Abstract
Proactive and reactive context reasoning architecture for smart web services
Highlights
The web of things (WoT) interconnects various sort of physical objects deployed in the real-world to the web to promote global interoperability
Our smart WoT architecture (SWA) for proactive and reactive context reasoning is at the crossroad of multiple domains: WoT, context-aware systems, multi-entity Bayesian networks (MEBNs), and smart web services
We considered the work of Park et al (2016) as a base of this work that introduced a process model for MEBN learning assisted by a human, which is called HML
Summary
The web of things (WoT) interconnects various sort of physical objects deployed in the real-world (e.g., sensors, actuators, devices, and networks) to the web to promote global interoperability. Services that adapt to their context are called ‘SSF’ They are available today on smartphones, smart highways, smart televisions, smart cities, and smart healthcare devices, with the objective to provide customised functionalities without the need for direct user intervention. We introduce an approach that combines proactive and reactive reasoning into a single architecture to enable smart web services for the WoT. Our work focuses especially on the reasoning/decision-making layer for semantically representation and reasoning about complex situations with uncertain information To reach this aim, the system is based on multi-entity Bayesian network (MEBN) (Laskey, 2008) that combines first-order logic (FOL) with Bayesian networks (BNs) for representing and reasoning about uncertainty in complex, knowledge-rich domains.
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