An Efficient Distributed Data Extraction Method for Mining Sensor Networks Data

TL;DR

This paper introduces a distributed data extraction method for sensor networks that enhances data accuracy, reduces data size, and conserves energy by applying rule-based clustering, association rule mining, and missing data estimation.

Contribution

It proposes a novel distributed data extraction approach that improves data accuracy and energy efficiency in sensor networks through data reduction and missing value estimation.

Findings

High data accuracy achieved

Reduced energy consumption in networks

Effective data reduction during transmission

Abstract

A wide range of Sensor Networks (SNs) are deployed in real world applications which generate large amount of raw sensory data. Data mining technique to extract useful knowledge from these applications is an emerging research area due to its crucial importance but still its a challenge to discover knowledge efficiently from the sensor network data. In this paper we proposed a Distributed Data Extraction (DDE) method to extract data from sensor networks by applying rules based clustering and association rule mining techniques. A significant amount of sensor readings sent from the sensors to the data processing point(s) may be lost or corrupted. DDE is also estimating these missing values from available sensor reading instead of requesting the sensor node to resend lost reading. DDE also apply data reduction which is able to reduce the data size while transmitting to sink. Results show our proposed approach exhibits the maximum data accuracy and efficient data extraction in term of the entire networks energy consumption.