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Real-Time Density Detection in Connected Vehicles: Design and Implementation
Kong LH(孔令和)1; Xue, Guangtao1; Ghafoor, Kayhan Zara2; Hussain, Rasheed3; Sheng H(盛浩)4; Zeng P(曾鹏)5
Indexed BySCI
WOS IDWOS:000447859300008
Contribution Rank5
Funding OrganizationNational Key R&D Program of China (No. 2017YFB1002000) ; NSFC (No. 61672349) ; Joint Key Project of NSFC (No. U1736207) ; Macao Science and Technology Development Fund (No. 138/2016/A3) ; Program of Introducing Talents of Discipline to Universities ; Open Fund of the State Key Laboratory of Software Development Environment under grant SKLSDE-2017ZX-09 ; Project of Experimental Verification of the Basic Commonness and Key Technical Standards of the Industrial Internet Network Architecture
AbstractDensity information plays an important role in intelligent transportation systems for not only traffic control but also information sharing. Existing products have been able to provide coarse-grained density services. For example, Google Maps can illustrate the traffic conditions by different colors via Internet connection. Vehicle-to-vehicle wireless communications can locally acquire the density by information exchange and neighbor counting. However, either the Internet access or one-by-one counting leads to a sub-second-level delay, which cannot satisfy real-time vehicular applications such as autonomous navigation and data dissemination. To speed up density acquisition, we propose an RDD system. Leveraging the frequency resource, RDD divides the wireless channel into fine-grained subchannels and detects the neighbors in a parallel manner. We establish a testbed using software defined radios and experimentally validate RDD. Moreover, to evaluate RDD in high-density scenarios, extensive simulations are conducted based on real collected data. Both the experiment and simulation results demonstrate that RDD achieves 100 ms level density detection, while the state-of-the-art time-domain acceleration method is at the 10 ms level.
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorSheng H(盛浩)
Affiliation1.Department of Computer Science and Engineering, Shanghai Jiao Tong University
2.Department of Computer Science, Cihan University-Erbil
3.Secure System and Network Engineering (SNE), Innopolis University, Russia
4.School of Computer Science and Engineering, Beihang University
5.Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS)
Recommended Citation
GB/T 7714
Kong LH,Xue, Guangtao,Ghafoor, Kayhan Zara,et al. Real-Time Density Detection in Connected Vehicles: Design and Implementation[J]. IEEE COMMUNICATIONS MAGAZINE,2018,56(10):64-70.
APA Kong LH,Xue, Guangtao,Ghafoor, Kayhan Zara,Hussain, Rasheed,Sheng H,&Zeng P.(2018).Real-Time Density Detection in Connected Vehicles: Design and Implementation.IEEE COMMUNICATIONS MAGAZINE,56(10),64-70.
MLA Kong LH,et al."Real-Time Density Detection in Connected Vehicles: Design and Implementation".IEEE COMMUNICATIONS MAGAZINE 56.10(2018):64-70.
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