## 5 Discussion  
This study aimed to evaluate the performance of three deep learning models--CNN, LSTM, and Dense--under an IoT architecture for recognizing and processing urban sounds. IoT studies typically rely on a classical four-layer architecture comprising sensing, network, service, and application layers. However, this study proposes an innovative data processing model to minimize certain issues inherent in the classical architecture, such as data latency and processing delays, and to contribute to the development of more efficient IoT-based sensing systems. The proposed model incorporates fog computing to perform real-time data processing between the sensing and Network Layers, ensuring that dataare processed closer to its origin, thereby reducing the need for extensive transmission to a central server before analysis. The research focused on urban sound recognition using the UrbanSound8K dataset, which includes a diverse range of urban sounds such as traffic noise, sirens, human voices, and construction noises. Each model was trained and tested to classify these sounds in a  
Figure 6: Confusion matrix for dense layers. setup designed to simulate real-time data from the Sensing Layer. Performance evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score, ensuring that the substantial volume of data from the IoT Sensing Layerwas processed and interpreted effectively. The performance of CNN, LSTM, and Dense models was evaluated across various sound categories to provide a comprehensive comparison. Table 4 presents the results of this evaluation, summarizing the accuracy, precision, recall, and F1-score metrics for each model on the UrbanSound8K dataset. This comparison highlights the strengths and limitations of each model in classifying diverse urban sounds, offering valuable insights into their suitability for real-time urban sound recognition within an IoT-enabled fog computing framework. The results demonstrated that the CNN model achieved the highest overall accuracy at 90%. This finding aligns with previous studies that highlight the CNN models' superior performance in sound recognition tasks, especially when spectral representations like spectrograms and MFCCs are used, which preserve important spatial features for classification [12; 37]. [PERSON] et al. (2017) also found that CNN models excel in environmental sound classification, particularly when using spectral images and MFCC features. The findings of this study strongly support the conclusion that CNN models are highly effective in both image and sound recognition due to their capacity to capture spatial hierarchies within data, which is essential for distinguishing between different sound categories in urban environments [38]. This conclusion is further supported by recent work by [PERSON] et al. (2017) on music classification, which demonstrated CNN models' effectiveness in capturing complex auditory features applicable to urban sound recognition [39]. Similarly, 2023 studies have validated CNN models' applicability in urban environments, particularly when deployed on fog nodes to reduce latency and improve real-time capabilities in dense urban settings (e.g., traffic prediction and urban noise monitoring) [40; 41]. Other studies have similarly supported the high performance of CNN models. For example, [PERSON] and [PERSON] (2014) underscored CNN models' role in sound analysis, illustrating the model's utility for extracting detailed features from audio data [42]. Previous studies have demonstrated the effectiveness of CNN models in environmental sound classification. For instance, it was shown that a decision-level fusion-based two-stream CNN model achieved notable success in this domain [43], while deep CNN models enhanced with mixup techniques were reported to improve performance in classification tasks [44]. Building on these findings, recent advancements in CNN architectures, such as those presented by [PERSON] et al. (2023) and [PERSON] et al.