Knowledge discovery in Wireless Sensor Networks for Chronological Patterns

Wireless Sensor Networks (WSNs) have proven their success in a variety of applications for monitoring physical and critical environments. However, the streaming nature, limited resources, and the unreliability of wireless communication are among the factors that affect the Quality of Service (QoS) of WSNs. In this paper, we propose a data mining technique to extract behavioral patterns about the sensor nodes during their operation. The behavioral patterns, which we refer to as Chronological Patterns, can be thought of as tutorials that teach about the set of sensors that report on events within a defined time interval and the order in which the events were detected. Chronological Patterns can serve as a helpful tool for predicting behaviors in order to enhance the performance of the WSN and thus improve the overall QoS. The proposed technique consists of: a formal definition of the Chronological Patterns and a new representation structure, which we refer to as Chlorotical Tree (CT), that facilities the mining of these patterns. To report about the performance of the CT, several experiments have been conducted to evaluate the CT using different density factors.