1 research outputs found
Providing Probabilistic Robustness Guarantee for Crowdsensing
Due to its flexible and pervasive sensing ability, crowdsensing has been
extensively studied recently in research communities. However, the fundamental
issue of how to meet the requirement of sensing robustness in crowdsensing
remains largely unsolved. Specifically, from the task owner's perspective, how
to minimize the total payment in crowdsensing while guaranteeing the sensing
data quality is a critical issue to be resolved. We elegantly model the
robustness requirement over sensing data quality as chance constraints, and
investigate both hard and soft chance constraints for different crowdsensing
applications. For the former, we reformulate the problem through Boole's
Inequality, and explore the optimal value gap between the original problem and
the reformulated problem. For the latter, we study a serial of a general
payment minimization problem, and propose a binary search algorithm that
achieves both feasibility and low payment. The performance gap between our
solution and the optimal solution is also theoretically analyzed. Extensive
simulations validate our theoretical analysis.Comment: 33 pages, 4 figures, 1 tabl