The concept of an adaptive acquisition of environment data in distributed scenarios promises a number of benefits. If an application aggregates and uses all available sensing information in an intelligent environment it may provide a higher precision and an increased fault-tolerance. Unfortunately, the application developer has to cope with a number of additional challenges compared to static sensor evaluation. It is not possible to generate an optimized sensor application schedule for a dynamic system at design-time. Due to the adaptive selection process, this has to be executed at runtime. In this paper we propose a general approach for this problem based on a two-level analysis. The first level compares sensor parameter sets (periods, offsets, delays) and application requirements (number of measurements, quality) based on a worst/best case analysis. If a more precise evaluation is necessary, the second level needs to be started. This one considers additional, situation-specific properties like phase shift of sensor periods, communication delays and jitter. At the end, it provides an online optimization of common goals e.g., minimization of the age of data and a constant number of input counts.
['Sebastian Zug', 'Andre Dietrich', 'Christoph Steup', 'Jörg Kaiser']
Adaptive environment perception in cyber-physical systems