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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.


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

Show Author's information Juli Yin1Linfeng Wei1( )Zhiquan Liu1,2Xi Yang3Hongliang Sun1Yudan Cheng1Jianbin Mai1
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

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.

Keywords: data collection, crowd sensing, vehicular ad hoc networks (VANETs), data aggregation security

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Received: 20 June 2022
Revised: 18 September 2022
Accepted: 19 October 2022
Published: 29 August 2023
Issue date: December 2023

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© The author(s) 2023.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 62272195 and 61802146); the Guangdong Province Science and Technology Planning Project (No. KTP20200022); the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011017); the Science and Technology Program of Guangzhou of China (No. 202201010421); the Fundamental Research Funds for the Central Universities (Nos. 21621417 and 21622402); and the Guangdong Provincial Key Laboratory of Cyber and Information Security Vulnerability Research (No. 2020B1212060081).

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