Document Type : Research Article
Authors
1 Department of Computer Engineering, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Gonbad Kavous, Iran.
2 Department of Mathematics and Statistics, Gonbad Kavous University, Gonbad Kavous, Iran.
10.30473/coam.2025.74415.1303
Abstract
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.
Highlights
- 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.
Keywords
Main Subjects
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