research-article

A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks

Authors:

Wen-Hwa Liao,

Yucheng Kao,

Ying-Shan LiAuthors Info & Claims

Volume 38, Issue 10

Pages 12180 - 12188

https://doi.org/10.1016/j.eswa.2011.03.053

Published: 15 September 2011 Publication History

Graphical Abstract

Highlights

► We present a sensor deployment scheme based on glowworm swarm optimization (GSO) to enhance the coverage after an initial random deployment of the sensors. ► A sensor node is attracted towards its neighbors having lower intensity of luciferin and decides to move towards one of them. ► The coverage of the sensing filed is maximized as the sensor nodes tend to move towards the region having lower sensor density.

Abstract

A wireless sensor network is composed of a large number of sensor nodes that are densely deployed in a sensing environment. The effectiveness of the wireless sensor networks depends to a large extent on the coverage provided by the sensor deployment scheme. In this paper, we present a sensor deployment scheme based on glowworm swarm optimization (GSO) to enhance the coverage after an initial random deployment of the sensors. Each sensor node is considered as individual glowworms emitting a luminant substance called luciferin and the intensity of the luciferin is dependent on the distance between the sensor node and its neighboring sensors. A sensor node is attracted towards its neighbors having lower intensity of luciferin and decides to move towards one of them. In this way, the coverage of the sensing field is maximized as the sensor nodes tend to move towards the region having lower sensor density. Simulation results show that our GSO-based sensor deployment approach can provide high coverage with limited movement of the sensor nodes.

References

[1]

I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine 40 (8) (2002) 102–114.

Google Scholar

[2]

M. Dorigo, L.M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation 1 (1) (1997) 53–66.

Digital Library

Google Scholar

[3]

M. Dorigo, V. Maniezzo, A. Colorni, The ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics – Part B 26 (1) (1996) 29–41.

Google Scholar

[4]

C.-F. Huang, Y.-C. Tseng, The coverage problem in a wireless sensor network, ACM Mobile Networks and Applications 10 (4) (2005) 519–528.

Google Scholar

[5]

K.N. Krishnanand, D. Ghose, Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions, Swarm Intelligence 3 (2) (2009) 87–124.

Google Scholar

[6]

W.-H. Liao, Y.-C. Kao, C.-M. Fan, Data aggregation in wireless sensor networks using ant colony algorithm, Journal of Network and Computer Applications 31 (4) (2008) 387–401.

Google Scholar

[7]

Meguerdichian, S., Koushanfar, F., Potkonjak, M., & Srivastava, M. B. (2001). Coverage problems in wireless ad-hoc sensor networks. In IEEE INFOCOM.

Google Scholar

[8]

S. Meguerdichian, F. Koushanfar, M. Potkonjak, M.B. Srivastava, Worst and best-case coverage in sensor networks, IEEE Transactions on Mobile Computing 4 (1) (2005) 84–92.

Google Scholar

[9]

Poduri, S., & Sukhatme, G. S. (2004). Constrained coverage for mobile sensor networks. In IEEE international conference on robotics and automation (ICRA) (pp. 165–172).

Google Scholar

[10]

Wang, Y.-C., Hu, C.-C., & Tseng, Y.-C. (2005). Efficient deployment algorithms for ensuring coverage and connectivity of wireless sensor networks. In IEEE international conference on wireless internet (WICON) (pp. 114–121).

Google Scholar

[11]

J. Yick, B. Mukherjee, D. Ghosal, Wireless sensor network survey, Computer Networks 52 (12) (2008) 2292–2330.

Google Scholar

[12]

H. Zhang, J.C. Hou, Maintaining sensing coverage and connectivity in large sensor networks, Ad Hoc and Sensor Wireless Networks 1 (1–2) (2005) 89–124.

Google Scholar

[13]

Zou, Y., & Chakrabarty, K. (2003). Sensor deployment and target localization based on virtual forces. In IEEE INFOCOM.

