Participant Incentive Mechanism Toward Quality-Oriented Sensing

Abstract

The ubiquity of ever-more-capable mobile devices, especially smartphones, brings forth participatory sensing to collect and interpret information. It can achieve unprecedented quantity of data. However, it is arduous to guarantee quality of data because everyone can contribute data without scrutinization. It is an important issue in quality-oriented participatory sensing. Our idea to address this issue is motivating participants to contribute accurate data for improving data quality directly. In this article, we propose a reputation-based incentive mechanism, RIM, to realize the idea. More specifically, we identify the participants who collect the accurate data and regard them as the reputable ones. Then, the reputable participants are granted a higher chance to obtain rewards so that other people will try to follow such users and become reputable as well. Namely, RIM can encourage and steer users to collect accurate data in the long term. We analyze our incentive mechanism by formalization and premise implications. For a feasibility study of participatory sensing and verification of the implications, we implement and deploy a participatory sensing application focusing on monitoring environmental noise in a specific location as a case study and conduct a simulation based on the case study to further evaluate the proposed incentive mechanism. The results from the case study and the simulation present that RIM can remarkably increase the quality of collected data in participatory sensing while corroborating our theoretical implications.