Abstract
Unstructured data gathered from various IoT sensors is rapidly increasing due to inexpensive electronic devices and high-speed networks. On the other hand, mobile edge computing (MEC) is an attractive data processing method that can shorten the communication distance and reduce the latency of computation-intensive tasks by distributing data to the edge servers close to the users, unlike processing data on clouds that are located far from users. In the present paper, we propose a specialized self-adaptive middleware for reconfiguration of image/video contents for adaptation to changes with the movement of a vehicle. The key concept behind this approach is to introduce the rule-based relocation of objects among sensor devices, edge servers, and existing clouds as a basic adaptation mechanism to recognize and track mobile vehicles. Experimental results show that tracking precision with a state-of-the-art tracker is up to 89% for MEC.
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- 1.
In our research, we use two types of sensor cameras, namely, day and night cameras, which work alternately. We captured an image every five seconds, and the memory capacity of these sensor devices allows data to be collected for up to two days.
- 2.
The processing speed for all sequences is above 60 fps.
- 3.
Note that these images are periodically captured by camera sensors scattered around the city; although these sensors have low-level computing power, their computational capabilities are sufficient for general low-level image processing and license plate recognition.
- 4.
Raspberry PI camera document. https://projects.raspberrypi.org.
- 5.
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. https://kubernetes.io/.
- 6.
IPOP (IP-Over-P2P) is an open-source user-centric software virtual network allowing end users to define and create their own virtual private networks (VPNs). http://ipop-project.org/.
- 7.
OpenALPR is an open-source automatic license plate recognition library that analyzes images and video streams to identify license plates.
- 8.
Due to the limited storage of the sensor camera, we can save data for up to two days then regularly free its space. In addition, the image sequence data in the sensor are periodically sent to the edge server every five minutes, and in which converted into a video stream.
- 9.
We used a low-luminance object and a vehicle recognition object for evaluation, the object size of the low-luminance object is 915 bytes, and the vehicle recognition object is 10 KB.
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Acknowledgement
This work was supported by the Japan Science Technology under CREST Grant JPMJCR1501.
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Sun, J., Yang, C., Tanjo, T., Sage, K., Aida, K. (2018). Implementation of Self-adaptive Middleware for Mobile Vehicle Tracking Applications on Edge Computing. In: Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J. (eds) Internet and Distributed Computing Systems. IDCS 2018. Lecture Notes in Computer Science(), vol 11226. Springer, Cham. https://doi.org/10.1007/978-3-030-02738-4_1
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