Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Optimization of Observations: a Stochastic Control Approach
SIAM Journal on Control and Optimization
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
State estimation over packet dropping networks using multiple description coding
Automatica (Journal of IFAC)
Brief paper: Hybrid method for a general optimal sensor scheduling problem in discrete time
Automatica (Journal of IFAC)
Brief paper: Observer design for wired linear networked control systems using matrix inequalities
Automatica (Journal of IFAC)
Remote Estimation with Sensor Scheduling
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Power-efficient dimensionality reduction for distributed channel-aware kalman tracking using WSNs
IEEE Transactions on Signal Processing
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
On the roles of smoothing in planning of informative paths
ACC'09 Proceedings of the 2009 conference on American Control Conference
Scheduling Kalman filters in continuous time
ACC'09 Proceedings of the 2009 conference on American Control Conference
Cross-entropy optimization for sensor selection problems
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Estimation of distribution algorithm for sensor selection problems
RWS'10 Proceedings of the 2010 IEEE conference on Radio and wireless symposium
Continuous trajectory planning of mobile sensors for informative forecasting
Automatica (Journal of IFAC)
Journal of Real-Time Image Processing
Linear systems with medium-access constraint and Markov actuator assignment
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Sensor selection strategies for state estimation in energy constrained wireless sensor networks
Automatica (Journal of IFAC)
Tracking a moving object via a sensor network with a partial information broadcasting scheme
Information Sciences: an International Journal
Automatica (Journal of IFAC)
On the Network Coverage Intensity in the Presence of Clock Asynchrony
Wireless Personal Communications: An International Journal
Brief paper: Data-driven communication for state estimation with sensor networks
Automatica (Journal of IFAC)
On efficient sensor scheduling for linear dynamical systems
Automatica (Journal of IFAC)
Distributed Active Sensor Scheduling for Target Tracking in Ultrasonic Sensor Networks
Mobile Networks and Applications
Scheduling parallel Kalman filters for multiple processes
Automatica (Journal of IFAC)
Approximate optimal periodic scheduling of multiple sensors with constraints
Automatica (Journal of IFAC)
Stochastic surveillance strategies for spatial quickest detection
International Journal of Robotics Research
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
Hi-index | 22.17 |
In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.