Distributed extraction and a novel association rule mining mechanism for WSN: An empirical analysis

With the advances of wireless sensor network and their ability to generate a large amount of data, data mining techniques, particularly association rule mining technique, for extracting useful knowledge regarding the underlying network have received a great deal of attention. Mining data from Wireless Sensor Network (WSN) poses many new challenges due to its limited resources such as computational capabilities, memory and most importantly the battery power of the sensor nodes. This paper presents a comparative study between distributed extraction algorithm (DEM) and a novel association rule mining mechanism (NARM) for wireless sensor networks.