skip to main content
10.1145/2001576.2001593acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

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

Published:12 July 2011Publication 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Braginsky and D. Estrin. Rumor routing algorithm for sensor networks. 1st Wksp. Sensor Networks and Apps., Oct. 2002. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Dorigo and G. DiCaro. Ant Net: A Mobile Agents Approach to Adaptive Routing Technical. IRIDIA Free Brussels University, Belgium, 1997.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. C. Intanagonwiwat, R. Govindan, and D. Estrin. Directed diffusion: a scalable and robust communication paradigm for sensor networks. MOBICOM, 2000. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. G. Torres. Energy Consumption in Wireless Sensor Networks Usig GSP. Master's thesis, Universidad Pontificia Bolivariana, Medellín, Colombia, 2006.Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  23. Y. Xu, J. Heidemann, and D. Estrin. Geography-Informed Energy Conservation for Ad hoc Routing. MOBICOM, July 2001. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle ScholarCross RefCross Ref
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle Scholar
  27. X. Zhu. Pheromone Based Energy Aware Directed Diffusion Algorithm for Wireless Sensor Network. Springer-Verlag, LNCS, 4681:283--291, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

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

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            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

            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]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 12 July 2011

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            Overall Acceptance Rate1,669of4,410submissions,38%

            Upcoming Conference

            GECCO '24
            Genetic and Evolutionary Computation Conference
            July 14 - 18, 2024
            Melbourne , VIC , Australia

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader