DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT
  Sensor Data
Muhammad Sakib Khan Inan Kewen Liao Haifeng Shen Prem Prakash Jayaraman Dimitrios Georgakopoulos Ming Jian Tang
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