Measuring Traffic Dynamics at the Edge

Keywords: traffic dynamics meter, edge computing, approximate computing

Abstract

This work aims to measure the impact of approximate computing on a case study of traffic dynamics metering on a System-on-Chip edge computing device. Firstly, the study proposes a baseline implementation of the metering system in C++. To analyze the application in detail, study profiled the baseline using a built-in instrumented profiler, presenting the overall performance of each of its parts. During the hotspot analysis, some parts had optimization opportunities exploitable by multi-threading and approximate computing techniques, particularly frame skipping, which is inspired by the loop perforation approximate technique. The first optimization employed was multi-threading, which led to a 1.32x speedup on the application without introducing errors in the metrics. Then, the meter was optimized by using frame skipping. This work demonstrated that adaptatively modifying the number of frames skipped improved the error in the final metrics compared to keeping it fixed. In terms of performance, the frame skipping brought the overall speed up to 1.76x. Approximate computing, in particular, frame skipping, managed to contribute up to 25% of the overall speedup, managing to accelerate the meter from 8.7 frames per second to 15 fps in the most critical case in exchange for some numerical error on the final metrics.

References

Bolme, D. S., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010). Visual object tracking using adaptive correlation filters. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2544-2550. IEEE. https://doi.org/10.1109/CVPR.2010.5539960
California Department of Transportation (Caltrans). (n. d.). Soquel Ave. Santa Cruz, California, United States of America. https://cruz511.org/drive/traffic-conditions/traffic-cameras/
D'Andrea, E., Ducange, P., Lazzerini, B., & Marcelloni, F. (2015). Real-Time Detection of Traffic From Twitter Stream Analysis. IEEE Transactions on Intelligent Transportation Systems, 16, 2269-2283. IEEE. https://doi.org/10.1109/TITS.2015.2404431
Giridharan, E. N., Kadaieaswaran, M., Arunprasath, V., & Karthika, M. (2017, February). Big Data Solution for Improving Traffic Management System with Video Processing. International Journal of Engineering Science and Computing, 7(2), 4606–4609.
Hall, F. D. (1992). Traffic Stream Characteristics. Federal Highway Administration. https://www.fhwa.dot.gov/publications/research/operations/tft/chap2.pdf
Ministerio de Obras Públicas y Transportes. (n. d.). Cámaras viales CR. https://www.camarasvialescr.com.
Samie, F., Bauer, L., & Henkel, J. (2019). From Cloud Down to Things: An Overview of Machine Learning in Internet of Things. IEEE Internet of Things Journal, 6(3), 4921-4934. https://doi.org/10.1109/JIOT.2019.2893866
Sidiroglou-Douskos, S., Misailovic, S., Hoffmann, H., & Rinard, M. (2011). Managing Performance vs. Accuracy Trade-Offs with Loop Perforation. 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering,124–134. https://doi.org/10.1145/2025113.2025133
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 511-518). IEEE. https://doi.org/ 10.1109/CVPR.2001.990517
Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2017). A Survey on the Edge Computing for the Internet of Things. IEEE Access, 6, 6900-6919. https://doi.org/10.1109/ACCESS.2017.2778504
Zhu, L., Yu, F., Wang, Y., Ning, B., & Tang, T. (2019). Big Data Analytics in Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 383-398. https://doi.org/10.1109/TITS.2018.2815678
Published
2022-11-01
How to Cite
León-Vega, L., & Castro-Godínez, J. (2022). Measuring Traffic Dynamics at the Edge. Uniciencia, 36(1), 1-9. https://doi.org/10.15359/ru.36-1.39
Section
Original scientific papers (evaluated by academic peers)

Comentarios (ver términos de uso)