  Sleep deprivation is a public health concern that significantly impacts one's
well-being and performance. Sleep is an intimate experience, and
state-of-the-art sleep monitoring solutions are highly-personalized to
individual users. With a motivation to expand sleep monitoring at a large-scale
and contribute sleep data to public health understanding, we present WiSleep, a
sleep monitoring and analytics platform using smartphone network connections
that are passively sensed from WiFi infrastructure. We propose an unsupervised
ensemble model of Bayesian change point detection to predict sleep and wake-up
times. Then, we validate our approach using ground truth from a user study in
campus dormitories and a private home. Our results find WiSleep outperforming
established methods for users with irregular sleep patterns while yielding
comparable accuracy for regular sleepers with an average 79.5\% accuracy. This
is comparable to client-side based methods, albeit utilizing only
coarse-grained information. Finally, we show that WiSleep can process data from
20,000 users on a single commodity server, allowing it to scale to large campus
populations with low server requirements.
