Improved Edge Computing for IoT Devices via Optimized Semantic Models

In computer science, semantic interoperability is widely researched and used for multiple applications in the Internet of Things. Since semantics involve a lot of data and require high processing capabilities, computers with high performance are used as intermediate devices to represent devices which are constrained in energy, memory, and processing capacity. Knowledge on the explicit setup of a constrained device, e.g. which sensors and actuators are attached for what purpose, is required to register the constrained device at such an intermediate device. Commonly, this knowledge is defined in a file with a semantic description which refers to a specific constrained device, but can not be shipped with the device itself due to the prior described constraints. We propose to optimize semantic descriptions to meet the requirements of constrained devices in terms of energy, memory, and processing capabilities. Our results show that semantic descriptions can be shipped with a constrained device. This enables self-registration and self-configuration when constrained devices are added or removed from networks.