Abstract
Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem.
| Original language | English |
|---|---|
| Title of host publication | Evolvable Systems: From Biology to Hardware |
| Subtitle of host publication | 8th International Conference, ICES 2008, Prague, Czech Republic, September 21-24, 2008. Proceedings |
| Place of Publication | Berlin |
| Pages | 249-260 |
| Number of pages | 12 |
| DOIs | |
| Publication status | Published - 8 Sep 2008 |
| Event | 8th International Conference on Evolvable Systems: From Biology to Hardware, ICES 2008 - Prague, United Kingdom Duration: 21 Sep 2008 → 24 Sep 2008 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer Berlin Heidelberg |
| Volume | 5216 |
| ISSN (Print) | 0302-9743 |
Conference
| Conference | 8th International Conference on Evolvable Systems: From Biology to Hardware, ICES 2008 |
|---|---|
| Country | United Kingdom |
| City | Prague |
| Period | 21/09/08 → 24/09/08 |
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Keywords
- cluster-based
- comparative studies
- high dimensionality
- metropolis algorithms
- optimization algorithms
- optimization problems
- particle swarm optimizers
- population genetics
- scalability issue
- shifting balance theory
- total energy consumption
- transmission power
- WSN optimization
Cite this
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Scalability of a novel shifting balance theory-based optimization algorithm : a comparative study on a cluster-based wireless sensor network. / Yang, Erfu; Barton, Nick H.; Arslan, Tughrul; Erdogan, Ahmet T.
Evolvable Systems: From Biology to Hardware: 8th International Conference, ICES 2008, Prague, Czech Republic, September 21-24, 2008. Proceedings. Berlin, 2008. p. 249-260 (Lecture Notes in Computer Science; Vol. 5216).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Scalability of a novel shifting balance theory-based optimization algorithm
T2 - a comparative study on a cluster-based wireless sensor network
AU - Yang, Erfu
AU - Barton, Nick H.
AU - Arslan, Tughrul
AU - Erdogan, Ahmet T.
PY - 2008/9/8
Y1 - 2008/9/8
N2 - Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem.
AB - Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem.
KW - cluster-based
KW - comparative studies
KW - high dimensionality
KW - metropolis algorithms
KW - optimization algorithms
KW - optimization problems
KW - particle swarm optimizers
KW - population genetics
KW - scalability issue
KW - shifting balance theory
KW - total energy consumption
KW - transmission power
KW - WSN optimization
UR - http://www.scopus.com/inward/record.url?scp=70349887604&partnerID=8YFLogxK
UR - http://link.springer.com/
UR - http://www.fit.vutbr.cz/events/ices2008
U2 - 10.1007/978-3-540-85857-7_22
DO - 10.1007/978-3-540-85857-7_22
M3 - Conference contribution book
SN - 9783540858560
T3 - Lecture Notes in Computer Science
SP - 249
EP - 260
BT - Evolvable Systems: From Biology to Hardware
CY - Berlin
ER -