Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Isolines: Energy-Efficient Mapping in Sensor Networks
ISCC '05 Proceedings of the 10th IEEE Symposium on Computers and Communications
Range-free localization and its impact on large scale sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
Contour map matching for event detection in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Detecting cuts in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Accurate prediction of power consumption in sensor networks
EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
Macro Programming through Bayesian Networks: Distributed Inference and Anomaly Detection
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fault tolerant multiple event detection in a wireless sensor network
Journal of Parallel and Distributed Computing
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
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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.