  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 capabilities at
a large scale and contribute sleep data to public health understanding, we
present Wisleep, a system for inferring sleep duration using smartphone network
connections that are passively sensed from WiFi infrastructure. We propose an
unsupervised ensemble model of Bayesian change point detection, validating it
over a user study among 20 students living in campus dormitories and a private
home. Our results find Wisleep outperforming prior techniques for users with
irregular sleep patterns while yielding an average 88.50% accuracy within 60
minutes sleep time error and 39 minutes wake-up time error. This is comparable
to client-side methods, albeit utilizing coarse-grained information.
Additionally, we utilize our approach to predict sleep and wake-up times from a
user study of more than 1000 student users, demonstrating results similar to
prior findings on students' sleep patterns. Finally, we show that Wisleep can
process data from twenty thousand users on a single commodity server, allowing
it to scale to large campus populations with low server requirements.
