An approach of flow compensation incentive based on Q-Learning strategy for IoT user privacy protection

Abstract

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 based on Markov chain Monte Carlo was constructed, which can protect the privacy data. Perception outcomes with higher accuracy of attribute relevance were generated. An incentive pattern of flow offset for user privacy protection based on Q-Learning opportunistic cooperation transfer was designed to cut down MCS cloud flow offset cost and facilitate user enthusiasm. Compared with the existing private protection of high-dimensional attribute data, opportunistic relay perception incentive, and other solutions, the QLPPIA method improves the perception result precision by 27.06%, increases MCS cloud cost of 88.80%, and declines flow compensation expenditure at 19.03% on average.