Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - An adaptive and composite spatio-temporal data compression approach for wireless sensor networks
AU - Ali, A.
AU - Khelil, A.
AU - Szczytowski, P.
AU - Suri, Neeraj
PY - 2011/10/31
Y1 - 2011/10/31
N2 - Wireless Sensor Networks (WSN) are often deployed to sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data volumes. Sensor nodes are battery powered and sending the requested large amount of data rapidly depletes their energy. Fortunately, the environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific measurement and forensics tolerate high latencies for sensor data collection. Accordingly, we develop a fully distributed adaptive technique for spatial and temporal innetwork data compression with accuracy guarantees. We exploit the spatio-temporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to further minimize the amount of data to be transported to the sink. Using real data, we demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data. In our simulations, we achieved data compression of up to 95% on the raw data requiring around 5% of the original data to be transported to the sink. Copyright 2011 ACM.
AB - Wireless Sensor Networks (WSN) are often deployed to sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data volumes. Sensor nodes are battery powered and sending the requested large amount of data rapidly depletes their energy. Fortunately, the environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific measurement and forensics tolerate high latencies for sensor data collection. Accordingly, we develop a fully distributed adaptive technique for spatial and temporal innetwork data compression with accuracy guarantees. We exploit the spatio-temporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to further minimize the amount of data to be transported to the sink. Using real data, we demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data. In our simulations, we achieved data compression of up to 95% on the raw data requiring around 5% of the original data to be transported to the sink. Copyright 2011 ACM.
KW - Accuracy
KW - Approximation
KW - Energy efficiency
KW - Hierarchical clustering
KW - Modeling
KW - Spatial and temporal correlations
KW - Adaptive technique
KW - Battery powered
KW - Compression approach
KW - Data accuracy
KW - Data delivery
KW - Data volume
KW - Environmental attributes
KW - Hier-archical clustering
KW - High granularity
KW - Large class
KW - Latency tolerance
KW - Sensor data collections
KW - Sensor readings
KW - Spatial and temporal correlation
KW - Spatio-temporal data
KW - Spatiotemporal correlation
KW - Computer simulation
KW - Models
KW - Sensor nodes
KW - Sensors
KW - Data compression
U2 - 10.1145/2068897.2068912
DO - 10.1145/2068897.2068912
M3 - Conference contribution/Paper
SP - 67
EP - 76
BT - Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
PB - ACM
ER -