AoI-Aware and Privacy Protection Incentive Mechanism for Crowdsensing Networks

In Crowdsensing Networks, the freshness of sensing data is critical for accurate analysis and reliable decisions, which is measured by Age of Information (AoI). However, the Sensing Users (SUs) are reluctant to execute frequent sensing without any incentive, since they incur not only the inevitable energy consumption but also the potential privacy leakage. Adopting Differential Privacy (DP) can effectively protect the privacy of SUs, through it reduces the AoI performance. To address this issue, we propose a freshness-aware privacy-preserving incentive mechanism to balance the trade-off between data value and privacy. SUs are classified with different update cycles, while the Sensing Platform (SP) is unknown about the information. Therefore, we design a contract to solve the information asymmetry problem, which is proved to be optimal and truth-telling. Finally, numerical results demonstrate that the proposed contract is feasible and achieves a utility for the SP when compared with other mechanisms.