TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services

Abstract

The shortage of parking in crowded urban areas causes severe societal problems such as traffic congestion, environmental pollution, and many others. Recently, crowdsourced parking, where smartphone users are exploited to collect realtime parking availability information, has attracted significant attention. However, existing crowdsourced parking information systems suffer from low user participation rate and data quality due to the lack of carefully designed incentive schemes.In this paper, we address the incentive problem of trustworthy crowdsourced parking information systems by presenting an incentive platform named TruCentive, where high utility parking data can be obtained from unreliable crowds of mobile users. Our contribution is three-fold. First, we provide hierarchical incentives to stimulate the participation of mobile users for contributing parking information. Second, by introducing utility-related incentives, our platform encourages participants to contribute high utility data and thereby enhances the quality of collected data. Third, our active confirmation scheme validates the parking information utility by game-theoretically formulated incentive protocols. The active confirming not only validates the utility of contributed data but re-sells the high utility data as well. Our evaluation through user study on Amazon Mechanical Turk and simulation study demonstrate the feasibility and stability of TruCentive incentive platform.

Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

“…There are lots of incentive mechanisms which can be classified into nonmonetary incentives [18][19][20][21][22][23][24][25][26][27][28][29] and monetary incentives [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Paying for sensed data in crowd sensing tasks is the most intuitive incentive.…”

“…However, the ordinary individuals are not willing to share their sensing capabilities unless there are sufficient incentives. Research on incentive mechanism has been widely concerned by investigators, and considerable designed schemes about the incentive mechanism design have been put forward which can be classified into nonmonetary incentives [18][19][20] and monetary incentives [21][22][23][24][25][26][27][28][29].…”

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

“…This is an important step forward from the traditional idea of indirectly improving the quality of data by increasing the quantity [93]. Few other studies that have used gamification in urban-scale crowdsensing initiatives include [98,99].…”

Providing Efficient Privacy-Aware Incentives for Mobile Sensing

“…Inner nodes of the tree are computed in the same way as the standard Merkle tree. The proof for m 2 , m 3 , and m 4 include h 4 , h 6 , h 8 and h 12 . When the node deposits the credit token m 1 , SIG d (m 1 ) , the collector cannot link h 12 to m 1 since it does not know r 1 .…”