TY - GEN

T1 - Distributed fermat-point location estimation for wireless sensor network applications

AU - Chen, Jiann Liang

AU - Chen, Ming Chiao

AU - Chiang, Tsui Lien

AU - Chang, Yao Chung

AU - Shih, Frank Y.

PY - 2007

Y1 - 2007

N2 - This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances from three sensors inside a triangle. This point is indeed at the triangle's center of gravity. We solve the problems of large errors and poor performance in the bounding box algorithm. We obtain two results by performance analysis for a deployed environment with 200 sensor nodes. First, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error stays below 1%. Second, when the number of beacon nodes is below 60, the normal nodes do not have sufficient number of accurate beacon nodes to help them estimate their locations. However, when the number of beacon nodes exceeds 60, the mean error changes slightly. Simulation results indicated that the proposed algorithm for sensor position estimation is more accurate than existing algorithms and improves on existing bounding box strategies.

AB - This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances from three sensors inside a triangle. This point is indeed at the triangle's center of gravity. We solve the problems of large errors and poor performance in the bounding box algorithm. We obtain two results by performance analysis for a deployed environment with 200 sensor nodes. First, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error stays below 1%. Second, when the number of beacon nodes is below 60, the normal nodes do not have sufficient number of accurate beacon nodes to help them estimate their locations. However, when the number of beacon nodes exceeds 60, the mean error changes slightly. Simulation results indicated that the proposed algorithm for sensor position estimation is more accurate than existing algorithms and improves on existing bounding box strategies.

KW - Bounding box algorithm

KW - Distributed Fermat-Point Location Estimation (DFPLE)

KW - Range estimation

KW - Wireless sensor network

UR - http://www.scopus.com/inward/record.url?scp=51849084759&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=51849084759&partnerID=8YFLogxK

U2 - 10.1109/SARNOF.2007.4567317

DO - 10.1109/SARNOF.2007.4567317

M3 - Conference contribution

AN - SCOPUS:51849084759

SN - 1424424836

SN - 9781424424832

T3 - 2007 IEEE Sarnoff Symposium, SARNOFF

BT - 2007 IEEE Sarnoff Symposium, SARNOFF

T2 - IEEE Sarnoff Symposium, SARNOFF 2007

Y2 - 30 April 2007 through 2 May 2007

ER -