Quality of Context Optimization in Opportunistic Sensing for the Automatization of Sensor Selection over the Internet of Things

AutorElif Eryilmaz, and Sahin Albayrak
QuelleECAI 2016, Workshop Modelling and Reasoning in Context (MRC), August 2016 

The Internet of Things (IoT) will cover billions of intelligent objects being able to sense, act and communicate with each other. Opportunistic sensing makes use of the IoT by dynamically selecting sensors to derive a piece of information. However, sensors in the IoT differ from each other regarding the quality of data they can provide and existing approaches usually use a simplified metric to optimize the quality of context recognition. In this work, we aim to provide an overview of ongoing research to enable quality of context optimization by an autonomous sensor selection amongst available sensors over the IoT. The evaluation criterion is finding the best fitting sensor combination by means of quality and updating autonomously in case of any change.