Clustering wireless sensor networks based on bird flocking behavior

Soon Gyo Jung; Sanggil Yeom; Min Han Shon; Dongsoo Stephen Kim; Hyunseung Choo

doi:10.1007/978-3-319-21410-8_10

Clustering wireless sensor networks based on bird flocking behavior

Soon Gyo Jung, Sanggil Yeom, Min Han Shon, Dongsoo Stephen Kim, Hyunseung Choo

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

5 Citations (Scopus)

Abstract

One of the most important issues in Wireless Sensor Networks (WSNs) is the efficient use of limited energy resources. A popular approach for efficient energy consumption is clustering. In this paper, we propose an energy efficient clustering algorithm, called Bird Flocking Behavior Clustering (BFBC). By adopting the bird flocking behavior, our clustering algorithm forms clusters with simple local interactions. With an improvement on the existing bio-inspired clustering algorithm, that forms a cluster using several messages, BFBC forms a cluster with only one message. Simulation results show that BFBC significantly decreases the number of messages for cluster head election, and also reduces the energy consumption for communication between cluster members and their dedicated cluster head.

Original language	English
Title of host publication	Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings
Editors	Marina L. Gavrilova, Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Carmelo Torre, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Sanjay Misra
Publisher	Springer Verlag
Pages	128-137
Number of pages	10
ISBN (Print)	9783319214092
DOIs	https://doi.org/10.1007/978-3-319-21410-8_10
Publication status	Published - 2015
Externally published	Yes
Event	15th International Conference on Computational Science and Its Applications, ICCSA 2015 - Banff, Canada Duration: 22 Jun 2015 → 25 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9158
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Computational Science and Its Applications, ICCSA 2015
Country/Territory	Canada
City	Banff
Period	22/06/15 → 25/06/15

Keywords

Bio-inspired
Bird flocking behaviour
Clustering
Collective behaviour
RSSI
Swarm Intelligence
Wireless Sensor Networks

Access to Document

10.1007/978-3-319-21410-8_10

Cite this

Jung, S. G., Yeom, S., Shon, M. H., Kim, D. S., & Choo, H. (2015). Clustering wireless sensor networks based on bird flocking behavior. In M. L. Gavrilova, O. Gervasi, B. Murgante, S. Misra, C. Torre, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, & S. Misra (Eds.), Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings (pp. 128-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9158). Springer Verlag. https://doi.org/10.1007/978-3-319-21410-8_10

Jung, Soon Gyo ; Yeom, Sanggil ; Shon, Min Han et al. / Clustering wireless sensor networks based on bird flocking behavior. Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings. editor / Marina L. Gavrilova ; Osvaldo Gervasi ; Beniamino Murgante ; Sanjay Misra ; Carmelo Torre ; David Taniar ; Bernady O. Apduhan ; Ana Maria A.C. Rocha ; Sanjay Misra. Springer Verlag, 2015. pp. 128-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Clustering wireless sensor networks based on bird flocking behavior",

abstract = "One of the most important issues in Wireless Sensor Networks (WSNs) is the efficient use of limited energy resources. A popular approach for efficient energy consumption is clustering. In this paper, we propose an energy efficient clustering algorithm, called Bird Flocking Behavior Clustering (BFBC). By adopting the bird flocking behavior, our clustering algorithm forms clusters with simple local interactions. With an improvement on the existing bio-inspired clustering algorithm, that forms a cluster using several messages, BFBC forms a cluster with only one message. Simulation results show that BFBC significantly decreases the number of messages for cluster head election, and also reduces the energy consumption for communication between cluster members and their dedicated cluster head.",

keywords = "Bio-inspired, Bird flocking behaviour, Clustering, Collective behaviour, RSSI, Swarm Intelligence, Wireless Sensor Networks",

author = "Jung, {Soon Gyo} and Sanggil Yeom and Shon, {Min Han} and Kim, {Dongsoo Stephen} and Hyunseung Choo",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 15th International Conference on Computational Science and Its Applications, ICCSA 2015 ; Conference date: 22-06-2015 Through 25-06-2015",

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doi = "10.1007/978-3-319-21410-8_10",

language = "English",

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editor = "Gavrilova, {Marina L.} and Osvaldo Gervasi and Beniamino Murgante and Sanjay Misra and Carmelo Torre and David Taniar and Apduhan, {Bernady O.} and Rocha, {Ana Maria A.C.} and Sanjay Misra",

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Jung, SG, Yeom, S, Shon, MH, Kim, DS & Choo, H 2015, Clustering wireless sensor networks based on bird flocking behavior. in ML Gavrilova, O Gervasi, B Murgante, S Misra, C Torre, D Taniar, BO Apduhan, AMAC Rocha & S Misra (eds), Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9158, Springer Verlag, pp. 128-137, 15th International Conference on Computational Science and Its Applications, ICCSA 2015, Banff, Canada, 22/06/15. https://doi.org/10.1007/978-3-319-21410-8_10

Clustering wireless sensor networks based on bird flocking behavior. / Jung, Soon Gyo; Yeom, Sanggil; Shon, Min Han et al.
Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings. ed. / Marina L. Gavrilova; Osvaldo Gervasi; Beniamino Murgante; Sanjay Misra; Carmelo Torre; David Taniar; Bernady O. Apduhan; Ana Maria A.C. Rocha; Sanjay Misra. Springer Verlag, 2015. p. 128-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9158).

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

TY - GEN

T1 - Clustering wireless sensor networks based on bird flocking behavior

AU - Jung, Soon Gyo

AU - Yeom, Sanggil

AU - Shon, Min Han

AU - Kim, Dongsoo Stephen

AU - Choo, Hyunseung

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - One of the most important issues in Wireless Sensor Networks (WSNs) is the efficient use of limited energy resources. A popular approach for efficient energy consumption is clustering. In this paper, we propose an energy efficient clustering algorithm, called Bird Flocking Behavior Clustering (BFBC). By adopting the bird flocking behavior, our clustering algorithm forms clusters with simple local interactions. With an improvement on the existing bio-inspired clustering algorithm, that forms a cluster using several messages, BFBC forms a cluster with only one message. Simulation results show that BFBC significantly decreases the number of messages for cluster head election, and also reduces the energy consumption for communication between cluster members and their dedicated cluster head.

AB - One of the most important issues in Wireless Sensor Networks (WSNs) is the efficient use of limited energy resources. A popular approach for efficient energy consumption is clustering. In this paper, we propose an energy efficient clustering algorithm, called Bird Flocking Behavior Clustering (BFBC). By adopting the bird flocking behavior, our clustering algorithm forms clusters with simple local interactions. With an improvement on the existing bio-inspired clustering algorithm, that forms a cluster using several messages, BFBC forms a cluster with only one message. Simulation results show that BFBC significantly decreases the number of messages for cluster head election, and also reduces the energy consumption for communication between cluster members and their dedicated cluster head.

KW - Bio-inspired

KW - Bird flocking behaviour

KW - Clustering

KW - Collective behaviour

KW - RSSI

KW - Swarm Intelligence

KW - Wireless Sensor Networks

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U2 - 10.1007/978-3-319-21410-8_10

DO - 10.1007/978-3-319-21410-8_10

M3 - Conference contribution

AN - SCOPUS:84948967955

SN - 9783319214092

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 128

EP - 137

BT - Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings

A2 - Gavrilova, Marina L.

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Misra, Sanjay

A2 - Torre, Carmelo

A2 - Taniar, David

A2 - Apduhan, Bernady O.

A2 - Rocha, Ana Maria A.C.

A2 - Misra, Sanjay

PB - Springer Verlag

T2 - 15th International Conference on Computational Science and Its Applications, ICCSA 2015

Y2 - 22 June 2015 through 25 June 2015

ER -

Jung SG, Yeom S, Shon MH, Kim DS, Choo H. Clustering wireless sensor networks based on bird flocking behavior. In Gavrilova ML, Gervasi O, Murgante B, Misra S, Torre C, Taniar D, Apduhan BO, Rocha AMAC, Misra S, editors, Computational Science and Its Applications - ICCSA 2015 - 15th International Conference, Proceedings. Springer Verlag. 2015. p. 128-137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-21410-8_10

Clustering wireless sensor networks based on bird flocking behavior

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