Regional heat and social attribute aware participant selection mechanis in mobile crowd sensing

Aiming at the problem of tasks that are low reliability acquired by platform and difficult to accomplish on time in user sparse area. A participant selection mechanism that combines regional heat and social attribute aware was proposed. Firstly, considering the influence of different regional heat on task completion, the regional heat was evaluated according to the number of active users, the average residence time of users and the completion of historical task. Secondly, in order to analyze the impact of user social attributes on task completion, the user willingness, reputation and activity were calculated by combining the status information of users and the historical task record of users. Finally, by taking the above factors into account, two different mechanisms of participant selection for social attribute perception were designed for high and low heat areas to maximize quality and number oftask completionrespectively. The results show that the proposed mechanism can significantly improve the overall da