  In Happy IoT, the revenue of service providers synchronizes to the
unobservable and dynamic usage-contexts (e.g. emotion, environmental
information, etc.) of Smart-device users. Hence, the usage-context-estimation
from the unreliable Smart-device sensed data is justified as an unsupervised
and non-linear optimization problem. Accordingly, Autonomic Happy IoT
Management is aimed at attracting initial user-groups based on the common
interests (i.e. recruitment ), then uncovering their latent usage-contexts from
unreliable sensed data (i.e. revenue-renewal ) and synchronizing to
usage-context dynamics (i.e. stochastic monetization). In this context, we have
proposed an unsupervised online Bayesian mechanism, namely Whiz (Greek word,
meaning Smart), in which, (a) once latent user-groups are initialized (i.e
measurement model ), (b) usage-context is iteratively estimated from the
unreliable sensed data (i.e. learning model ), (c) followed by online filtering
of Bayesian knowledge about usage-context (i.e. filtering model ). Finally, we
have proposed an Expectation Maximization (EM)-based iterative algorithm Whiz,
which facilitates Happy IoT by solving (a) recruitment, (b) revenue-renewal and
(c) stochastic- monetization problems with (a) measurement, (b) learning, and
(c) filtering models, respectively.
