Fairness and Social Welfare in Incentivizing Participatory Sensing

TL;DR

This paper proposes demand-based incentive schemes for participatory sensing, enhancing fairness and social welfare, with theoretical guarantees and practical stochastic optimization for real-world uncertainties.

Contribution

Introduces demand-based incentive schemes (IDF and ITF) for participatory sensing, with theoretical analysis and practical stochastic optimization.

Findings

IDF scheme is max-min fair with high Jain's fairness index

ITF scheme maximizes social welfare with Nash equilibrium

Stochastic programming addresses real-world uncertainties

Abstract

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.