Incidents could not always be avoided, resulting in significant losses for the state property and people's safety. Effective monitoring of incidents is becoming more and more important. Wireless sensor networks (WSNs) are widely used in environmental monitoring. On the basis of the existing wireless sensor network node deployment model, a network node deployment model was proposed, which was used in the monitoring of unexpected accidents. The model area was divided into the core area and the evacuation area, and different monitoring nodes deployment plans were applied in different areas. The simulation experiment is carried out by MATLAB, and the simulation results of the node coverage are obtained. Results show that the model for the practical application of the node deployment provides an effective deployment plan, as well as offers a strong basis for real-time monitoring and post-accident emergency evacuation.

The depth of corrosion pits is critical to the safe operation of pipelines. Conventionally, monitoring of metal pipeline corrosion has generally relied on a single technique. Among these, the ultrasonic thickness measurement method and the field signature method are widely used in pipeline monitoring systems. The ultrasonic method has high reliability and accuracy for monitoring the thickness of pipelines, but it is limited by coupling and measurement range. Additionally, the field signature method often suffers from inadequate identification of corrosion pits and lower measurement accuracy. To address these limitations, a multi-sensor fusion model is proposed to monitor corrosion in metal pipelines. The multi-sensor fusion model is constructed by alternately arranging ultrasonic sensors and field signature probes, and a dedicated fusion algorithm is designed. The integrated model leverages the complementary strengths of both techniques while mitigating their individual shortcomings. Furthermore, an artificial neural network is employed to accurately identify pitting depth, thereby resolving the challenge in discriminating corrosion pit depths. Experimental results demonstrate that the multi-sensor fusion model can overcome the inherent drawbacks associated with a single technique. Consequently, it enhances the overall reliability, measurement accuracy, and operational range of the pipeline corrosion measurement system.

The liquid level sensor based on ultrasonic guided waves has been applied in the aircraft fuel level measurement system due to its advantages such as high accuracy, strong anti-electromagnetic interference ability, and immunity to water accumulation. It compensates for the shortcomings of capacitive liquid level sensors. However, high order mode waves, interference noise and aircraft vibrations have affected the accuracy and reliability of the ultrasonic liquid level sensor. A conical orthogonal matrix ring structure is designed to eliminate interference waves through mechanisms of acoustic scattering and absorption, leading to a notable improvement in signal-to-noise ratio and operational reliability. Furthermore, an envelope superposition algorithm grounded in multi-wave superposition theory is proposed for processing ultrasonic signals under dynamic liquid level conditions. This algorithm not only counteracts vibration-induced signal degradation but also further enhances signal-to-noise ratio. Experiments show that the proposed structure and algorithm have effectively reduced the interference from clutter and overcome the influence caused by aircraft vibration. The signal-to-noise ratio at low liquid level has increased from 3.3 dB to 13.9 dB. The signal-to-noise ratio during low-frequency vibration has increased from 6.4 dB to 11.5 dB. At the same time, it has enhanced the reliability of the sensors and their ability to adapt to the onboard environment, which holds significant engineering application value.