Cloud-Assisted Mobile Crowd Sensing for Traffic Congestion Control
- 484 Downloads
With the development of mobile cloud computing, wireless communication techniques, intelligent mobile terminals, and data mining techniques, Mobile Crowd Sensing (MCS) as a new paradigm of the Internet of Things can be used in traffic congestion control to provide more convenient services and alleviate the traffic problems. In this paper, we propose a cloud-assisted MCS architecture for urban transportation system. Then, we make the case for cloud-assisted MCS traffic congestion control by sensing data obtained continuously from a large set of smartphones carried by drivers. In this case, we consider a Mechanism of more Contributions and more Feedback Services (MCFS) to recruit, engage, and retain the participants. Finally, we pay close attention to the issues and challenges, including system architecture, resource limitations, and security and privacy.
KeywordsMobile crowd sensing Urban transportation Cloud computing
This work was supported by the Natural Science Foundation of Guangdong Province (Nos. 2016A030313734, 2015A030313746, and 2016A030313735), the Research Fund Program of Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing (CIMSOF2016004), the Fundamental Research Funds for the Central Universities (No. 2015ZZ079), the Major Projects for Numerical Control Machine (2015ZX04005001), and the National Natural Science Foundation of China (No. 61572220).
- 8.D. Zhang, J. Wan, Z. He, S. Zhao, K. Fan, S. Park and Z. Jiang, “Identifying Region-wide Functions Using Urban Taxicab Trajectories,” ACM Transactions on Embedded Computing Systems, vol. 15, no. 2, Article 36, 2016. doi: 10.1145/2821507.
- 9.Hull B, Bychkovsky V, Zhang Y, Chen K, Goraczko M, Miu A, Shih E, Balakrishnan H, and Madden S (2006) Cartel: a distributed mobile sensor computing system. In Proceedings of the 4th international conference on Embedded networked sensor systems. ACM, 125-138.Google Scholar
- 10.Mohan P, Padmanabhan VN, and Ramjee R (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM conference on Embedded network sensor systems. ACM, 323-336.Google Scholar
- 12.Mathur S, Jin T, Kasturirangan N, Chandrasekaran J, Xue W, Gruteser M, and Trappe W (2010) Parknet: driveby sensing of road-side parking statistics. In Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, 123-136.Google Scholar
- 13.Ali K, Al-Yaseen D, Ejaz A, Javed T, and Hassanein HS (2012) Crowdits: crowdsourcing in intelligent transportation systems. In Wireless Communications and Networking Conference (WCNC), 2012 IEEE. IEEE, 3307-3311.Google Scholar
- 17.Wan J, Zou C, Zhou K, Lu R, Li D (2014) IoT sensing framework with inter-cloud computing capability in vehicular networking. Electron Commer Res 3:1755–1764Google Scholar
- 18.Wan J, Liu J, Shao Z, Vasilakos A, Imran M and Zhou K (2016) Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors 16(1):88. doi: 10.3390/s16010088