Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -