Bootstrap-Based Quality Metric for Scarce Sensing Systems

This paper considers Mobile Crowd-Sensing (MCS) systems that suffer from scarce participant availability due to small sample sizes in each sensing cycle. With such small sample sizes, a sample in error would dramatically affect the MCS system performance. Therefore, we propose a novel quality of source metric targeted for small sample sizes through the non-parametric bootstrap, the trimmed mean, and the Median Absolute Deviation Trimming-based mean (MAD-mean). This statistic permits outlier detection, and therefore allows the estimation of quality under the stringent conditions of small sample sizes present in MCS independent sensing cycles. We introduce an algorithm that allows MCS administrators to control the accuracy of the metric, and therefore control the range of accepted values. Such control is achieved by means of introducing the MAD-mean, which deliberately widens the statistic's distribution, and therefore the perception of quality. In combination with the bootstrap, our metric allows quality estimation for samples as small as 8. We develop our robust quality of source metric algorithm, showing the impact of all the involved parameters; and we compare it to computer simulations to demonstrate its viability.