# Providing Long-Term Participation Incentive in Participatory Sensing

arXiv: https://arxiv.org/abs/1501.02480 · HTML: https://mlsanity.com/paper/1501.02480/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2015.

## Built on by (most cited first, 2 of 2)

- [Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach](https://mlsanity.com/paper/2107.11728/) (2021)
- [Socially-Optimal Mechanism Design for Incentivized Online Learning](https://mlsanity.com/paper/2112.14338/) (2021)

## Abstract (arXiv)

Providing an adequate long-term participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under (current) information asymmetry. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% to 90%) and social performance (e.g., increasing the social welfare by 15% to 80%).