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

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 ۱۰۰ nodes demonstrate significant performance improvements‎: ‎the proposed FNN extends network lifetime by  ۳۵%  compared to LEACH‎, ‎۲۸% compared to HEED‎, ‎and ۱۵% compared to ANN-based ELDC‎. ‎The First Node Dies (FND) is delayed by ۴۵%‎, ‎۳۸%‎, ‎and ۲۲% respectively‎, ‎while achieving ۲۵% lower energy consumption‎. ‎Results confirm the FNN approach's superior energy efficiency and network stability‎, ‎making it highly suitable for dynamic WSN applications‎.

کلیدواژه ها:

‎ Wireless sensor networks ، Energy Efficiency ، Clustering ، Fuzzy neural networks ، Intelligent systems