WiFi P2P allows mobile apps to connect to each other via WiFi without an intermediate access point. This communication mode is widely used by mobile apps to support interactions with one or more devices simultaneously. However, testing such P2P apps remains a challenge for app developers as i) existing testing frameworks lack support for WiFi P2P, and ii) WiFi P2P testing fails to scale when considering a deployment on more than two devices. In this paper, we therefore propose an acceptance testing framework, named ANDROFLEET, to automate testing of WiFi P2P mobile apps at scale. Beyond the capability of testing point-to-point interactions under various conditions, AN-DROFLEET supports the deployment and the emulation of a fleet of mobile devices as part of an alpha testing phase in order to assess the robustness of a WiFi P2P app once deployed in the field. To validate ANDROFLEET, we demonstrate the detection of failing black-box acceptance tests for WiFi P2P apps and we capture the conditions under which such a mobile app can correctly work in the field. The demo video of ANDROFLEET is made available from https://youtu.be/gJ5 Ed7XL04.

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IoT devices are ubiquitous and widely adopted by end-users to gather personal and environmental data that often need to be put into context in order to gain insights. In particular, location is often a critical context information that is required by third parties in order to analyse such data at scale. However, sharing this information is i) sensitive for the user privacy and ii) hard to capture when considering indoor environments. This paper therefore addresses the challenge of producing a new location hash, named IndoorHash, that captures the indoor location of a user, without disclosing the physical coordinates, thus preserving their privacy. This location hash leverages surrounding infrastructure, such as WiFi access points, to compute a key that uniquely identifies an indoor location. Location hashes are only known from users physically visiting these locations, thus enabling a new generation of privacy-preserving crowdsourcing mobile applications that protect from third parties reidentification attacks. We validate our results with a crowdsourcing campaign of 31 mobile devices during one month of data collection.

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