Applied & Interdisciplinary
Ahmad Jalili; Fatemeh Babakordi
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 ...
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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.
