MCS data collection mechanism for participants' reputation awareness

Task participants' malicious behavior can significantly reduce the credibility of mobile crowd sensing (MCS). To solve this problem, this paper proposed a data collection mechanism that analyzed and quantified participants' historical reputation according to their willingness and the quality of data they had shared, and then updated their current reputation through the logistic regression model. Simultaneously, to measure the authenticity of the collected data, the participants were divided into two types:those who were related to direct transmission of sensing data and second, those who were involved in indirect forwarding of these, which was based on the remaining transmission time of sensing data and residual energy of mobile equipment. Then the server analyzed the accuracy of data collected by participants according to the multitasking scenario. Simulation results show that the proposed mechanism can significantly improve the perceived tasks performed in real time, greatly upgrade the quality of sensing data, and effectively reduce the reward expenses.