  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.
