNN) that adaptively optimizes energy consumption by dynamically selecting cluster heads and determining optimal cluster configurations‎. ‎The FNN integrates fuzzy logic's uncertainty handling with neural networks' learning capabilities‎, ‎using key parameters including residual energy‎, ‎node distance‎, ‎neighbor density‎, ‎and signal-to-noise ratio‎. ‎Unlike static clustering approaches such as LEACH and HEED‎, ‎our method continuously adapts to changing network conditions through real-time parameter evaluation‎. ‎Extensive MATLAB simulations with 100 nodes demonstrate significant performance improvements‎: ‎the proposed FNN extends network lifetime by  35%  compared to LEACH‎, ‎28% compared to HEED‎, ‎and 15% compared to ANN-based ELDC‎. ‎The First Node Dies (FND) is delayed by 45%‎, ‎38%‎, ‎and 22% respectively‎, ‎while achieving 25% lower energy consumption‎. ‎Results confirm the FNN approach's superior energy efficiency and network stability‎, ‎making it highly suitable for dynamic WSN applications‎.

تازه های تحقیق
  • Develop and rigorously evaluate an intelligent, dynamic clustering protocol for Wireless Sensor Networks (WSNs) based on a Fuzzy Neural Network (FNN) to optimize energy consumption and extend network lifetime under real-time, changing conditions.
  • Determine the optimal number of clusters and select appropriate cluster heads (CHs) in each round based on the current network state.
  • Incorporate residual energy, node distances, neighbor density, and SNR into the FNN decision process to guide clustering.
  • Enable dynamic clustering by leveraging the synergy of fuzzy logic and neural learning to handle uncertainty and evolving network conditions.
  • Assess the proposed FNN-based clustering in terms of network lifetime, stability, and energy consumption under realistic simulations.
  • Compare against static/probabilistic approaches, such as LEACH and HEED, as well as a relevant ANN-based method (e.g., ELDC).
  • Demonstrate the effective application of FNNs for multi-objective optimization in distributed WSNs, highlighting how the fusion of fuzzy logic with adaptive learning improves decisions under uncertainty.
  • Provide a framework that demonstrates how hybrid intelligent systems can address complex resource management problems in dynamic sensor networks.
  • Offer a viable pathway to significantly extend the operational lifetime of WSNs in dynamic or mobile environments, enhancing the feasibility and reliability of deployments.
  • Inform design principles for more intelligent and autonomous sensor systems in next-generation applications, where energy efficiency and robust adaptation are critical.
کلیدواژه‌ها
‎ Wireless sensor networks؛ Energy efficiency؛ Clustering؛ Fuzzy neural networks؛ Intelligent systems
مراجع

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