The purpose of this paper is to focus on sensor data fluctuations. Context‐aware applications in the real world adapt their behavior to contexts abstracted from real‐world situations sensed as physical quantities by heterogeneous and distributed sensors. Most of the adaptations are programmed as rules derived from human experience in making environments comfortable and efficient. Preparation of sufficient rules, however, is difficult because oversights and exceptional contexts are inevitable.
This paper, focuses on sensor data fluctuations and calculates the probabilities indicating the frequency of such fluctuations.
The results help to confirm the preset rules of the context‐aware applications and find previously unnoticed situations that the context‐aware applications should cope with.
The paper shows how this method is applied to log data captured in an office in order to evaluate the method's capabilities and consider the feasibility of abstracting the newly observed situations into rules.
