Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks

Ping Che Hsiao; Tsung Che Chiang; Li Chen Fu

doi:10.1109/CEC.2012.6252949

Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks

Ping Che Hsiao^*, Tsung Che Chiang, Li Chen Fu

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

Abstract

In this paper, we propose a novel approach based on particle swarm optimization (PSO) for solving the minimum energy broadcast (MEB) problem, which has been proven to be NP-complete. Wireless sensor networks (WSNs) have attracted large intention in recent years due to its powerful ability. One crucial issue in WSN is energy saving because of the limited battery resource. The MEB problem is one of the important scenarios in WSN, where a node needs to broadcast packets to all other nodes in the network. The objective is to minimize power consumption of all nodes in the network. Here we take advantage of fast and guided convergence characteristics of PSO to solve the MEB problem. For applying PSO to the MEB problem, we use the power degree to define the particle position. We go a step further to analyze one well-known local search mechanism: r-shrink and propose an improved version. The experimental results show that the proposed approach is able to compete and even outperform state-of-the-art works.

Original language	English
Title of host publication	2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs	https://doi.org/10.1109/CEC.2012.6252949
Publication status	Published - 2012
Event	2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: 2012 Jun 10 → 2012 Jun 15

Publication series

Name	2012 IEEE Congress on Evolutionary Computation, CEC 2012

Other

Other	2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/Territory	Australia
City	Brisbane, QLD
Period	2012/06/10 → 2012/06/15

Keywords

Minimum Energy Broadcast Problem
Minimum Power Broadcast Problem
Network Routing
Particle Swarm Optimizatioin
Wireless Ad-Hoc Networks
Wireless Sensor Networks

ASJC Scopus subject areas

Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1109/CEC.2012.6252949

Cite this

Hsiao, PC, Chiang, TC & Fu, LC 2012, Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks. in 2012 IEEE Congress on Evolutionary Computation, CEC 2012., 6252949, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, Brisbane, QLD, Australia, 2012/06/10. https://doi.org/10.1109/CEC.2012.6252949

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title = "Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks",

abstract = "In this paper, we propose a novel approach based on particle swarm optimization (PSO) for solving the minimum energy broadcast (MEB) problem, which has been proven to be NP-complete. Wireless sensor networks (WSNs) have attracted large intention in recent years due to its powerful ability. One crucial issue in WSN is energy saving because of the limited battery resource. The MEB problem is one of the important scenarios in WSN, where a node needs to broadcast packets to all other nodes in the network. The objective is to minimize power consumption of all nodes in the network. Here we take advantage of fast and guided convergence characteristics of PSO to solve the MEB problem. For applying PSO to the MEB problem, we use the power degree to define the particle position. We go a step further to analyze one well-known local search mechanism: r-shrink and propose an improved version. The experimental results show that the proposed approach is able to compete and even outperform state-of-the-art works.",

keywords = "Minimum Energy Broadcast Problem, Minimum Power Broadcast Problem, Network Routing, Particle Swarm Optimizatioin, Wireless Ad-Hoc Networks, Wireless Sensor Networks",

author = "Hsiao, {Ping Che} and Chiang, {Tsung Che} and Fu, {Li Chen}",

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doi = "10.1109/CEC.2012.6252949",

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AU - Chiang, Tsung Che

AU - Fu, Li Chen

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N2 - In this paper, we propose a novel approach based on particle swarm optimization (PSO) for solving the minimum energy broadcast (MEB) problem, which has been proven to be NP-complete. Wireless sensor networks (WSNs) have attracted large intention in recent years due to its powerful ability. One crucial issue in WSN is energy saving because of the limited battery resource. The MEB problem is one of the important scenarios in WSN, where a node needs to broadcast packets to all other nodes in the network. The objective is to minimize power consumption of all nodes in the network. Here we take advantage of fast and guided convergence characteristics of PSO to solve the MEB problem. For applying PSO to the MEB problem, we use the power degree to define the particle position. We go a step further to analyze one well-known local search mechanism: r-shrink and propose an improved version. The experimental results show that the proposed approach is able to compete and even outperform state-of-the-art works.

AB - In this paper, we propose a novel approach based on particle swarm optimization (PSO) for solving the minimum energy broadcast (MEB) problem, which has been proven to be NP-complete. Wireless sensor networks (WSNs) have attracted large intention in recent years due to its powerful ability. One crucial issue in WSN is energy saving because of the limited battery resource. The MEB problem is one of the important scenarios in WSN, where a node needs to broadcast packets to all other nodes in the network. The objective is to minimize power consumption of all nodes in the network. Here we take advantage of fast and guided convergence characteristics of PSO to solve the MEB problem. For applying PSO to the MEB problem, we use the power degree to define the particle position. We go a step further to analyze one well-known local search mechanism: r-shrink and propose an improved version. The experimental results show that the proposed approach is able to compete and even outperform state-of-the-art works.

KW - Minimum Energy Broadcast Problem

KW - Minimum Power Broadcast Problem

KW - Network Routing

KW - Particle Swarm Optimizatioin

KW - Wireless Ad-Hoc Networks

KW - Wireless Sensor Networks

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Y2 - 10 June 2012 through 15 June 2012

ER -

Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks

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