Service-Constraint Based Truthful Incentive Mechanisms for Crowd Sensing

TL;DR

This paper introduces novel incentive mechanisms for crowd sensing platforms with service constraints, ensuring truthful participation and efficiency, applicable in both offline and online settings, validated through extensive simulations.

Contribution

It proposes the first service-constraint incentive mechanisms for crowd sensing with monotone submodular value functions, addressing both offline and online scenarios.

Findings

Mechanisms are individually rational and task feasible.

They are computationally efficient and truthful.

Perform well in extensive simulations.

Abstract

Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive mechanisms are necessary for attracting more user participation. Most of existing mechanisms apply only for the budget-constraint scenario where the platform (the crowd sensing organizer) has a budget limit. On the contrary, we focus on a different scenario where the platform has a service limit. Based on the offline and online auction model, we consider a general problem: users submit their private profiles to the platform, and the platform aims at selecting a subset of users before a specified deadline for minimizing the total payment while a specific service can be completed. Specially, we design offline and online service-constraint incentive mechanisms for the case where the value function of selected users is monotone submodular. The mechanisms are individual rationality, task feasibility, computational efficiency, truthfulness, consumer sovereignty, constant frugality, and also performs well in practice. Finally, we use extensive simulations to demonstrate the theoretical properties of our mechanisms.