Measuring Traffic Dynamics at the Edge
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.
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
Copyright (c) 2022 Shared by Journal and Authors (CC-BY-NC-ND)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors guarantee the journal the right to be the first publication of the work as licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors can set separate additional agreements for non-exclusive distribution of the version of the work published in the journal (eg, place it in an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. The authors have declared to hold all permissions to use the resources they provided in the paper (images, tables, among others) and assume full responsibility for damages to third parties.
4. The opinions expressed in the paper are the exclusive responsibility of the authors and do not necessarily represent the opinion of the editors or the Universidad Nacional.
Uniciencia Journal and all its productions are under Creative Commons Atribución-NoComercial-SinDerivadas 4.0 Unported.
There is neither fee for access nor Article Processing Charge (APC)