Invited Paper: Semantic IoT Data Description and Discovery in the IoT-Edge-Fog-Cloud Infrastructure

Many IoT systems are data intensive, where a large volume of data steadily get generated from a large number of sensors in the system. These data are continuous, thus, how to store and manage them is an important issue. Existing time series databases (TSDBs) offer some good strategies for storing continuous IoT data streams, but they lack a good semantic model for describing the IoT data streams to support effective data discovery. This shortcoming becomes critical when we consider the need for data sharing in many application domains; and it becomes significant when we consider the super huge scale of the IoT-Edge-Fog-Cloud infrastructure and the dynamic data flows in the infrastructure. In this paper, we develop the solutions for IoT data management in the IoT-Edge-Fog-Cloud infrastructure. We focus on the issues of data storage, specification and discovery. First, we build a semantic model for better specification of the IoT data streams (time series data), the DS-ontology. We have applied DS-ontology to TSDBs and developed the SE-TSDB tool suite, which runs on top of existing TSDBs to help establish semantic specifications for data streams and enable semantic-based data retrievals. We have also developed the IoT data discovery techniques based on SE-TSDB to facilitate semantic based data retrieval in the IoT-Edge-Fog-Cloud infrastructure. With our techniques, IoT data streams can be more effectively tracked and flexibly retrieved to help with integrated data analytics and improved knowledge discovery.