A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network

Combustible gas leakage remains a critical safety concern in industrial and indoor environments, necessitating the development of detection systems that are both accurate and practically deployable. This study presents a wireless gas detection system that integrates a gas sensor array, a low-power microcontroller with Zigbee-based communication, and a Back Propagation (BP) neural network optimized via a sequential hybrid strategy. Specifically, Particle Swarm Optimization (PSO) is employed for global parameter initialization, followed by Dung Beetle Optimization (DBO) for local refinement, jointly enhancing the network’s convergence speed and predictive precision. Experimental results confirm that the proposed PSO-DBO-BP model achieves high correlation coefficients (above 0.997) and low mean relative errors (below 0.25%) for all monitored gases, including hydrogen, carbon monoxide, alkanes, and smog. The model exhibits strong robustness in handling nonlinear responses and cross-sensitivity effects across multiple sensors, demonstrating its effectiveness in complex detection scenarios under laboratory conditions within embedded wireless sensor networks.