Research On Safe And Efficient Fog-Based Vehicular Crowd Sensing

In recent years,the integration of sensors and embedded computing devices has promoted the rapid development of vehicular crowd sensing.However,fine-grained data transmission increases the burden of "cloud," and centralized data management mode cannot meet the demand of vehicles for high mobility and low latency.As a new distributed computing method closer to the edge of the network,fog computing migrates computing from the cloud to the edge of the network.It not only provides closer computing resources to the vehicle,deployed flexibility,but also can take advantage of local storage and computing resources,reduce the communication and storage burden of the data center.However,there are still many problems in the fog-based vehicular crowdsensing.Firstly,in this kind of structure,the privacy of users must be guaranteed,on this premise,how to improve the accuracy of data and encourage more vehicles to participate in crowd sensing are the critical technical difficulties that should be solved in the fog-based vehicular crowd sensing.Based on this,this paper mainly studies the safe and efficient fog-based vehicular crowd sensing,which mainly includes the following two aspects:Firstly,this paper proposes a privacy-preserving trust management scheme for the fog-based vehicular crowd sensing.This scheme uses somewhat-homomorphic encryption(SHE)algorithm to make the sensing data always exist in the form of ciphertext in the process of transmission and aggregation.The property of SHE makes it possible for the fog and cloud node to obtain the encrypted aggregating result based on the encrypted sensing data and decrypting this result.The vehicles who participate in the crowd sensing tasks submit the sensing data to the fog node without exposing their real identities through the randomizable signature.On the other hand,this scheme introduces a trust management system to screen the data from malicious participants in order to improve sensing results accuracy.Detailed security analysis and extensive simulations demonstrate that the proposed scheme is secure with the security properties,including data analysis attack,collusion attack,malicious sensing attack,on-off attack,and privacy attacks,but also is sufficient to deal with malicious participants and improve the sensing results accuracy.Secondly,taking traffic accident evidence as the application scenario,this paper proposes a privacy-preserving incentive mechanism in the process of fog-based vehicular crowd sensing.Under the premise of ensuring vehicle privacy,this mechanism provides incentives to vehicles according to the quality of information provided by vehicles.Further,it stimulates the enthusiasm of vehicles to participate in crowd sensing.Among them,the vehicle uses a pseudonym instead of a real identity for data transmission,and the fog node and cloud server can verify the validity of the pseudonym used by the vehicle without exposing its real identity.At the same time,after using the covert communication-based pseudonym exchange mechanism to make it safely,it effectively resists the tracking attacks so as to improve its privacy further.The performance analysis shows that the scheme can better realize the full conditional privacy of vehicles right from the reporting all the way till the redemption of the incentives,and effectively resist the attack types such as identity forgery,location privacy theft,and repeated redeem the reward.