LAUSR

The data explosion caused by the rapid and widespread use of IoT devices is placing tremendous pressure on current communication, computing and storage resources. In an ambient ubiquitous computing environment, taking advantage of the context of the application scenario can significantly improve the system performance of IoT networks. Therefore, in this paper, we propose CONTESS, a context-aware edge computing framework with selective sensing that leverages the context information of the sensed environment to improve its applicability to smart IoT systems. CONTESS is composed of two parts: context management, where context acquisition, modeling and reasoning happens; and selective sensing, where data selection happens. We demonstrate the capabilities of CONTESS in the scenario of a parking management system for a smart city environment. We implement CONTESS using linked data and semantic web technologies. We start by designing an OWL-based ontology and then simulating the proposed scenario using the OMNET++ network simulator along with the Veins framework and SUMO traffic simulator. The results show an improvement compared to traditional sensing methods in both communication overhead and the application results.