A bio-inspired algorithm for performance optimization in wireless sensor networks

C.T. Cheng; Chi Kong Tse; Chung Ming Lau

A bio-inspired algorithm for performance optimization in wireless sensor networks

C.T. Cheng, Chi Kong Tse, Chung Ming Lau

Research output: Unpublished conference presentation (presented paper, abstract, poster) › Conference presentation (not published in journal/proceeding/book) › Academic research › peer-review

Abstract

A wireless sensor network typically comprises a number of inexpensive power constrained sensors which collect data from the sensing environment and transmit them towards the base station in a coordinated way. Employing techniques of clustering and election of cluster heads can increase the transmission efficiency and prolong the network lifetime. This paper proposes a bio-inspired de-centralized clustering algorithm for wireless sensor networks. The clustering algorithm is evaluated assuming a first-order radio model. Simulation results show that the proposed algorithm brings a 16 % to 161 % improvement over other de-centralized clustering algorithms in terms of network lifetime. Simulation results also show that the proposed de-centralized clustering algorithm has a similar performance as the centralized clustering algorithm.

Original language	English
Pages	309-312
Number of pages	4
Publication status	Published - 2007
Event	International Symposium on Nonlinear Theory and Its Applications [NOLTA] - Duration: 1 Jan 2007 → …

Conference

Conference	International Symposium on Nonlinear Theory and Its Applications [NOLTA]
Period	1/01/07 → …

Keywords

Bio-inspired algorithm
Clustering
Decentralized control
Wireless sensor networks

Cite this

@conference{b999e1002eba41a39a6e5c7c2a040b34,

title = "A bio-inspired algorithm for performance optimization in wireless sensor networks",

abstract = "A wireless sensor network typically comprises a number of inexpensive power constrained sensors which collect data from the sensing environment and transmit them towards the base station in a coordinated way. Employing techniques of clustering and election of cluster heads can increase the transmission efficiency and prolong the network lifetime. This paper proposes a bio-inspired de-centralized clustering algorithm for wireless sensor networks. The clustering algorithm is evaluated assuming a first-order radio model. Simulation results show that the proposed algorithm brings a 16 % to 161 % improvement over other de-centralized clustering algorithms in terms of network lifetime. Simulation results also show that the proposed de-centralized clustering algorithm has a similar performance as the centralized clustering algorithm.",

keywords = "Bio-inspired algorithm, Clustering, Decentralized control, Wireless sensor networks",

author = "C.T. Cheng and Tse, {Chi Kong} and Lau, {Chung Ming}",

year = "2007",

language = "English",

pages = "309--312",

note = "International Symposium on Nonlinear Theory and Its Applications [NOLTA] ; Conference date: 01-01-2007",

}

A bio-inspired algorithm for performance optimization in wireless sensor networks. / Cheng, C.T.; Tse, Chi Kong; Lau, Chung Ming.

2007. 309-312 Paper presented at International Symposium on Nonlinear Theory and Its Applications [NOLTA].

Research output: Unpublished conference presentation (presented paper, abstract, poster) › Conference presentation (not published in journal/proceeding/book) › Academic research › peer-review

TY - CONF

T1 - A bio-inspired algorithm for performance optimization in wireless sensor networks

AU - Cheng, C.T.

AU - Tse, Chi Kong

AU - Lau, Chung Ming

PY - 2007

Y1 - 2007

N2 - A wireless sensor network typically comprises a number of inexpensive power constrained sensors which collect data from the sensing environment and transmit them towards the base station in a coordinated way. Employing techniques of clustering and election of cluster heads can increase the transmission efficiency and prolong the network lifetime. This paper proposes a bio-inspired de-centralized clustering algorithm for wireless sensor networks. The clustering algorithm is evaluated assuming a first-order radio model. Simulation results show that the proposed algorithm brings a 16 % to 161 % improvement over other de-centralized clustering algorithms in terms of network lifetime. Simulation results also show that the proposed de-centralized clustering algorithm has a similar performance as the centralized clustering algorithm.

AB - A wireless sensor network typically comprises a number of inexpensive power constrained sensors which collect data from the sensing environment and transmit them towards the base station in a coordinated way. Employing techniques of clustering and election of cluster heads can increase the transmission efficiency and prolong the network lifetime. This paper proposes a bio-inspired de-centralized clustering algorithm for wireless sensor networks. The clustering algorithm is evaluated assuming a first-order radio model. Simulation results show that the proposed algorithm brings a 16 % to 161 % improvement over other de-centralized clustering algorithms in terms of network lifetime. Simulation results also show that the proposed de-centralized clustering algorithm has a similar performance as the centralized clustering algorithm.

KW - Bio-inspired algorithm

KW - Clustering

KW - Decentralized control

KW - Wireless sensor networks

M3 - Conference presentation (not published in journal/proceeding/book)

SP - 309

EP - 312

T2 - International Symposium on Nonlinear Theory and Its Applications [NOLTA]

Y2 - 1 January 2007

ER -

A bio-inspired algorithm for performance optimization in wireless sensor networks

Abstract

Conference

Keywords

Fingerprint

Cite this