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Efficient predictive monitoring of wireless sensor networks

Research output: Contribution to journalJournal article

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Efficient predictive monitoring of wireless sensor networks. / Ali, A.; Khelil, A.; Shaikh, F.K.; Suri, Neeraj.

In: International Journal of Autonomous and Adaptive Communications Systems, Vol. 5, No. 3, 2012, p. 233-254.

Research output: Contribution to journalJournal article

Harvard

Ali, A, Khelil, A, Shaikh, FK & Suri, N 2012, 'Efficient predictive monitoring of wireless sensor networks', International Journal of Autonomous and Adaptive Communications Systems, vol. 5, no. 3, pp. 233-254. https://doi.org/10.1504/IJAACS.2012.047657

APA

Ali, A., Khelil, A., Shaikh, F. K., & Suri, N. (2012). Efficient predictive monitoring of wireless sensor networks. International Journal of Autonomous and Adaptive Communications Systems, 5(3), 233-254. https://doi.org/10.1504/IJAACS.2012.047657

Vancouver

Ali A, Khelil A, Shaikh FK, Suri N. Efficient predictive monitoring of wireless sensor networks. International Journal of Autonomous and Adaptive Communications Systems. 2012;5(3):233-254. https://doi.org/10.1504/IJAACS.2012.047657

Author

Ali, A. ; Khelil, A. ; Shaikh, F.K. ; Suri, Neeraj. / Efficient predictive monitoring of wireless sensor networks. In: International Journal of Autonomous and Adaptive Communications Systems. 2012 ; Vol. 5, No. 3. pp. 233-254.

Bibtex

@article{d8e615aae15d4343b24a9b2dc788827c,
title = "Efficient predictive monitoring of wireless sensor networks",
abstract = "Wireless sensor networks (WSNs) are deployed to monitor physical events such as fire, or the state of physical objects such as bridges in order to support appropriate reaction to avoid potential damages. However, many situations require immediate attention or long-reaction plan. Therefore, the classical approach of just detecting the physical events may not suffice in many cases. We present a generic WSN level event prediction framework to forecast the physical events, such as network partitioning, well in advance to support proactive self-actions. The framework collects the state of a specified attribute on the sink using an efficient spatio-temporal compression technique. The future state of the targeted attributes is then predicted using time series modelling. We propose a generic event prediction algorithm, which is adaptable to multiple application domains. Using simulations we show our framework's enhanced ability to accurately predict the network partitioning with very high accuracy and efficiency. Copyright {\textcopyright} 2012 Inderscience Enterprises Ltd.",
keywords = "Event detection and prediction, Predictive monitoring, Spatio-temporal compression, Time series analysis, Wireless sensor networks, WSNs, Forecasting, Event detection and predictions, Multiple applications, Network partitioning, Spatio-temporal compressions, Time-series modelling, Wireless sensor network (WSNs)",
author = "A. Ali and A. Khelil and F.K. Shaikh and Neeraj Suri",
year = "2012",
doi = "10.1504/IJAACS.2012.047657",
language = "English",
volume = "5",
pages = "233--254",
journal = "International Journal of Autonomous and Adaptive Communications Systems",
issn = "1754-8632",
publisher = "Inderscience Enterprises Ltd.",
number = "3",

}

RIS

TY - JOUR

T1 - Efficient predictive monitoring of wireless sensor networks

AU - Ali, A.

AU - Khelil, A.

AU - Shaikh, F.K.

AU - Suri, Neeraj

PY - 2012

Y1 - 2012

N2 - Wireless sensor networks (WSNs) are deployed to monitor physical events such as fire, or the state of physical objects such as bridges in order to support appropriate reaction to avoid potential damages. However, many situations require immediate attention or long-reaction plan. Therefore, the classical approach of just detecting the physical events may not suffice in many cases. We present a generic WSN level event prediction framework to forecast the physical events, such as network partitioning, well in advance to support proactive self-actions. The framework collects the state of a specified attribute on the sink using an efficient spatio-temporal compression technique. The future state of the targeted attributes is then predicted using time series modelling. We propose a generic event prediction algorithm, which is adaptable to multiple application domains. Using simulations we show our framework's enhanced ability to accurately predict the network partitioning with very high accuracy and efficiency. Copyright © 2012 Inderscience Enterprises Ltd.

AB - Wireless sensor networks (WSNs) are deployed to monitor physical events such as fire, or the state of physical objects such as bridges in order to support appropriate reaction to avoid potential damages. However, many situations require immediate attention or long-reaction plan. Therefore, the classical approach of just detecting the physical events may not suffice in many cases. We present a generic WSN level event prediction framework to forecast the physical events, such as network partitioning, well in advance to support proactive self-actions. The framework collects the state of a specified attribute on the sink using an efficient spatio-temporal compression technique. The future state of the targeted attributes is then predicted using time series modelling. We propose a generic event prediction algorithm, which is adaptable to multiple application domains. Using simulations we show our framework's enhanced ability to accurately predict the network partitioning with very high accuracy and efficiency. Copyright © 2012 Inderscience Enterprises Ltd.

KW - Event detection and prediction

KW - Predictive monitoring

KW - Spatio-temporal compression

KW - Time series analysis

KW - Wireless sensor networks

KW - WSNs

KW - Forecasting

KW - Event detection and predictions

KW - Multiple applications

KW - Network partitioning

KW - Spatio-temporal compressions

KW - Time-series modelling

KW - Wireless sensor network (WSNs)

U2 - 10.1504/IJAACS.2012.047657

DO - 10.1504/IJAACS.2012.047657

M3 - Journal article

VL - 5

SP - 233

EP - 254

JO - International Journal of Autonomous and Adaptive Communications Systems

JF - International Journal of Autonomous and Adaptive Communications Systems

SN - 1754-8632

IS - 3

ER -