Unsupervised Online Bayesian Autonomic Happy Internet-of-Things Management

TL;DR

This paper introduces Whiz, an unsupervised online Bayesian mechanism for estimating user contexts in Happy IoT, enabling dynamic revenue management from unreliable sensed data through iterative learning and filtering.

Contribution

It proposes a novel Bayesian-based online framework, Whiz, for unsupervised context estimation and revenue optimization in Happy IoT environments, addressing challenges of unreliable data.

Findings

Effective latent context estimation from noisy data.

Improved revenue synchronization through Bayesian filtering.

Successful iterative learning and monetization strategies.

Abstract

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.