research-article

Energy-efficient and location-aware ant colony based routing algorithms for wireless sensor networks

Authors:

Christian Dominguez,

Nareli Cruz-CortésAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 117 - 124

https://doi.org/10.1145/2001576.2001593

Published: 12 July 2011 Publication History

Abstract

In recent years, advances in miniaturization, low-power circuit design, simple, low power, yet reasonably efficient wireless communication equipment, and improved small-scale energy supplies have combined with reduced manufacturing costs to make a new technological vision possible, Wireless Sensor Networks (WSN). As WSN are still a young research field, much activity is still on-going to solve many open issues. One is the data routing problem. As the size of the network increases, this problem becomes more complex due the amount of sensor nodes in the network. The meta-heuristic Ant Colony Optimization (ACO) has been proposed to solve this issue. ACO based routing algorithms can add a significant contribution to assist in the maximisation of the network lifetime and in the minimisation of the latency in data transmissions, but this is only possible by means of an adaptable and balanced algorithm that takes into account the WSN main restrictions, for example, memory and power supply. A comparison of two ACO based routing algorithms for WSN is presented, taking into account current amounts of energy consumption under a WSN scenario proposed in this work. Furthermore, a new routing algorithm is defined.

References

[1]

J. N. Al-Karaki and E. Kamal-Ahmed. Routing Techniques in Wireless Sensor Networks A Survey. Wireless Communications, IEEE, 11(6):6--28, Dec. 2004.

Digital Library

[2]

I. Bouazizi. ARA - The Ant-Colony Based Routing Algorithm for MANETs. In Proceedings of the 2002 International Conference on Parallel Processing Workshops, pages 79--85, Washington, DC, USA, 2002. IEEE Computer Society.

Digital Library

[3]

D. Braginsky and D. Estrin. Rumor routing algorithm for sensor networks. 1st Wksp. Sensor Networks and Apps., Oct. 2002.

Digital Library

[4]

W. Cai, X. Jin, Y. Zhang, K. Chen, and R. Wang. ACO Based QoS Routing Algorithm for Wireless Sensor Networks. Springer-Verlag, LNCS, 4159:419--428, 2006.

Digital Library

[5]

T. Camilo, C. Carreto, J. S. Silva, and F. Boavida. An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks. Springer-Verlag LNCS, 4150:49--59, 2006.

Digital Library

[6]

M. Dorigo and G. DiCaro. Ant Net: A Mobile Agents Approach to Adaptive Routing Technical. IRIDIA Free Brussels University, Belgium, 1997.

[7]

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

Digital Library

[8]

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

Digital Library

[9]

M. Dorigo, V. Maniezzo, and A. Colorni. The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics--Part B, 26(1):1--13, 1996.

Digital Library

[10]

M. Farooq and G. A. Caro. Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies An Overview. Swarm Intelligence, pages 101--160, 2008.

[11]

T. Heimfarth and P. Janacik. Experiments with Biologically-Inspired Methods for Service Assignment in Wireless Sensor Networks. Biologically-Inspired Collaborative Computing, 268:71--84, 2008.

[12]

W. Heinzelman and H. Balakrishnan. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. Proceedings of the 33rd Hawaii International Conference on System Sciences, 2000.

Digital Library

[13]

C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: a scalable and robust communication paradigm for sensor networks. MOBICOM, 2000.

Digital Library

[14]

S. Iyengar, H. Wu, N. Balakrishnan, and S. Chang. Biologically Inspired Cooperative Routing for Wireless Mobile Sensor Networks. IEEE SYSTEMS, 1(1):29--37, Sept. 2007.

[15]

S. Lindsey and C. Raghavendra. Data Gathering Algorithms in Sensor Networks Using Energy Metrics. IEEE Aerospace Conference Proceedings, Vol. 3(9--16):1125--1130, 2002.

[16]

C. Liu, L. Li, and Y. Xiang. Research of Multi-Path Routing Protocol Based on Parallel Ant Colony Algorithm Optimization in Mobile Ad Hoc Networks. In Proceedings of the Fifth International Conference on Information Technology: New Generations, pages 1006--1010, Washington, DC, USA, 2008. IEEE Computer Society.

Digital Library

[17]

S. Okdem and D. Karaboga. Routing in Wireless Sensor Networks Using an Ant Colony Optimization ACO Router Chip. Sensors, pages 909--921, 2009.

[18]

G. Reza, A. Rahman, W. Gueaieb, and A. Saddik. Ant Colony-Based Reinforcement Learning Algorithm for Routing in Wireless Sensor Networks. IEEE, pages 1--6, 2007.

