Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones

Abstract

Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

“…However, it cannot apply to most situations for the incentives are often too few for users. Most participatory users move according to the intended purpose, rather than depending on the incentive situation to determine the direction of the next movement [5,7].…”

“…In this paper, we consider a participatory sensing system with a similar scenario as [5]. The system consists of Max user mobile participants and an application server.…”

“…The parameters for users are initialized in the range randomly obeying the normal distribution. The users' sensitivity of privacy leakage levels is initialized in [1,5], and level 5 represents the highest sensitivity of privacy leakage level. Figure 4(a) shows how users respond to different price threshold of last cycle which the server sends them.…”

“…A user may not be interested in the task, unless he receives a satisfactory reward to compensate for his resource consumption and potential privacy breach. Therefore, incentive mechanisms have been proposed to attract adequate participants [5][6][7].…”

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

“…Depending on who sets the price for a sensing task, monetary incentives can be either platform-centric or user-centric [28]. In the platform-centric model, sometimes referred to as crowdsourcercentric incentive mechanism [29], the initiator of the sensing task sets the price and aims to maximize the platform's profit [30]. In the user-centric model, the reverse is the case as the price is defined by the participants [30].…”