Abstract
The estimation of a random scalar parameter using sensors' noisy data has been studied in the context of wireless sensor networks. In energy-constrained networks, the sensors are restricted to low transmission rates to preserve battery power. Hence, in our distributed estimation method, only one bit of a B-bit quantization is sent from each sensor to the fusion center, where the unknown parameter is estimated. To optimize the estimation performance in an inhomogeneous sensor network, we find the best bit-sensor assignment based on the sensors' noise variance. In our method, we use the Hungarian algorithm for the assignment optimization problem.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
