Providing Long-Term Participation Incentive in Participatory Sensing
Lin Gao, Fen Hou, and Jianwei Huang

TL;DR
This paper introduces a Lyapunov-based VCG auction policy for sensor selection in participatory sensing systems, effectively incentivizing long-term user participation and outperforming existing methods in participation and social welfare.
Contribution
It proposes a novel online sensor selection policy that guarantees near-optimal long-term participation incentives without requiring future information.
Findings
Reduces user dropping probability by 25% to 90%.
Increases social welfare by 15% to 80%.
Outperforms state-of-the-art policies in simulations.
Abstract
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…
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