An online spatio-temporal association rule mining framework for analyzing and estimating sensor data
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Spatio-temporal association rule mining framework for real-time sensor network applications
Proceedings of the 18th ACM conference on Information and knowledge management
Using data mining to handle missing data in multi-hop sensor network applications
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
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
SKIF: a data imputation framework for concept drifting data streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
DBOD-DS: distance based outlier detection for data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Dispersion-based prediction framework for estimating missing values in wireless sensor networks
International Journal of Sensor Networks
Efficient and scalable monitoring and summarization of large probabilistic data
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
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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Estimating missing sensor values is an inherent problem in sensor network applications; however, existing data estimation approaches do not apply well to the context of datastreams, a major characteristic of sensornet applications. Additionally, they fail to account for relationships among sensors and simultaneously, incorporate the time factor making the estimation process computationally aware of the relative relevance of each data round in the datastream. To address this gap, we propose a data estimation technique, FARM, which uses association rule mining to discover intrinsic relationships among sensors and incorporate them into the data estimation while taking data freshness into consideration. FARM was tested with data from two real sensornet applications, namely climate sensing and traffic monitoring. Simulation shows that in terms of estimation accuracy, FARM outperformed existing techniques costing only marginally more space and time overheads while scaling well with the network size, thus assuring quality of service for real-time applications.