The main challenges in large-scale people tracking are the recognition of people density in a specific area and tracking the people flow path. To address these challenges, we present SenseFlow, a lightweight people tracking system. SenseFlow utilises off-the-shelf devices which sniff probe requests periodically polled by user's smartphones in a passive manner. We demonstrate the feasibility of SenseFlow by building a proof-of-concept prototype and undertaking extensive evaluations in real-world settings. We deploy the system in one laboratory to study office hours of researchers, a crowded public area in city to evaluate the scalability and performance "in the wild", and four classrooms in the university to monitor the number of students. We also evaluate SenseFlow with varying walking speeds and different models of smartphones to investigate the people flow tracking performance.
The ever increasing ubiquitousness of WiFi access points, coupled with the diffusion of smartphones, suggest that Internet every time and everywhere will soon (if not already has) become a reality. Even in presence of 3G connectivity, our devices are built to switch automatically to WiFi networks so to improve user experience. Most of the times, this is achieved by recurrently broadcasting automatic connectivity requests (known as Probe Requests) to known access points (APs), like, e.g., "Home WiFi", "Campus WiFi", and so on. In a large gathering of people, the number of these probes can be very high. This scenario rises a natural question: "Can significant information on the social structure of a large crowd and on its socioeconomic status be inferred by looking at smartphone probes?". In this work we give a positive answer to this question. We organized a 3-months long campaign, through which we collected around 11M probes sent by more than 160K different devices. During the campaign we targeted national and international events that attracted large crowds as well as other gatherings of people. Then, we present a simple and automatic methodology to build the underlying social graph of the smartphone users, starting from their probes. We do so for each of our target events, and find that they all feature social-network properties. In addition, we show that, by looking at the probes in an event, we can learn important sociological aspects of its participants---language, vendor adoption, and so on.