Abstract
In Wireless Sensor Networks (WSN), the sensor nodes and its central node are located some distance apart to ensure good coverage over the concerning area. However, the communication distance between sensor nodes is directly proportional to power consumption, and the ultimate effective point-to-point distance is nevertheless limited. To overcome this problem, a cluster based layout formation and an message routing algorithm among the head node of each cluster are suggested to ensure a WSN achieve good coverage, balance workload and traffic-load, and prolong the overall network lifetime. In this paper, we use a Negotiable Evolutionary Algorithm (NEA) to solve the complex multi-object WSN layout and signal routing problem. Experiment result shows that NEA is an effective approach for solving the problem.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 |
| Pages | 207-210 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 - Kitakyushu Duration: 2012 Aug 25 → 2012 Aug 28 |
Other
| Other | 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 |
|---|---|
| City | Kitakyushu |
| Period | 12/8/25 → 12/8/28 |
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Keywords
- Evolutionary algorithm
- Multi-objective optimization
- Wireless sensor network
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
Cite this
A wireless sensor network topology design method based on negotiable evolutionary algorithm. / Lin, Hao Wen; Zhang, Li; Hao, Xinchang; Murata, Tomohiro.
Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012. 2012. p. 207-210 6457138.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A wireless sensor network topology design method based on negotiable evolutionary algorithm
AU - Lin, Hao Wen
AU - Zhang, Li
AU - Hao, Xinchang
AU - Murata, Tomohiro
PY - 2012
Y1 - 2012
N2 - In Wireless Sensor Networks (WSN), the sensor nodes and its central node are located some distance apart to ensure good coverage over the concerning area. However, the communication distance between sensor nodes is directly proportional to power consumption, and the ultimate effective point-to-point distance is nevertheless limited. To overcome this problem, a cluster based layout formation and an message routing algorithm among the head node of each cluster are suggested to ensure a WSN achieve good coverage, balance workload and traffic-load, and prolong the overall network lifetime. In this paper, we use a Negotiable Evolutionary Algorithm (NEA) to solve the complex multi-object WSN layout and signal routing problem. Experiment result shows that NEA is an effective approach for solving the problem.
AB - In Wireless Sensor Networks (WSN), the sensor nodes and its central node are located some distance apart to ensure good coverage over the concerning area. However, the communication distance between sensor nodes is directly proportional to power consumption, and the ultimate effective point-to-point distance is nevertheless limited. To overcome this problem, a cluster based layout formation and an message routing algorithm among the head node of each cluster are suggested to ensure a WSN achieve good coverage, balance workload and traffic-load, and prolong the overall network lifetime. In this paper, we use a Negotiable Evolutionary Algorithm (NEA) to solve the complex multi-object WSN layout and signal routing problem. Experiment result shows that NEA is an effective approach for solving the problem.
KW - Evolutionary algorithm
KW - Multi-objective optimization
KW - Wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=84874377702&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874377702&partnerID=8YFLogxK
U2 - 10.1109/ICGEC.2012.37
DO - 10.1109/ICGEC.2012.37
M3 - Conference contribution
SN - 9780769547633
SP - 207
EP - 210
BT - Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
ER -