Comment on “Energy Aware Iterative Source Localization for Wireless Sensor Networks”

Author

Xiaojun Yang

Author_Institution

Sch. of Inf. Eng., Chang´an Univ., Xi´an, China

Volume

62

Issue

9

fYear

2014

fDate

1-May-14

Firstpage

2236

Lastpage

2237

Abstract

A recent paper [E. Masazade, R. Niu, P. K. Varshney, and M. Keskinoz, “Energy Aware Iterative Source Localization for Wireless Sensor Networks”, IEEE Transactions on Signal Processing, vol. 58, no. 9, pp. 4824-4835, 2010] presents an iterative source localization scheme based on the selection of sensors. An importance sampling based Monte Carlo method was employed at each iteration using the entire received data to approximate the posterior distribution of the source location in the paper. In this note, we propose an alternative sequential importance sampling and resampling algorithm to implement the iterative source localization. The proposed method provides significant performance improvement over the current method via combination of auxiliary particle filtering with kernel smoothing approximation. The superiority of the alternative algorithm is verified by Monte Carlo simulation results.

Keywords

Monte Carlo methods; particle filtering (numerical methods); smoothing methods; telecommunication power management; wireless sensor networks; Monte Carlo method; energy aware iterative source localization; kernel smoothing approximation; particle filtering; posterior distribution; sensor selection; sequential importance resampling algorithm; sequential importance sampling algorithm; wireless sensor networks; Approximation methods; Kernel; Monte Carlo methods; Position measurement; Sensors; Signal processing algorithms; Wireless sensor networks; Iterative source localization; Monte Carlo method; kernel smoothing; particle filtering;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2014.2309092

Filename

6750772