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SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing
Wang,Kafeng1; Xiong,Haoyi2; Zhang,Jie3; Chen,Hongyang4; Dou,Dejing5; Xu,Cheng Zhong6
Source PublicationIEEE Internet of Things Journal

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely , to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error.

KeywordInternet Of Vehicles (Iov). Magnetic Domains Magnetic Sensing Magnetic Sensors Monitoring Perpendicular Magnetic Anisotropy Roads Sensors Time-frequency Analysis Traffic Monitoring Vehicle Type Classification
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000714714400045
Scopus ID2-s2.0-85104634844
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorXiong,Haoyi
Affiliation1.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
2.Big Data Lab, Baidu Inc., Haidian, Beijing, China. (e-mail:
3.Key Laboratory of High Confidence Software Technologies, Peking University, Haidian, Beijing, China.
4.Research Center for Intelligent Network, Zhejiang Lab, Hangzhou, Zhejiang, China.
5.Big Data Lab, Baidu Inc., Haidian, Beijing, China.
6.State Key Laboratory of Internet of Things for Smart City, and Department of Computer and Information Science, University of Macau, Tapia, Macau, China.
Recommended Citation
GB/T 7714
Wang,Kafeng,Xiong,Haoyi,Zhang,Jie,et al. SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing[J]. IEEE Internet of Things Journal,2021,8(22):16666-16679.
APA Wang,Kafeng,Xiong,Haoyi,Zhang,Jie,Chen,Hongyang,Dou,Dejing,&Xu,Cheng Zhong.(2021).SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing.IEEE Internet of Things Journal,8(22),16666-16679.
MLA Wang,Kafeng,et al."SenseMag: Enabling Low-Cost Traffic Monitoring using Non-invasive Magnetic Sensing".IEEE Internet of Things Journal 8.22(2021):16666-16679.
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