Enabling real-time road anomaly detection via mobile edge computing
To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, f...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-11-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719891319 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405336955387904 |
|---|---|
| author | Zengwei Zheng Mingxuan Zhou Yuanyi Chen Meimei Huo Dan Chen |
| author_facet | Zengwei Zheng Mingxuan Zhou Yuanyi Chen Meimei Huo Dan Chen |
| author_sort | Zengwei Zheng |
| collection | DOAJ |
| description | To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node. (2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work. |
| format | Article |
| id | doaj-art-53877ae3e8c546bb84ccc6211fe2ee8a |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-53877ae3e8c546bb84ccc6211fe2ee8a2025-08-20T03:36:42ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-11-011510.1177/1550147719891319Enabling real-time road anomaly detection via mobile edge computingZengwei Zheng0Mingxuan Zhou1Yuanyi Chen2Meimei Huo3Dan Chen4Hangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaHangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou, ChinaHangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou, ChinaHangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou, ChinaTo discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node. (2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.https://doi.org/10.1177/1550147719891319 |
| spellingShingle | Zengwei Zheng Mingxuan Zhou Yuanyi Chen Meimei Huo Dan Chen Enabling real-time road anomaly detection via mobile edge computing International Journal of Distributed Sensor Networks |
| title | Enabling real-time road anomaly detection via mobile edge computing |
| title_full | Enabling real-time road anomaly detection via mobile edge computing |
| title_fullStr | Enabling real-time road anomaly detection via mobile edge computing |
| title_full_unstemmed | Enabling real-time road anomaly detection via mobile edge computing |
| title_short | Enabling real-time road anomaly detection via mobile edge computing |
| title_sort | enabling real time road anomaly detection via mobile edge computing |
| url | https://doi.org/10.1177/1550147719891319 |
| work_keys_str_mv | AT zengweizheng enablingrealtimeroadanomalydetectionviamobileedgecomputing AT mingxuanzhou enablingrealtimeroadanomalydetectionviamobileedgecomputing AT yuanyichen enablingrealtimeroadanomalydetectionviamobileedgecomputing AT meimeihuo enablingrealtimeroadanomalydetectionviamobileedgecomputing AT danchen enablingrealtimeroadanomalydetectionviamobileedgecomputing |