A Socially-Aware Incentive Mechanism for Mobile Crowdsensing Service Market

TL;DR

This paper proposes a socially-aware incentive mechanism for mobile crowdsensing markets, modeling participant behavior and reward strategies considering social network effects to enhance participation and CSP revenue.

Contribution

It introduces an optimal reward mechanism incorporating social network effects and analyzes its impact on participation and revenue, including analytical expressions and approximations.

Findings

Network effects significantly increase participation levels.

Discriminatory rewards yield higher CSP surplus.

Performance evaluation confirms effectiveness of the proposed mechanism.

Abstract

Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs' participation level and the CSP's optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.