How multiple crowdsourcers compete for smartphone contributions?

Abstract

Mobile crowdsourcing provides us various exciting and profitable applications. All the mobile crowdsourcing systems purchase sensing services from a population of smartphone users. The issue of competition arises. However, few studies address this issue, which significantly affects the functionality of crowdsourcing systems. In this paper, we study the problem of price competition among multiple crowdsourcers in a free market, where smartphone users are able to withdraw from the current crowdsourcer if they can gain more utility from other crowdsourcers. We formulate the price competition as a dynamic non-cooperative game, where each crowdsourcer independently decides its own price aiming at highest profit and all smartphone users join the crowdsourcers at their satisfaction. In practice, a crowdsourcer may not know strategies of others which are unrevealed information. We propose a distributed learning algorithm that every crowdsourcer learns from its historic information to achieve the Nash equilibrium in the market. We studied and identified the suitable learning speed to make the price adaption converge to the Nash equilibrium. All the results are supported by both theoretical analysis and simulations.

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

“…We set the value of e as 0.5 which is the median of (0, 1), so that it guarantees general applicability of experimental results. The value of unit cost c is 0.1 referring the settings in [34] and [35]. The setting of these parameters is to make contributors have the reasonable incomes.…”