Spatial databases with application to GIS
Spatial databases with application to GIS
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Adaptive cell-based index for moving objects
Data & Knowledge Engineering
Sensor networks: a bridge to the physical world
Wireless sensor networks
Computer
Approximate Data Collection in Sensor Networks using Probabilistic Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Faster In-Network Evaluation of Spatial Aggregationin Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
IEEE Communications Magazine
A Stimulus-Centric Algebraic Approach to Sensors and Observations
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
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Environmental observation applications are designed for monitoring phenomena using heterogeneous sensor data types and for providing derived and often integrated information. To effectively handle such a large variety of different sensors, both in scale and type and data volume, we propose a geosensor abstraction for large-scale geosensor networks. Our SGSA(Slope Grid for Sensor Data Abstraction) represents collected data in single grid-based layers, and allows for summarizing the measured data in various integrated grid layers. Within each cell, a slope vector is used to represents the trend of the observed sensor data. This slope is used as a simplifying factor for processing queries over several sensor types. To handle dynamic sensor data, the proposed abstraction model also supports rapid data update by using a mapping table. This model can be utilized as a data representation model in various geosensor network applications.