Demonstration abstract: CrowdMeter — Predicting performance of crowd-sensing applications using emulations

摘要

Predicting performance of crowd-sensing applications at large scale, in the pre-deployment phase, represents a significant challenge for developers. We demonstrate a solution to this problem in the form of a cloud-based emulation platform called CrowdMeter. Our platform emulates mobile devices and access network links, models human factors in crowd-sensing, and leverages virtualization through cloud infrastructure-as-service resources to model large scale crowd-sensing. In this demo we exhibit the capabilities of CrowdMeter by deploying VideoQuest, a simple crowd-sensing application, on hundreds of emulated mobile devices, and by measuring its performance.