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An adaptive and composite spatio-temporal data compression approach for wireless sensor networks

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An adaptive and composite spatio-temporal data compression approach for wireless sensor networks. / Ali, A.; Khelil, A.; Szczytowski, P.; Suri, Neeraj.

Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. ACM, 2011. p. 67-76.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Ali, A, Khelil, A, Szczytowski, P & Suri, N 2011, An adaptive and composite spatio-temporal data compression approach for wireless sensor networks. in Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. ACM, pp. 67-76. https://doi.org/10.1145/2068897.2068912

APA

Ali, A., Khelil, A., Szczytowski, P., & Suri, N. (2011). An adaptive and composite spatio-temporal data compression approach for wireless sensor networks. In Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems (pp. 67-76). ACM. https://doi.org/10.1145/2068897.2068912

Vancouver

Ali A, Khelil A, Szczytowski P, Suri N. An adaptive and composite spatio-temporal data compression approach for wireless sensor networks. In Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. ACM. 2011. p. 67-76 https://doi.org/10.1145/2068897.2068912

Author

Ali, A. ; Khelil, A. ; Szczytowski, P. ; Suri, Neeraj. / An adaptive and composite spatio-temporal data compression approach for wireless sensor networks. Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems. ACM, 2011. pp. 67-76

Bibtex

@inproceedings{59723214b0fe49e3930ed693b8ccf3f3,
title = "An adaptive and composite spatio-temporal data compression approach for wireless sensor networks",
abstract = "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.",
keywords = "Accuracy, Approximation, Energy efficiency, Hierarchical clustering, Modeling, Spatial and temporal correlations, Adaptive technique, Battery powered, Compression approach, Data accuracy, Data delivery, Data volume, Environmental attributes, Hier-archical clustering, High granularity, Large class, Latency tolerance, Sensor data collections, Sensor readings, Spatial and temporal correlation, Spatio-temporal data, Spatiotemporal correlation, Computer simulation, Models, Sensor nodes, Sensors, Data compression",
author = "A. Ali and A. Khelil and P. Szczytowski and Neeraj Suri",
year = "2011",
month = oct,
day = "31",
doi = "10.1145/2068897.2068912",
language = "English",
pages = "67--76",
booktitle = "Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems",
publisher = "ACM",

}

RIS

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 -