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.
Overcoming the inefficiency of non-cooperative out-comes poses an important challenge for network managers in achieving efficient utilization of network resources. This paper studies a class of incentive schemes based on intervention, which are aimed to drive self-interested users towards a system objective. A manager can implement an intervention scheme by introducing in the network an intervention device that is able to monitor the actions of users and to take an action that influences the network usage of users. We consider the case of perfect monitoring, where the intervention device can immediately observe the actions of users without errors. We also assume that there exist actions of the intervention device that are most and least preferred by all users and the intervention device, regardless of the actions of users. We derive analytical results about the outcomes achievable with intervention and optimal intervention rules, and illustrate the results with an example based on random access networks.