Estimating Spatial Averages of Environmental Parameters Based on Mobile Crowdsensing

Abstract

Mobile crowdsensing can facilitate environmental surveys by leveraging sensor-equipped mobile devices that carry out measurements covering a wide area in a short time without bearing the costs of traditional field work. In this article, we examine statistical methods to perform an accurate estimate of the mean value of an environmental parameter in a region based on such measurements. The main focus is on estimates produced by considering the mobile device readings at a random instant in time. We compare stratified sampling with different stratification weights to sampling without stratification as well as an appropriately modified version of systematic sampling. Our main result is that stratification with weights proportional to stratum areas can produce significantly smaller bias for a moderate number of strata and gets arbitrarily close to the true area average as the number of mobiles increases. The performance of the methods is evaluated for an application scenario where we estimate the mean area temperature in a linear region that exhibits the so-called Urban Heat Island effect, with mobile users moving in the region according to the Random Waypoint Model.