DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
This addresses the challenge of handling varied sensor data for IoT applications, but it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of classifying heterogeneous IoT sensor data by proposing a deep learning model that combines CNN and Bi-GRU to learn local and global features, achieving an average improvement of 3.37% in Accuracy and 2.85% in F1-Score over state-of-the-art methods.
Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc. Such presented sequence data heterogeneity makes it difficult for traditional time series classification algorithms to perform well. Therefore, addressing the heterogeneity challenge demands learning not only the sub-patterns (local features) but also the overall pattern (global feature). To address the challenge of classifying heterogeneous IoT sensor data (e.g., categorizing sensor data types like temperature and humidity), we propose a novel deep learning model that incorporates both Convolutional Neural Network and Bi-directional Gated Recurrent Unit to learn local and global features respectively, in an end-to-end manner. Through rigorous experimentation on heterogeneous IoT sensor datasets, we validate the effectiveness of our proposed model, which outperforms recent state-of-the-art classification methods as well as several machine learning and deep learning baselines. In particular, the model achieves an average absolute improvement of 3.37% in Accuracy and 2.85% in F1-Score across datasets