Krill herd based clustering algorithm for wireless sensor networks

Md Shopon; Md Akhtaruzzaman Adnan; Md Firoz Mridha

doi:10.1109/IWCI.2016.7860346

Krill herd based clustering algorithm for wireless sensor networks

Md Shopon, Md Akhtaruzzaman Adnan, Md Firoz Mridha

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

11 Citations (Scopus)

Abstract

Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a centralized approach that deals with energy-awareness of wireless sensor networks using the Krill Herd algorithm. The performance of the suggested algorithm is assessed with famous clustering protocols. The simulation results show that suggested approach can maximize sensor network lifetime over other algorithms of the same category.

Original language	English
Title of host publication	2016 International Workshop on Computational Intelligence (IWCI)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	96-100
Number of pages	5
ISBN (Electronic)	9781509057696
DOIs	https://doi.org/10.1109/IWCI.2016.7860346
Publication status	Published - 13 Dec 2016
Externally published	Yes
Event	International Workshop on Computational Intelligence (IWCI 2016) - Dhaka, Bangladesh Duration: 12 Dec 2016 → 13 Dec 2016

Conference

Conference	International Workshop on Computational Intelligence (IWCI 2016)
Country/Territory	Bangladesh
City	Dhaka
Period	12/12/16 → 13/12/16

Keywords

Clustering algorithm
Krill Herd
Network lifetime
Optimization
Swarm intelligence
Wireless sensor networks

Access to Document

10.1109/IWCI.2016.7860346

Cite this

@inproceedings{944b63357cf3408285bc83a75f409e7a,

title = "Krill herd based clustering algorithm for wireless sensor networks",

abstract = "Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a centralized approach that deals with energy-awareness of wireless sensor networks using the Krill Herd algorithm. The performance of the suggested algorithm is assessed with famous clustering protocols. The simulation results show that suggested approach can maximize sensor network lifetime over other algorithms of the same category.",

keywords = "Clustering algorithm, Krill Herd, Network lifetime, Optimization, Swarm intelligence, Wireless sensor networks",

author = "Md Shopon and Adnan, \{Md Akhtaruzzaman\} and Mridha, \{Md Firoz\}",

year = "2016",

month = dec,

day = "13",

doi = "10.1109/IWCI.2016.7860346",

language = "English",

pages = "96--100",

booktitle = "2016 International Workshop on Computational Intelligence (IWCI)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "International Workshop on Computational Intelligence (IWCI 2016) ; Conference date: 12-12-2016 Through 13-12-2016",

}

Shopon, M, Adnan, MA & Mridha, MF 2016, Krill herd based clustering algorithm for wireless sensor networks. in 2016 International Workshop on Computational Intelligence (IWCI). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 96-100, International Workshop on Computational Intelligence (IWCI 2016), Dhaka, Bangladesh, 12/12/16. https://doi.org/10.1109/IWCI.2016.7860346

Krill herd based clustering algorithm for wireless sensor networks. / Shopon, Md; Adnan, Md Akhtaruzzaman; Mridha, Md Firoz.
2016 International Workshop on Computational Intelligence (IWCI). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 96-100.

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

TY - GEN

T1 - Krill herd based clustering algorithm for wireless sensor networks

AU - Shopon, Md

AU - Adnan, Md Akhtaruzzaman

AU - Mridha, Md Firoz

PY - 2016/12/13

Y1 - 2016/12/13

N2 - Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a centralized approach that deals with energy-awareness of wireless sensor networks using the Krill Herd algorithm. The performance of the suggested algorithm is assessed with famous clustering protocols. The simulation results show that suggested approach can maximize sensor network lifetime over other algorithms of the same category.

AB - Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a centralized approach that deals with energy-awareness of wireless sensor networks using the Krill Herd algorithm. The performance of the suggested algorithm is assessed with famous clustering protocols. The simulation results show that suggested approach can maximize sensor network lifetime over other algorithms of the same category.

KW - Clustering algorithm

KW - Krill Herd

KW - Network lifetime

KW - Optimization

KW - Swarm intelligence

KW - Wireless sensor networks

UR - https://www.scopus.com/pages/publications/85015813713

U2 - 10.1109/IWCI.2016.7860346

DO - 10.1109/IWCI.2016.7860346

M3 - Conference proceeding contribution

SP - 96

EP - 100

BT - 2016 International Workshop on Computational Intelligence (IWCI)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - International Workshop on Computational Intelligence (IWCI 2016)

Y2 - 12 December 2016 through 13 December 2016

ER -

Krill herd based clustering algorithm for wireless sensor networks

Abstract

Conference

Keywords

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