Date Added: Jul 2010
Many sensor network applications observe trends over an area by regularly sampling slow-moving values such as humidity or air pressure (for example in habitat monitoring). Another well-published type of application aims at spotting sporadic events, such as sudden rises in temperature or the presence of methane, which are tackled by detection on the individual nodes. This paper focuses on a zone between these two types of applications, where phenomena that cannot be detected on the nodes need to be observed by relatively long sequences of sensor samples. An algorithm that stems from data mining is proposed that abstracts the raw sensor data on the node into smaller packet sizes, thereby minimizing the network traffic and keeping the essence of the information embedded in the data.