A cooperative incentive mechanism for recurrent crowd sensing

Abstract

Crowd sensing (CS) is an approach that consists of collecting many samples of a phenomena of interest by distributing the sampling process across a large number of individuals. In this work, we address the effect of cooperation among individuals by modeling a recurrent CS task as a repeated game. In this game, participants are the players of the corresponding game, and every round of the CS task is considered as a single-shot game which is repeated over time. In this model, participants compete and cooperate with each other in order to sell their samples. We represent the participants evolutionary behaviors by a graph network in which all the individuals make utilities in the long run. We show that although a pure competition approach faces problems such as the continuous drop-out of participants and the raise of prices of samples, this hybrid approach keeps the prices of samples low while maintaining the required number of participants.