Ten lectures on wavelets
Overload management in sensor-actuator networks used for spatially-distributed control systems
Proceedings of the 1st international conference on Embedded networked sensor systems
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
State-Centric Programming for Sensor-Actuator Network Systems
IEEE Pervasive Computing
Distributed EM algorithms for density estimation and clustering in sensor networks
IEEE Transactions on Signal Processing
Frame-theoretic analysis of oversampled filter banks
IEEE Transactions on Signal Processing
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
Power scheduling for wireless sensor and actuator networks
Proceedings of the 6th international conference on Information processing in sensor networks
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Wireless sensor networks are often studied with the goal of removing information from the network as efficiently as possible. However, when the application also includes an actuator network, it is advantageous to determine actions in-network. In such settings, optimizing the sensor node behavior with respect to sensor information fidelity does not necessarily translate into optimum behavior in terms of action fidelity. Inspired by neural systems, we present a model of a sensor and actuator network based on the vector space tools of frame theory that applies to applications analogous to reflex behaviors in biological systems. Our analysis yields bounds on both absolute and average actuation error that point directly to strategies for limiting sensor communication based not only on local measurements but also on a measure of how important each sensor-actuator link is to the fidelity of the total actuation output.