Abstract

The Internet of Things (IoT) is a building block of the Internet of the future and 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 information sources to achieve a recognition goal. However, existing approaches usually use a simplified metric to optimize the quality of context recognition, which is determined during design time and thus fixed at run time. In this paper, we analyse challenges for a dynamic integration of quality of context recognition into opportunistic sensing approaches and state of the art research that could be used to fill the gaps.

@inproceedings{Eryilmaz2016b,
author = {Elif Eryilmaz AND Frank Trollmann AND Sebastian Ahrndt AND Sahin Albayrak},
title = {Challenges for Adaptable Quality of Context Recognition in Opportunistic Sensing  },
booktitle = {VDE Kongress 2016 - Internet der Dinge},
year = {2016},
pages = {1--6},
note={To appear}
}