A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things
This addresses the challenge of rapid IoT application development and dynamic reconfiguration for developers and system administrators, but it appears incremental as it builds on existing knowledge representation and recommendation techniques.
The paper tackles the problem of heterogeneous configuration knowledge representation in IoT by proposing a unified data model and a context-aware recommender system called IoT-CANE, which facilitates incremental knowledge acquisition and declarative context-driven recommendations.
Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.