## 1 Introduction
By using memory cells and gating mechanisms, LSTM networks can retain long-term dependencies while mitigating the vanishing gradient problem common in traditional recurrent neural networks. This makes LSTM networks particularly effective for recognizing patterns in time-series data, such as fluctuating sound intensities or rhythmic acoustic events [18; 19]. However, their reliance on sequential processing can result in higher computational demands and slower inference times, particularly when applied to large datasets. Dense neural networks, also referred to as fully connected networks, provide a flexible framework for sound classification by connecting each neuron in one layer to every neuron in the subsequent layer. This architecture allows Dense networks to model complex relationships between features, making them suitable for analyzing large datasets where intricate patterns and dependencies exist. However, this dense connectivity can lead to increased computational costs, requiring optimization techniques such as dropout regularization and batch normalization to prevent overfitting and enhance efficiency [20; 21]. Despite their significant capabilities, deep learning models often require substantial computational resources. For example, CNNs and Dense networks rely heavily on GPUacceleration to process large-scale spectrograms, while LSTM models require substantial memory for maintaining temporal state information over long sequences. These demands often exceed the processing capacities of IoT devices, which are constrained by limited energy efficiency and computational power [22]. This limitation presents a critical challenge for deploying these models directly on IoT hardware in real-time urban sound recognition applications [23]. To address these constraints, fog computing provides a robust platform for integrating IoT and deep learning. This study evaluates the performance of CNN, LSTM, and Dense models within a fog computing framework, using the UrbanSound8K dataset, which contains 8732 recordings representing diverse urban sound categories. MFCCs were employed as the primary feature extraction method to capture key acoustic features, and simulations were conducted on the AWS platform to assess model performance using metrics such as precision, recall, macro F1-score, weighted F1-score, and confusion matrix. The results revealed that the CNN model achieved the highest accuracy (90%), followed by Dense (84%) and LSTM (81%) models. The CNN model's superior performance is attributed to its ability to effectively capture spectral features, whereas the LSTM model exhibited limitations in distinguishing overlapping sound categories. Dense models, while effective, performed slightly below the CNN model in handling complex spectral characteristics. In this study, although real-time data were not employed, the simulated fog computing environment was designed to provide a realistic assessment of model performance under conditions relevant to smart city applications. The findings demonstrate the feasibility of integrating fog computing with advanced deep learning models for scalable and efficient urban sound recognition. The proposed framework shows significant potential for real-time applications in environmental monitoring and public safety, underscoring the importance of decentrald architectures in future smart city systems. The contributions of this study include a comprehensive evaluation of CNN, LSTM, and Dense models for urban sound classification within a fog computing framework, the validation of MFCC-based feature extraction for urban sound data, demonstrating its effectiveness in capturing complex acoustic features, and insights into fog computing's ability to reduce latency and optimize computational efficiency for scalable smart city applications. The structure of this paper is as follows: Section 2 provides an in-depth discussion of the IoT architecture and its significance in smart city applications, establishing the technical foundation for this study.