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Abstract
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Using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Internet of Things (IoT) applications presents a promising approach to address the increasing demands of real-time data processing. IoT devices generate massive amounts of heterogeneous data that need to be processed efficiently with low latency to enable timely decision-making. Deep neural networks, especially CNNs and RNNs, have demonstrated significant potential in extracting meaningful features and modeling temporal dependencies from complex data streams, which are crucial for IoT environments [1].
CNNs, known for their excellence in spatial data analysis, are highly effective in processing images, sensor data, and other forms of structured input commonly found in IoT scenarios. Their ability to perform automatic feature extraction reduces the burden of manual feature engineering and enables robust performance even under constrained computational resources typical of IoT devices. On the other hand, RNNs, particularly architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), excel at capturing sequential patterns, making them ideal for time-series data processing and prediction tasks inherent to IoT sensor streams [4, 5].
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