poostfroushan S, Agha sarram M, Sheikhpour R. Distributed energy efficient backbone construction utilizing particle swarm optimization algorithm in wireless sensor networks with bidirectional links. MJEE 2016; 16 (2) :72-82
URL:
http://mjee.modares.ac.ir/article-17-11917-en.html
1- Electrical and Computer Engineering Department, Yazd University, Yazd, Iran
2- R. S. Electrical and Computer Engineering Department, Yazd University, Yazd, Iran
Abstract: (3314 Views)
Connected dominating set (CDS) problem is the most widely used method for backbone formation in wireless sensor networks. To date, numerous algorithms have been proposed for backbone construction on minimum CDS (MCDS) problem in unit disk graphs (UDG); however, only a few algorithms have been proposed on MCDS problem in disk graphs with bidirectional links (DGB) and on degree-constrained minimum-weight CDS (DC-MWCDS) problem in UDG. To the best of our knowledge, no work has been done on DC-MWCDS problem in DGB. In this paper, we present OEDC-MWCDS problem (optimal energy and degree constrained minimum-weight connected dominating set) for constructing energy efficient backbone in wireless sensor networks. Then, we model a wireless sensor network as a disk graph with bidirectional links and propose a backbone construction algorithm called EBC-PSO (Energy efficient Backbone Construction utilizing Particle Swarm Optimization algorithm) to obtain a CDS with the minimum weight subject to the optimal energy and degree constraint. The main objective of the proposed algorithm is to find the optimal values of energy and degree of constraint to maximize network lifetime. In the proposed algorithm, optimal coefficients of minimum remaining energy and maximum degree of nodes are determined utilizing PSO algorithm. Then, in the selection of DS nodes, these coefficients are used. Simulation results verify the performance of the proposed algorithm in terms of network lifetime and backbone size.
Article Type:
Full Research Paper |
Subject:
معماری شبکه های کامپیوتری Received: 2017/02/15 | Accepted: 2016/06/1 | Published: 2018/02/3