By Topic

A Monte Carlo based energy efficient source localization method for wireless sensor networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Masazade, E. ; Fac. of Eng. & Natural Sci., Sabanci Univ., Istanbul, Turkey ; Ruixin Niu ; Varshney, P.K. ; Keskinoz, M.

In this paper, we study the source localization problem in wireless sensor networks where the location of the source is estimated according to the quantized measurements received from sensors in the field. We propose an energy efficient iterative source localization scheme, where the algorithm begins with a coarse location estimate obtained from a set of anchor sensors. Based on the available data at each iteration, we approximate the posterior probability density function (pdf) of the source location using a Monte Carlo method and we use this information to activate a number of non-anchor sensors that minimize the Conditional Posterior Crame¿r Rao Lower Bound (C-PCRLB). Then we also use the Monte Carlo approximation of the posterior pdf of the source location to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that the proposed iterative method achieves the mean squared error that gets close to the unconditional Posterior Crame¿r Rao Lower Bound (PCRLB) for a Bayesian estimate based on quantized data from all the sensors within a few iterations. By selecting only the most informative sensors, the iterative approach also reduces the communication requirements significantly and resulting in energy savings.

Published in:

Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009 3rd IEEE International Workshop on

Date of Conference:

13-16 Dec. 2009