Abstract

Opportunistic sensing is an adaptive technique for context recognition that aims to make use of available sensors instead of requiring the deployment of specific sensors for a specific recognition goal. Methods implementing the opportunistic sensing approach need to adapt to the availability of data sources and the context recognition goals of the system. In the scope autonomous computing the explicit use of feedback loops to control self-adaptation has been proposed. Several authors have argued that this concept is central to self-adaptive systems in general. In this paper we compare the Opportunity Framework and the MAPE-K feedback loop on a conceptual level. Our findings are that this feedback loop could be applied to Opportunity but that multiple feedback loops seem to be a better option. A comparison with the DYNAMICO model, a conceptual model that proposes three interacting feedback loops, yields further insights and leads us to propose a dedicated adaptation component for the adaptation of recognition goals in opportunistic sensing.

@inproceedings{eryilmaz2015conceptual,
  title={Conceptual application of the MAPE-K feedback loop to opportunistic sensing},
  author={Eryilmaz, Elif and Trollmann, Frank and Albayrak, Sahin},
  booktitle={Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2015},
  pages={1--6},
  year={2015},
  organization={IEEE}
}