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 | |
| Publication status | Published - 2012 |
| Event | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: 2012 Jun 10 → 2012 Jun 15 |
Other
| Other | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
|---|---|
| Country | Australia |
| City | Brisbane, QLD |
| Period | 12/6/10 → 12/6/15 |
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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
Cite this
Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks. / Hsiao, Ping Che; Chiang, Tsung Che; Fu, Li Chen.
2012 IEEE Congress on Evolutionary Computation, CEC 2012. 2012. 6252949.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks
AU - Hsiao, Ping Che
AU - Chiang, Tsung Che
AU - Fu, Li Chen
PY - 2012
Y1 - 2012
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
UR - http://www.scopus.com/inward/record.url?scp=84866866071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866866071&partnerID=8YFLogxK
U2 - 10.1109/CEC.2012.6252949
DO - 10.1109/CEC.2012.6252949
M3 - Conference contribution
AN - SCOPUS:84866866071
SN - 9781467315098
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
ER -