Using data mining to handle missing data in multi-hop sensor network applications

Abstract

A sensor's data loss or corruption, aka sensor data missing, is a common phenomenon in modern wireless sensor networks. It is more severe for multi-hop sensor network (MSN) applications where sensor data reach the base station via other sensors; hence a sensor's failure can cause multiple missing data. In this paper we present MASTER-M, a data estimation framework based on data clustering and association rule mining to estimate the values of missing sensor data for MSN. Estimating, instead of resending, the missing sensor data is becoming popular as it may reduce query response time and sensor energy consumption; however the current works cater to only single-hop sensor networks. To fill this gap, our novel technique addresses the issues related to MSN, such as simultaneous missing sensors and missing spatially correlated sensors. It consists of three steps: 1) clustering sensors online; 2) capturing association rules between sensors inside each cluster, and 3) estimating the values of the missing data using the obtained association rules. Experimental results on both real-life sensor data and synthetic sensor data demonstrate the efficacy of MASTER-M in terms of estimation accuracy compared to the existing techniques. Moreover, we also present experiments showing the supremacy of data estimation by MASTER-M in terms of energy savings over re-transmission of missing data.