Robust quality metric for scarce mobile crowd-sensing scenarios

Abstract

This paper proposes a novel quality of source metric for Mobile Crowd-Sensing systems (MCS), for systems with scarce participant availability due to small sample sizes in each sensing cycle. We introduce a controlled quality metric that is based on the difference between centrality estimates, the trimmed mean, and the Median Absolute Deviation (MAD) filtered mean. Our metric permits outlier detection, and therefore allows the estimation of quality under the stringent conditions of small sample sizes. The proposed algorithm also introduces a parameter 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 error terms, and therefore affects the perception of quality. We mathematically develop the proposed metric, while showing the impact of all MCS design parameters in it, in a closed-form expression, and we compare it to computer simulations. ? 2018 IEEE.