Gathering meaningful information from Home Networking (HN) environments has presented researchers with measurement strategy challenges. A measurement platform is typically designed around the process of gathering data from a range of devices or usage statistics in a network that are specifically behind the HN firewall.
HN studies require a fine balance between incentives and impediments to promote usage and minimize efforts for user participation with the focus on gathering robust datasets and results. In this dissertation we explore how to gather data from the HN Ecosystem (e.g. devices, apps, permissions, configurations) and feedback from HN users across a multitude of HN infrastructures, leveraging low impediment and low/high incentive methods to entice user participation.
We look to understand the trade-offs of using a variety of approach types (e.g. Java, Mobile app, survey) for data collections, user preferences, and how HN users react and make changes to the HN environment when presented with privacy/security concerns, norms of comparisons (e.g. comparisons to the local environment and to other HNs) and other HN results. We show that the HN Ecosystem is more than just “the network”, it also includes a combination of both devices and apps within the HN Ecosystem.