Mining Associated Ranking Patterns from Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are complex networks consisting of many sensors which can detect and collect sensed data. Valuable information can be extracted from the sensed data of WSNs using data mining techniques, which provide unlimited possibilities of applications, and have been the object of an increasing number of studies in recent years. This study applies ranking sequence mining to WSNs. For example, if a sensor detects the following scenario during a time interval: A occurs three times; B occurs twice; C occurs four times, where A, B and C are events. The information, (C(4) > A(3) > B(2)), can be abstractly expressed as a ranking sequence (C ≻ A ≻ B), where "≻" indicates to the importance of the preceding item is greater than that of the succeeding item. To the best of our knowledge, there has been no research conducted on ranking pattern mining problems with WSNs. The objective of this study is to construct a novel model to discover interesting patterns from the sensed data of WSNs.