Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computingcapability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.
|Journal||ACM Transactions on Intelligent Systems and Technology|
|Early online date||24 Oct 2019|
|Publication status||Published - Dec 2019|
Bibliographical noteFunding Information:
The work is supported by Macao Science and Technology Development Fund under Grant No. 0026/2018/A1, National Natural Science Foundation of China (NFSC) under Grant No. 61672170, NSFC-Guangdong Joint Fund under Grant No. U1401251, the Science and Technology Planning Project of Guangdong Province under Grant No. 2015B090923004 and No. 2017A050501035, Science and Technology Program of Guangzhou under Grant No. 201807010058. Authors’ addresses: J. Zhou and H.-N. Dai, Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau; emails: email@example.com, firstname.lastname@example.org; H. Wang, Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, postboks 191, NO-2802 Gjøvik, Norway; email: email@example.com. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from firstname.lastname@example.org. © 2019 Association for Computing Machinery. 2157-6904/2019/10-ART67 $15.00 https://doi.org/10.1145/3339308
© 2019 Association for Computing Machinery.
- Convolutional neural network
- Cyber physical systems
- Jetson TX2 module
- Mobile edge computing
- Model compression