Google Scholar

Cited By

View all

Gao HZhang QBu XZhang H(2024)Quadruple parameter adaptation growth optimizer with integrated distribution, confrontation, and balance features for optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121218235:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121218
Houssein ESaad MDjenouri YHu GAli AShaban H(2024)Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challengesCluster Computing10.1007/s10586-024-04619-927:10(13643-13673)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04619-9
Mohar SGoyal SKaur R(2023)Exploration of different topologies for optimal sensor nodes deployment in wireless sensor networks using jaya-sine cosine optimization algorithmThe Journal of Supercomputing10.1007/s11227-023-05147-w79:12(13001-13030)Online publication date: 21-Mar-2023
https://dl.acm.org/doi/10.1007/s11227-023-05147-w
Show More Cited By

Index Terms

A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Ant colony optimization based sensor deployment protocol for wireless sensor networks

Research highlights We consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. We model the deployment problem as the multiple knapsack problem. Based on ACO algorithm, we ...
Movement-assisted sensor redeployment scheme for network lifetime increase
MSWiM '07: Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems

Sensor deployment in mobile sensor networks has received significant attention in recent years. Goals during sensor deployment include improving coverage, achieving load balance, and prolonging the network lifetime. To improve the initial deployment, ...
Maximizing the wireless sensor networks lifetime through energy efficient connected coverage

Wireless Sensor Network (WSN) is an emerging technology that is gaining much importance owing to its immense contribution in various day-to-day applications. A sensor is battery-operated, unattended low-cost device with limited computing, communication ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 38, Issue 10

Sep 2011

1499 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 September 2011

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gao HZhang QBu XZhang H(2024)Quadruple parameter adaptation growth optimizer with integrated distribution, confrontation, and balance features for optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121218235:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121218
Houssein ESaad MDjenouri YHu GAli AShaban H(2024)Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challengesCluster Computing10.1007/s10586-024-04619-927:10(13643-13673)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04619-9
Mohar SGoyal SKaur R(2023)Exploration of different topologies for optimal sensor nodes deployment in wireless sensor networks using jaya-sine cosine optimization algorithmThe Journal of Supercomputing10.1007/s11227-023-05147-w79:12(13001-13030)Online publication date: 21-Mar-2023
https://dl.acm.org/doi/10.1007/s11227-023-05147-w
Singh SMittal NSingh HOliva D(2023)Improving the segmentation of digital images by using a modified Otsu’s between-class varianceMultimedia Tools and Applications10.1007/s11042-023-15129-y82:26(40701-40743)Online publication date: 31-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-15129-y
Cullen ARubinstein BKandeepan SFlower BLeong P(2023)Predicting dynamic spectrum allocation: a review covering simulation, modelling, and predictionArtificial Intelligence Review10.1007/s10462-023-10449-956:10(10921-10959)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-023-10449-9
Wang ZTian LWu WLin LLi ZTong Y(2022)A Metaheuristic Algorithm for Coverage Enhancement of Wireless Sensor NetworksWireless Communications & Mobile Computing10.1155/2022/77329892022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7732989
Srinivas MPatnaik M(2022)Clustering with a high-performance secure routing protocol for mobile ad hoc networksThe Journal of Supercomputing10.1007/s11227-021-04258-678:6(8830-8851)Online publication date: 13-Jan-2022
https://dl.acm.org/doi/10.1007/s11227-021-04258-6
Ghassemi PBalazon MChowdhury S(2022)A penalized batch-Bayesian approach to informative path planning for decentralized swarm robotic searchAutonomous Robots10.1007/s10514-022-10047-846:6(725-747)Online publication date: 25-Jun-2022
https://dl.acm.org/doi/10.1007/s10514-022-10047-8
Nematzadeh STorkamanian-Afshar MSeyyedabbasi AKiani F(2022)Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: an efficient metaheuristic-based method for environment-aware node deploymentNeural Computing and Applications10.1007/s00521-022-07786-135:1(611-641)Online publication date: 18-Sep-2022
https://dl.acm.org/doi/10.1007/s00521-022-07786-1
Jiao J(2021)Mobile Industrial Robots Localization Algorithm Based on Improved Multidimensional Scale and Received Signal Strength IndicationAdvances in Multimedia10.1155/2021/24372242021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2437224
Show More Cited By

Graphical Abstract

Highlights

Abstract

References

Cited By

Index Terms

Recommendations

Ant colony optimization based sensor deployment protocol for wireless sensor networks

Movement-assisted sensor redeployment scheme for network lifetime increase

Maximizing the wireless sensor networks lifetime through energy efficient connected coverage

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations