Joint sensor location/power rating optimization for temporally-correlated source estimation
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberOSR-2015-Sensors-2700
Online Publication Date2017-12-22
Print Publication Date2017-07
Permanent link to this recordhttp://hdl.handle.net/10754/626663
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AbstractThe optimal sensor selection for scalar state parameter estimation in wireless sensor networks is studied in the paper. A subset of N candidate sensing locations is selected to measure a state parameter and send the observation to a fusion center via wireless AWGN channel. In addition to selecting the optimal sensing location, the sensor type to be placed in these locations is selected from a pool of T sensor types such that different sensor types have different power ratings and costs. The sensor transmission power is limited based on the amount of energy harvested at the sensing location and the type of the sensor. The Kalman filter is used to efficiently obtain the MMSE estimator at the fusion center. Sensors are selected such that the MMSE estimator error is minimized subject to a prescribed system budget. This goal is achieved using convex relaxation and greedy algorithm approaches.
CitationBushnaq OM, Chaaban A, Al-Naffouri T (2017) Joint sensor location/power rating optimization for temporally-correlated source estimation. 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Available: http://dx.doi.org/10.1109/SPAWC.2017.8227640.
SponsorsThis work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.
Journal2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)