###### Abstract  
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. url  
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Conventional sound monitoring systems typically utilize professional-grade sensors that offer reliable and precise sound pressure level (SPL) measurements. However, these systems are often limited by high deployment costs and restricted spatial coverage, making large-scale implementation infeasible for many urban areas [6]. To address these limitations, Wireless Acoustic Sensor Networks (WASNs) have emerged as a promising solution for autonomous sound monitoring. By leveraging cost-effective hardware platforms, such as ESP8266 and Arduino, and employing efficient communication protocols like WiFi, Bluetooth, and LoRaWAN, WASNs offer a more flexible, scalable, and cost-efficient alternative for urban sound monitoring [7; 8; 9; 10; 11]. However, despite these benefits, WASNs still face significant challenges. Many systems lack sound classification capabilities, limiting their ability to identify sound sources accurately. Even systems with classification functionality often rely on expensive hardware, such as Raspberry Pi used by [PERSON] et al. [12], significantly raising the overall cost in high-density monitoring environments. To overcome these challenges, we have introduced the Hierarchical Wireless Acoustic Sensor Network (HWASN) in the previous study [13], a system designed to address the limitations of traditional setups. HWASN adopts a multi-hop communication architecture, wherein sensor nodes transmit encoded audio data to a central node via BLE and WiFi Mesh protocols for further processing, such as A-weighted SPL calculations and sound classification. By employing multi-hop communication and allowing each central node to manage multiple sensor nodes, this system significantly reduces costs compared to equipping each sensor node with its edge computing device for classification. HWASN demonstrated strong performance in laboratory testing, including accurate sound sampling, reliable SPL calculations, and efficient data transmission. However, the system's real-world deployment and evaluation remained unexplored, making the system lack comprehensive assessment in practical implementations. Real-world environments can introduce various challenges, such as unpredictable interference, dynamic acoustic conditions, and potential issues with network stability [14]. These factors are critical for deploying wireless sensor networks in urban sound monitoring, smart cities, and environmental noise assessments.