LAUSR

Mobile crowdsensing (MCS) has appeared as a viable solution for data gathering in Internet of Vehicle (IoV). As it utilizes plenty of mobile users to perform sensing tasks, the cost of sensor deployment can be reduced and the data quality can be improved. However, there exist two main challenges for the IoV-based MCS, which are the privacy issues and the malicious vehicles issues. Therefore, how to protect privacy, resist malicious vehicles, and recruit trustworthy contributors are crucial to be investigated. In order to solve the challenges simultaneously, we propose a privacy-protecting and reputation-based participant recruitment scheme, including a privacy-protecting mechanism, reputation value calculation method, and reputation-based recruitment algorithm. In particular, the privacy-protecting mechanism can be executed quickly since 1) its signaling overhead is low and 2) the computation-intensive procedures are processed by the mobile edge server. The reputation value calculation method considers the vehicle’s past behaviors and Quality of Information (QoI) to guarantee the accuracy. In addition, the introduction of time fading makes the current behavior have a larger weight. The reputation value will drop rapidly once a malicious behavior is detected, so that the method can detect malicious vehicles more quickly. Moreover, we propose a reputation-based recruitment algorithm to recruit appropriate vehicles to perform sensing tasks based on the above method. Simulation results demonstrate that our method can assess the reputation value accurately and detect malicious vehicles quickly while protecting the privacy. It is shown that our recruitment algorithm can realize load balancing, improve sensing quality, and reduce the cost.