Robust Incentive Tree Design for Mobile Crowdsensing

With the proliferation of smart mobile devices (smart phone, tablet, and wearable), mobile crowdsensing becomes a powerful sensing and computation paradigm. It has been put into application 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, it does not consider the incentive for user solicitation, where in crowdsensing, such incentive would ease the tension when there is a lack of crowdsensing users. 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 malicious price manipulations. We design RIT as a Robust Incentive Tree mechanism for mobile crowdsensing 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.