[19]

K. Saleem, N. Fisal, M. Baharudin, A. Ahmed, S. Hafizah, and S. Kamilah. Ant Colony inspired Self-Optimized Routing Protocol based on Cross Layer Architecture for Wireless Sensor Networks. WSEAS Transactions on Communications, 9(10):669--678, Oct. 2010.

Digital Library

[20]

M. G. Torres. Energy Consumption in Wireless Sensor Networks Usig GSP. Master's thesis, Universidad Pontificia Bolivariana, Medellín, Colombia, 2006.

[21]

X. Wang, Q. Li, N. Xiong, and Y. Pan. Ant Colony Optimization-Based Location-Aware Routing for Wireless Sensor Networks. Springer-Verlag, LNCS, 5258:109--120, 2008.

Digital Library

[22]

Y. Wen, Y. Chen, and D. Qian. An Ant-based approach to Power-Efficient Algorithm for Wireless Sensor Networks. WCE, pages 1546--1550, 2007.

[23]

Y. Xu, J. Heidemann, and D. Estrin. Geography-Informed Energy Conservation for Ad hoc Routing. MOBICOM, July 2001.

Digital Library

[24]

J. Yang, M. Xu, W. Zhao, and B. Xu. A Multipath Routing Protocol Based on Clustering and ACO for WSN. Sensors 2010, 10:4521--4540, May 2010.

[25]

N. Ye, J. Shao, R. Wang, and Z. Wang. Colony Algorithm for Wireless Sensor Networks Adaptive Data Aggregation Routing Schema. Springer-Verlag, LNCS, 4688:248--257, 2007.

Digital Library

[26]

Y. Yu, D. Estrin, and R. Govindan. Geographical and energy-aware routing: A recursive data dissemination protocol for wireless sensor networks. Technical report, UCLA Comp. Sci. Dept., May 2001.

[27]

X. Zhu. Pheromone Based Energy Aware Directed Diffusion Algorithm for Wireless Sensor Network. Springer-Verlag, LNCS, 4681:283--291, 2007.

Digital Library

Cited By

Kaushik CVibhath JNagamani LReddy PReddy P(2022)Energy Optimization and Optimal Routing Protocol in Wireless Sensor Networks2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA54612.2022.9985549(484-489)Online publication date: 21-Sep-2022
https://doi.org/10.1109/ICIRCA54612.2022.9985549
Chavan SKulkarni A(2018)Event Based Clustering Localized Energy Efficient Ant Colony Optimization (EBC_LEE-ACO) for Performance Enhancement of Wireless Sensor NetworkEngineering, Technology & Applied Science Research10.48084/etasr.21218:4(3177-3183)Online publication date: 18-Aug-2018
https://doi.org/10.48084/etasr.2121
Woungang IDhurandher SObaidat M(2013)Using Ant Colony Agentsfor Designing Energy-Efficient Protocols for Wireless Ad Hoc and Sensor NetworksHandbook of Green Information and Communication Systems10.1016/B978-0-12-415844-3.00024-3(611-629)Online publication date: 2013
https://doi.org/10.1016/B978-0-12-415844-3.00024-3
Show More Cited By

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Energy-efficient and location-aware ant colony based routing algorithms for wireless sensor networks

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Kaushik CVibhath JNagamani LReddy PReddy P(2022)Energy Optimization and Optimal Routing Protocol in Wireless Sensor Networks2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA54612.2022.9985549(484-489)Online publication date: 21-Sep-2022
https://doi.org/10.1109/ICIRCA54612.2022.9985549
Chavan SKulkarni A(2018)Event Based Clustering Localized Energy Efficient Ant Colony Optimization (EBC_LEE-ACO) for Performance Enhancement of Wireless Sensor NetworkEngineering, Technology & Applied Science Research10.48084/etasr.21218:4(3177-3183)Online publication date: 18-Aug-2018
https://doi.org/10.48084/etasr.2121
Woungang IDhurandher SObaidat M(2013)Using Ant Colony Agentsfor Designing Energy-Efficient Protocols for Wireless Ad Hoc and Sensor NetworksHandbook of Green Information and Communication Systems10.1016/B978-0-12-415844-3.00024-3(611-629)Online publication date: 2013
https://doi.org/10.1016/B978-0-12-415844-3.00024-3
Rothenpieler PPfisterer D(2012)Towards Distributed Protocol Stacks for Wireless Sensor NetworksProceedings of the 2012 IEEE International Conference on Green Computing and Communications10.1109/GreenCom.2012.148(418-425)Online publication date: 20-Nov-2012
https://dl.acm.org/doi/10.1109/GreenCom.2012.148

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