  Wireless Sensor Networks (WSNs) are indispensable for data-intensive
applications, necessitating efficient energy management and robust data fusion
techniques. This paper proposes an integrated framework leveraging fuzzy logic
and backpropagation neural networks (BPNN) to enhance energy efficiency and
data accuracy in WSNs. The model focuses on optimizing Cluster Head (CH)
selection using fuzzy logic, considering parameters such as energy levels,
proximity to the base station, and local density centrality. A Minimum Spanning
Tree (MST) algorithm is employed for energy-efficient data transmission from
sensor nodes to CHs, minimizing energy consumption during data routing.
BPNN-based data fusion at CHs reduces redundant data transmissions to the base
station, thereby optimizing energy utilization and enhancing overall network
performance. Simulation results demonstrate substantial improvements over
conventional methods, including a 30% increase in network longevity, 25%
improvement in data accuracy, and a 40% reduction in energy consumption. These
gains are attributed to the intelligent CH selection strategy enabled by fuzzy
logic, which ensures efficient resource allocation and minimizes energy
wastage. The effectiveness of the proposed approach is validated through
comprehensive simulations, showcasing its ability to prolong network lifetime,
maintain data integrity, and improve energy efficiency. The integration of
fuzzy logic and BPNN not only addresses the challenges of energy management and
data fusion in WSNs but also provides a scalable and adaptable framework for
future applications requiring reliable and sustainable sensor network
operations. The source code is available at
https://github.com/hikomal/BPNN_WSN.git
