Mean Field Game and Broadcast Encryption-based Joint Data Freshness Optimization and Privacy Preservation for Mobile Crowdsensing

Recently, due to the benefits of system extensibility, deployment cost, and implementation flexibility, mobile crowdsensing (MCS) is a popular method for sensing data collection. This paper aims at developing a timely and secure model for MCS by realizing the joint Age of Information (AoI) performance optimization and privacy preservation. First, based on the queuing theory, the AoI expression is derived in the closed form. Next, through the mean field game (MFG)-based sensing rate control policy, the AoI of the sensing data is optimized. Meanwhile, by adopting the broadcast and EIGamal encryption schemes, sensing task, data content, location, and identities of mobile devices are protected. Finally, simulation results under different parameter settings show that the proposed scheme is effective in improving the timeliness and security performance of the MCS system.