Fairness and Social Welfare in Incentivizing Participatory Sensing


Participatory sensing has emerged recently as a promising approach to large-scale data collection. However, without incentives for users to regularly contribute good quality data, this method is unlikely to be viable in the long run. In this paper, we link incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches. With this demand-based principle, we design two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively. Our study shows that the IDF scheme is max-min fair and can score close to 1 on the Jain's fairness index, while the ITF scheme maximizes social welfare and achieves a unique Nash equilibrium which is also Pareto and globally optimal. We adopted a game theoretic approach to derive the optimal service demands. Furthermore, to address practical considerations, we use a stochastic programming technique to handle uncertainty that is often encountered in real life situations.

Meeting Name

Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops (2012: Jun. 18-21, Seoul, South Korea)


Computer Science

Keywords and Phrases

Data collection; Fairness index; Game-theoretic; Incentive schemes; Max-min; Nash equilibria; Quality data; Service demand; Social welfare, Game theory; Iterative methods; Optimization; Sensors; Stochastic programming, MESH networking

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2012 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2012