Selecting Most Informative Contributors with Unknown Costs for Budgeted Crowdsensing
Mobile crowdsensing has become a novel and promising paradigm in collecting environmental data. A critical problem in improving the QoS of crowdsensing is to decide which users to select to perform sensing tasks, in order to obtain the most informative data, while maintaining the total sensing costs below a given budget. The key challenges lie in (i) finding an effective measure of the informativeness of users' data, (ii) learning users' sensing costs which are unknown a priori, and (iii) designing efficient user selection algorithms that achieve low-regret guarantees. In this paper, we build Gaussian Processes (GPs) to model spatial locations, and provide a mutual information-based criteria to characterize users' informativeness. To tackle the second and third challenges, we model the problem as a budgeted multi-armed bandit (MAB) problem based on stochastic assumptions, and propose an algorithm with theoretically proven low-regret guarantee. Our theoretical analysis and evaluation results both demonstrate that our algorithm can efficiently select most informative users under stringent constraints.
S. Yang et al., "Selecting Most Informative Contributors with Unknown Costs for Budgeted Crowdsensing," Proceedings of the 24th IEEE/ACM 24th International Symposium on Quality of Service (2016, Beijing, China), Institute of Electrical and Electronics Engineers (IEEE), Jun 2016.
The definitive version is available at https://doi.org/10.1109/IWQoS.2016.7590447
24th IEEE/ACM 24th International Symposium on Quality of Service, IWQoS 2016 (2016: Jun. 20-21, Beijing, China)
Keywords and Phrases
Costs; Quality of service; Stochastic models; Stochastic systems, Analysis and evaluation; Critical problems; Effective measures; Environmental data; Gaussian Processes; Multi armed bandit; Mutual informations; Stringent constraints, Budget control
International Standard Book Number (ISBN)
Article - Conference proceedings
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