PROJECT TITLE :

Share-Frequent Sensor Patterns Mining from Wireless Sensor Network Data

ABSTRACT:

Mining interesting knowledge from the huge quantity of data gathered from WSNs may be a challenge. Works reported in literature use support metric-based mostly sensor association rules which use the prevalence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern isn't a sufficient indicator for finding meaningful patterns as a result of it solely reflects the number of epochs which contain that pattern in the dataset. The share live of sensorsets might discover helpful data about trigger values associated with a sensor. Here, we have a tendency to propose a brand new kind of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs will notice a correlation among a collection of sensors and hence can improve the performance of WSNs in a very resource management method. During this paper, a share-frequent sensor pattern tree (ShrFSP-tree) has been proposed to facilitate a pattern growth mining technique to find SFSPs from WSN knowledge. We additionally gift a parallel and distributed methodology where the ShrFSP-tree is enhanced into PShrFSP-tree and its performance is investigated for each homogeneous and heterogeneous systems. Results show that our method is time and memory economical to find SFSPs than the present most efficient algorithms.


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