An Integrated Hierarchical Wireless Acoustic Sensor Network and Optimized Deep Learning Model for Scalable Urban Sound and Environmental Monitoring

Bo Peng, Kevin I-Kai Wang, Waleed H. Abdulla

Urban sound encompasses various acoustic events, from critical safety-related sound to everyday environmental noise. In response to the need for comprehensive and scalable sound monitoring, this study introduces an integrated system combining the Hierarchical Wireless Acoustic Sensor Network (HWASN) with the new proposed end-to-end CNN-CNN-BiLSTM-Attention (CCBA) sound classification model. HWASN facilitates large-scale, scalable sound data collection and transmission through a multi-hop architecture. At the same time, the CCBA model, optimized for Jetson Nano, delivers high-accuracy classification in noisy environments with minimal computational overhead. The CCBA model is trained using distillation techniques, achieving up to a 71-fold speed-up compared to its teacher system. Real-world deployments demonstrate the system’s robust performance under dynamic acoustic conditions. Combining HWASN’s scalability with CCBA’s classification efficiency provides a versatile and long-term solution for comprehensive urban sound monitoring. Additionally, other environmental parameters, such as air quality, light intensity, temperature, humidity, and atmospheric pressure, are sampled using this system to enhance its application in smart city management, urban planning, and public safety, addressing various modern urban needs.