Joint sensor location/power rating optimization for temporally-correlated source estimation

Osama Bushnaq, Anas Chaaban, Tareq Y. Al-Naffouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publication2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-5
Number of pages5
ISBN (Print)9781509030095
DOIs
StatePublished - Dec 22 2017

Fingerprint Dive into the research topics of 'Joint sensor location/power rating optimization for temporally-correlated source estimation'. Together they form a unique fingerprint.

Cite this