On efficient meta-data collection for crowdsensing

Participatory sensing applications have an on-going requirement to turn raw data into useful knowledge, and to achieve this, many rely on prompt human generated meta-data to support and/or validate the primary data payload. These human contributions are inherently error prone and subject to bias and inaccuracies, so multiple overlapping labels are needed to cross-validate one another. While probabilistic inference can be used to reduce the required label overlap, there is still a need to minimise the overhead and improve the accuracy of timely label collection. We present three general algorithms for efficient human meta-data collection, which support different constraints on how the central authority collects contributions, and three methods to intelligently pair annotators with tasks based on formal information theoretic principles. We test our methods´ performance on challenging synthetic data-sets, based on real data, and show that our algorithms can significantly lower the cost and improve the accuracy of human meta-data labelling, with little or no impact on time.