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The Scientific World Journal
Volume 2014, Article ID 580854, 8 pages
http://dx.doi.org/10.1155/2014/580854
Research Article

Distributed Optimal Power and Rate Control in Wireless Sensor Networks

1School of Mathematics and Statistics, Ludong University, Yantai 264025, China
2Department of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
3School of Information Engineering, Nanchang University, Nanchang 330031, China

Received 23 January 2014; Accepted 20 February 2014; Published 8 May 2014

Academic Editors: N. Barsoum, P. Vasant, and G.-W. Weber

Copyright © 2014 Meiqin Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the rapid development of wireless sensor networks, reducing energy consumption is becoming one of the important factors to extend node lifetime, and it is necessary to adjust the launching power of each node because of the limited energy available to the sensor nodes in the networks. This paper proposes a power and rate control model based on the network utility maximization (NUM) framework, where a weighting factor is used to reflect the influence degree of the sending power and transmission rate to the utility function. In real networks, nodes interfere with each other in the procedure of transmitting signal, which may lead to signal transmission failure and may negatively have impacts on networks throughput. Using dual decomposition techniques, the NUM problem is decomposed into two distributed subproblems, and then the conjugate gradient method is applied to solve the optimization problem with the calculation of the Hessian matrix and its inverse in order to guarantee fast convergence of the algorithm. The convergence proof is also provided in this paper. Numerical examples show that the proposed solution achieves significant throughput compared with exiting approaches.