A deep residual computation model for heterogeneous data learning in smart Internet of Things

Abstract

Smart Internet of Things (smart IoT) have emerged as a transformative computing paradigm recently. This new approach has made great contributions in the area of cyber–physical–social systems by employing various computational intelligence techniques like deep learning, for analyzing data, especially heterogeneous data from sensing and wireless communication. As a representative example of deep learning, deep residual networks have achieved excellent performance for big data feature learning since they can avoid gradient vanishing issues in deep learning models effectively. Unfortunately, they could not learn features for heterogeneous data, especially multi-modal data, in smart IoT. This paper proposes a deep residual computation model by generalizing the deep residual network in the tensor space. Especially, each multi-modal data object is represented as a tensor, while all hidden layers are also represented as tensors. Furthermore, we propose a tensor back-propagation algorithm to train the parameters of the deep residual computation model. Finally, we conduct extensive experiments to evaluate the presented deep residual model by comparing with the existing models such as multi-modal deep learning models, 3D deep residual models, deep computation models, and deep convolutional computation models. Results show that the proposed model produces more accurate classification results than other models for heterogeneous data feature learning in cyber–physical–social systems.