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Open Access

VDCM: A Data Collection Mechanism for Crowd Sensing in Vehicular Ad Hoc Networks

College of Cyber Security, Jinan University, Guangzhou 510632, China
Guangdong Provincial Key Laboratory of Cyber and Information Security Vulnerability Research, Guangzhou 510643, China
College of Information Science Technology, Jinan University, Guangzhou 510632, China
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Abstract

With the rapid development of mobile devices, aggregation security and efficiency topics are more important than past in crowd sensing. When collecting large-scale vehicle-provided data, the data transmitted via autonomous networks are publicly accessible to all attackers, which increases the risk of vehicle exposure. So we need to ensure data aggregation security. In addition, low aggregation efficiency will lead to insufficient sensing data, making the data unable to provide data mining services. Aiming at the problem of aggregation security and efficiency in large-scale data collection, this article proposes a data collection mechanism (VDCM) for crowd sensing in vehicular ad hoc networks (VANETs). The mechanism includes two mechanism assumptions and selects appropriate methods to reduce consumption. It selects sub mechanism 1 when there exist very few vehicles or the coalition cannot be formed, otherwise selects sub mechanism 2. Single aggregation is used to collect data in sub mechanism 1. In sub mechanism 2, cooperative vehicles are selected by using coalition formation strategy and auction cooperation agreement, and multi aggregation is used to collect data. Two sub mechanisms use Paillier homomorphic encryption technology to ensure the security of data aggregation. In addition, mechanism supplements the data update and scoring steps to increase the amount of available data. The performance analysis shows that the mechanism proposed in this paper can safely aggregate data and reduce consumption. The simulation results indicate that the proposed mechanism reduces time consumption and increases the amount of available data compared with existing mechanisms.

References

[1]
A. K. Sandhu, Big data with cloud computing: Discussions and challenges, Big Data Mining and Analytics, vol. 5, no. 1, pp. 32–40, 2022.
[2]
R. K. Ganti, F. Ye, and H. Lei, Mobile crowdsensing: Current state and future challenges, IEEE Commun. Mag., vol. 49, no. 11, pp. 32–39, 2011.
[3]
N. Banerjee, T. Giannetsos, E. Panaousis, and C. C. Took, Unsupervised learning for trustworthy IoT, in Proc. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, Brazil, 2018, pp. 1–8.
[4]
G. Xu, H. Li, S. Liu, M. Wen, and R. Lu, Efficient and privacy-preserving truth discovery in mobile crowd sensing systems, IEEE Trans. Veh. Technol., vol. 68, no. 4, pp. 3854–3865, 2019.
[5]
X. Liu, K. Ota, A. Liu, and Z. Chen, An incentive game based evolutionary model for crowd sensing networks, Peer Peer Netw. Appl., vol. 9, no. 4, pp. 692–711, 2016.
[6]
S. Basudan, X. Lin, and K. Sankaranarayanan, A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing, IEEE Internet Things J., vol. 4, no. 3, pp. 772–782, 2017.
[7]
M. Sookhak, A. Gani, M. K. Khan, and R. Buyya, Dynamic remote data auditing for securing big data storage in cloud computing, Inf. Sci., vol. 380, pp. 101–116, 2017.
[8]
G. Sun, S. Sun, J. Sun, H. Yu, X. Du, and M. Guizani, Security and privacy preservation in fog-based crowd sensing on the internet of vehicles, J. Netw. Comput. Appl., vol. 134, no. 5, pp. 89–99, 2019.
[9]
P. Qian, M. Wu, and Z. Liu, A method on homomorphic encryption privacy-preserving for cloud computing, (in Chinese), Journal of Chinese Computer Systems, vol. 36, no. 4, pp. 840–844, 2015.
[10]
T. Kuo, K. C. Lin, and M. Tsai, On the construction of data aggregation tree with minimum energy cost in wireless sensor networks: NP-completeness and approximation algorithms, IEEE T. Comput., vol. 65, no. 10, pp. 3109–3121, 2016.
[11]
P. G. Naranjo, M. Shojafar, H. Mostafaei, Z. Pooranian, and E. Baccarelli, P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks, J. Supercomput., vol. 73, no. 2, pp. 733–755, 2017.
[12]
N. Javaid, M. Jafri, Z. Khan, N. Alrajeh, M. Imran, and A. Vasilakos, Chain-based communication in cylindrical underwater wireless sensor networks, Sensors, vol. 15, no. 2, pp. 3625–3649, 2015.
[13]
C. Liu and G. Cao, Distributed monitoring and aggregation in wireless sensor networks, in Proc. 29th Conference on Information Communications, San Diego, CA, USA, 2010, pp. 2097–2105.
[14]
K. Rabieh, M. Mahmoud, and M. Younis, Privacy-preserving route reporting schemes for traffic management systems, IEEE Trans. Veh. Technol., vol. 66, no. 3, pp. 2703–2713, 2017.
[15]
P. Zhao, Y. Fu, and C. Li, Privacy and security-oriented crowdsensing system framework for vehicular networks, (in Chinese), Mobile Communications, vol. 45, no. 6, pp. 37–42, 2021.
[16]
Y. Zhang, Y. Xu, Q. Wu, Y. Luo, Y. Xu, X. Chen, A. Anpalagan, and D. Zhang, Context awareness group buying in D2D networks: A coalition formation game-theoretic approach, IEEE Trans. Veh. Technol., vol. 67, no. 12, pp. 12259–12272, 2018
[17]
F. Xiong, H. Zheng, L. Ruan, H. Wang, L. Tang, X. Dong, and A. Li, Energy-saving data aggregation for multi-UAV system, IEEE Trans. Veh. Technol., vol. 69, no. 8, pp. 9002–9016, 2020.
[18]
M. Huang, Research on improved homomorphic encryption scheme based on integer, (in Chinese), China Science & Technology Overview, no. 16, pp. 22–23, 2021.
[19]
Y. Zhang and J. Ling, Improved algorithm for privacy-preserving association rules mining on horizontally distributed databases, (in Chinese), Computer Science, vol. 44, no. 8, pp. 157–161, 2017.
[20]
Z. Liu, F. Huang, J. Weng, K. Cao, Y. Miao, J. Guo, and Y. Wu, BTMPP: Balancing trust management and privacy preservation for emergency message dissemination in vehicular networks, IEEE Internet Things, vol. 8, no. 7, pp. 5386–5407, 2021.
[21]
Z. Liu, J. Weng, J. Ma, J. Guo, B. Feng, Z. Jiang, and K. Wei, TCEMD: A trust cascading-based emergency message dissemination model in VANETs, IEEE Internet Things, vol. 7, no. 5, pp. 4028–4048, 2020.
[22]
Z. Liu, J. Guo, F. Huang, D. Cai, Y. Wu, X. Chen, and K. K. Igorevich, Lightweight trustworthy message exchange in unmanned aerial vehicle networks, IEEE T. Intell. Transp., vol. 24, no. 2, pp. 2144–2157, 2023.
Big Data Mining and Analytics
Pages 391-403
Cite this article:
Yin J, Wei L, Liu Z, et al. VDCM: A Data Collection Mechanism for Crowd Sensing in Vehicular Ad Hoc Networks. Big Data Mining and Analytics, 2023, 6(4): 391-403. https://doi.org/10.26599/BDMA.2022.9020041

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Received: 20 June 2022
Revised: 18 September 2022
Accepted: 19 October 2022
Published: 29 August 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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