## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Visualizable Interactions and Results  
We developed intuitive visual interfaces to provide insights into campus sound patterns and environmental parameters, including the HWASN performance, geographic distribution, A-weighted SPL curves, sound category proportions, and other environmental metrics. Users can input commands through a panel to control the system and display statistical data. These visual interactions significantly streamline the operation and present statistical data more intuitively, helping users better understand and analyze the information. Figure 13 illustrates the topology of the HWASN, where each node features a pop-up menu displaying information such as the node address, location, parent node address, packet loss count, and the expected number of transmitted packets. The packet loss count and expected transmissions are calculated using packet IDs embedded in each packet. The experimental results demonstrate that during the 15-minute real-world deployment, all data were successfully transmitted to the central processing module, achieving a packet loss rate of 0%. The two relay nodes transmitted 5419, 5419, and 5421 packets for the three sensor nodes. Since each audio data packet contains approximately 0.17 s of sound information [13], and the sampling period for the other four environmental parameters is 30 s, the theoretical number of packets sent by the relay nodes for each sensor node over the 15-min test is about 5414 packets (900 s \(\div\) 0.17 s/packet + 4 \(\times\) 900 s \(\div\) 30 s/packet), which aligns with the observed results. This confirms that the HWASN established stable communications between all nodes and the central processing module. Relay nodes that could not directly communicate with the central module effectively utilized nearer relay nodes to transmit data, validating the network's capability to handle indirect communication paths. Figure 14 presents the sound monitoring results collected from 7:17 PM to 7:32 PM on 30 August 2024, NZST. The first image displays the 1-second equivalent A-weighted SPL curve, while the second image shows the percentage of time the 1-second equivalent A-weighted SPL exceeded a preset 50 dBA threshold. The third image presents real-time classification results for sound segments surpassing the threshold using the fine-tuned CCBA model. According to [PERSON] et al., noise levels between 45-54 dBA can significantly increase the incidence of diseases, including but not limited to myocardial infarction [40]. Therefore, a middle value of 50 dBA was used as the preset threshold. In this test, the A-weighted SPL mostly ranged from 52 to 68 dBA, with levels above 50 dBA 95.4% of the time. We classified only the sound segments that exceeded 50 dBA, setting the model's classification confidence threshold at 0.5 to ensure that only results with confidence levels above 0.5 were considered valid. Sounds with lower confidence were categorized as \"Other sources.\" The model identified the following sound sources: 22.1% for buses and trucks, 56.9% for cars, 10.8% for pedestrian traffic lights, and 10.2% for other sources. Figure 12: The confusion matrix of the fine-tuned CCBA was used using the collected audio samples, where 0, 1, and 2 represent bus and truck, car, and pedestrian traffic lights, respectively. Upon comparing the model's output with the original audio, we found that the \"Bus and truck\" and \"Car\" classifications closely matched the ground truth. However, the predicted proportion for \"Pedestrian Traffic Light\" was overestimated at 10.8% when it should be around 5%. This overestimation may stem from the model confusing certain sounds typically heard in quieter environments--such as the silence during red traffic lights  
Figure 14: Fifteen-minute urban sound monitoring results from a sensor node. Figure 13: Network topology and node status. for cars--with other unfamiliar quiet sounds like speech or music. The classification result is satisfactory other than the overestimation with manual verifications.