Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Research challenges in wireless networks of biomedical sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Research issues in data stream association rule mining
ACM SIGMOD Record
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Energy-efficient monitoring of extreme values in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Using Data Mining to Estimate Missing Sensor Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
DEMS: a data mining based technique to handle missing data in mobile sensor network applications
Proceedings of the Seventh International Workshop on Data Management for Sensor Networks
A novel real-time framework for extracting patterns from trajectory data streams
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
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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.