Robust Incentive Tree Design for Mobile Crowdsensing
Xiang Zhang, Guoliang Xue, Ruozhou Yu, Dejun Yang and Jian Tang
Arizona State University, Arizona State University, Arizona State University, Colorado School of Mines, Syracuse University

With the proliferation of smart mobile devices such as smart phones, tablets, and wearable, mobile crowdsensing becomes a powerful sensing and computation paradigm which has been applied in many fields, such as spectrum sensing, environmental monitoring, healthcare, and so on. Driven by promising incentives, the power of the crowd grants crowdsensing an advantage in mobilizing users who perform sensing tasks with the embedded sensors on the smart devices. Auction is one of the commonly adopted crowdsensing incentive mechanisms to incentivize users for participation. However, auction does not consider the incentive for user solicitation where in crowdsensing, a large number of users is often needed. To deal with this issue, we aim to design an auction-based incentive tree to offer rewards to users for both participation and solicitation. Meanwhile, we want the incentive mechanism to be robust against dishonest behavior such as untruthful bidding and sybil attacks, to eliminate the malicious price manipulation. We design an incentive mechanism RIT, which combines the advantages of auctions and incentive trees. We prove that RIT is truthful and sybil-proof with probability at least H, for any given H _ (0, 1). We also prove that RIT satisfies individual rationality, computational efficiency, and solicitation incentive. Simulation results of RIT further confirm our analysis.