System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Architecture-Level Compact Thermal R-C Modeling
Architecture-Level Compact Thermal R-C Modeling
Graph Theory with Applications to Engineering and Computer Science (Prentice Hall Series in Automatic Computation)
A prediction error-based hypothesis testing method for sensor data acquisition
ACM Transactions on Sensor Networks (TOSN)
Stability of Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
Identification in sensor networks
ICAI'08 Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information
Kriging for Localized Spatial Interpolation in Sensor Networks
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
WSEAS TRANSACTIONS on SYSTEMS
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
IEEE Transactions on Signal Processing
ACMOS'09 Proceedings of the 11th WSEAS international conference on Automatic control, modelling and simulation
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In many sensor networks applications, sensors collect correlated measurements of a physical field, e.g., temperature field in a building or in a data center. However, the locations of the sensors are usually inconsistent with the application requirements. In this paper, we consider the problem of estimating the field at arbitrary positions of interest, where there are possibly no sensors, from the irregularly placed sensors. We map this sensor network on a graph, and, by introducing the concepts of interconnection matrices, system digraphs, and cut point sets, we can pose sensor network tradeoffs and derive real-time field estimation algorithms. The results of temperature field estimation, obtained from simulations and real world experiments, show that the methodology presented in this paper can successfully predict the field values at arbitrary locations, including others than the ones with sensors.