With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by *71% while maintaining a classification accuracy of *98%. Under the same conditions, this process naturally reduces energy consumption by *71% compared to a WISN that sends the original uncompressed images.
Bibliographical noteFunding Information:
Authors J.Paek and J.Ko were supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT - previously MSIP) (Ministry of Science, ICT and Future Planning No.2017-0-00501, Development of Self-learnable common IoT SW Engine). This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-00501, Development of Self-learnable common IoT SW Engine).
© 2018 Ahn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)