Bing Huang

• PhD Student at The University of Sydney

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home...

We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited t...

We present a Cognitive Amplifier framework to augment things part of an IoT, with cognitive capabilities for the purpose of improving life convenience. Specifically, the Cognitive Amplifier consists of knowledge discovery and prediction components. The knowledge discovery component focuses on finding natural activity patterns considering their regu...

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and things in smart homes. We focus on single resident smart homes setting. We propose an ontology to model different types of contexts for IoT services. The ontology is used as a vehicle to accurately model and...

We propose a novel service mining framework to personalize services in an IoT based smart home. We describe a new technique based on the concept of convenience to discover periodic composite IoT services to suit the smart home occupant’s convenience needs. The key features of convenience is the ability to model the spatio-temporal aspects as occupa...

We propose a framework to discover proximate IoT service relationships based on spatio-temporal features. We introduce a spatio-temporal proximity model in terms of spatial-proximity and temporal-proximity to discard insignificant IoT service relationships. The proximity model focuses on quantifying the correlation strength among IoT services from...

A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up. The service relationships are modeled based on spatial-temporal aspects, environment, people, and operation. An ontology-based service model is proposed to describe services. We present a set of metrics to evaluate the...