An Optimized Analysis of Localization Algorithm in Wireless Sensor Networks

An Optimized Analysis of Localization Algorithm in Wireless Sensor Networks Determining the position of the sensor node is the ultimate challenging one in wireless sensor networks (WSNs), which may perhaps lead a large localization error. To tackle this tricky, a novel Semi Definite Programming (SDP)—Multiplier method is proposed using time difference of arrival (TDOA) measurement in asynchronous networks. The SDP relaxation method is derived to prevaricate the optimal maximum likelihood (ML) convergence problem that will result in Lagrangian problem. Consequently, this problem can be optimally solved in a stress-free method which provides the value of multipliers. In this Relax and cut scheme, Lagrangian multiplier added in the objective function which gives the minimum value of the function that fulfills the relaxed constraints and reduce the execution time. Also to monitor the mobile target and to improve the energy of sensors, an optimization framework of selective approach algorithm has been used. The simulation result demonstrates that the proposed algorithm provides a better position estimation with less localization error and energy consumption. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

An Optimized Analysis of Localization Algorithm in Wireless Sensor Networks

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4247-7
Publisher site
See Article on Publisher Site

Abstract

Determining the position of the sensor node is the ultimate challenging one in wireless sensor networks (WSNs), which may perhaps lead a large localization error. To tackle this tricky, a novel Semi Definite Programming (SDP)—Multiplier method is proposed using time difference of arrival (TDOA) measurement in asynchronous networks. The SDP relaxation method is derived to prevaricate the optimal maximum likelihood (ML) convergence problem that will result in Lagrangian problem. Consequently, this problem can be optimally solved in a stress-free method which provides the value of multipliers. In this Relax and cut scheme, Lagrangian multiplier added in the objective function which gives the minimum value of the function that fulfills the relaxed constraints and reduce the execution time. Also to monitor the mobile target and to improve the energy of sensors, an optimization framework of selective approach algorithm has been used. The simulation result demonstrates that the proposed algorithm provides a better position estimation with less localization error and energy consumption.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Apr 26, 2017

References

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