An approach of flow compensation incentive based on Q-learning strategy for IoT user privacy protection
L Chen, D Zhang, J Zhang, T Zhang, J Du… - AEU-International Journal …, 2022 - Elsevier
In MCS (mobile crowd sensing), reducing network overhead, protecting IoT user privacy and
increasing the participation enthusiasm of perception task are key issues. The QLPPIA (an
incentive approach of flow offset based on Q-Learning algorithm for perception user privacy
protection) was proposed. A system model that combined MCS with MEC (mobile edge
computing) was designed. The edge center uploaded the perception results to the MCS
cloud declining its cloud overhead. A privacy protection structure of attribute relevance …
increasing the participation enthusiasm of perception task are key issues. The QLPPIA (an
incentive approach of flow offset based on Q-Learning algorithm for perception user privacy
protection) was proposed. A system model that combined MCS with MEC (mobile edge
computing) was designed. The edge center uploaded the perception results to the MCS
cloud declining its cloud overhead. A privacy protection structure of attribute relevance …
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