The 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.
|Title of host publication
|2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
|Institute of Electrical and Electronics Engineers (IEEE)
|Number of pages
|Published - Dec 22 2017
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700.