JACIII Vol.18 No.5 pp. 769-775
doi: 10.20965/jaciii.2014.p0769


Swarm Robot System for Underwater Communication Network

Ryan Rhay P. Vicerra, Elmer P. Dadios, Argel A. Bandala,
and Laurence A. Gan Lim

De La Salle University, 2401 Taft Ave., Manila, 1004 Philippines

February 5, 2014
May 4, 2014
September 20, 2014
swarm intelligence, underwater communication simulation, underwater swarm robot system, swarm robotics
This paper presents a swarm robot simulator for implementing underwater wireless communication network. Swarm intelligence is based on the collective behavior of social insects and animals such as ants, bees and others. In this paper, swarm was applied to overcome the challenges of transmitting data in a large underwater environment. A robot considered to be a member of the swarm acts as a simple “physical” carrier of the data, it moves until they converge and manage to form a link connecting the data transmitter and receiver. The system is developed, simulated and tested using a coded simulator.
Cite this article as:
R. Vicerra, E. Dadios, A. Bandala, and L. Lim, “Swarm Robot System for Underwater Communication Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 769-775, 2014.
Data files:
  1. [1] G. Beni and J. Wang, “Swarm Intelligence,” 7th Annual Meeting of the Robotics Society of Japan, 1989.
  2. [2] G. Beni, “From Swarm Intelligence to Swarm Robotics,” Swarm Robotics: State-of-the-art Survey, Lecture Notes in Computer Science 3342, Springer-Verlag, pp. 1-9, 2005.
  3. [3] C. Grosan, A. Abraham, and C. Monica, “Swarm Intelligence in Data Mining,” Swarm Intelligence in Data Mining, Vol.34, pp. 1-16, Springer, 2006.
  4. [4] T. Seeley, “The Wisdom of the Hive,” Harward University Press, 1996.
  5. [5] D. Teodorovic and M. Dell’orco, “Bee Colony Optimization-A Cooperative Learning Approach to Complex Transportation Problems,” Advanced OR and AIMethods in Transportation,” pp. 51-60, 2005.
  6. [6] A. Colorni, M. Dorigo, and V.Maniezzo, “Distributed Optimization by Ant Colonies,” Proc. of the First European Conf. on Artifical Life, pp. 134-142, MIT Press, 1992.
  7. [7] M. Dorigo, V. Maniezzo, and A. Colorni, “The Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.26, pp. 29-41, 1996.
  8. [8] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Networks, Vol.4, pp. 1942-1948, 1995.
  9. [9] Y. del Valle, G. Venayagamoorthy, S. Mohaghenghi, J. Hernandez, and R. Harley, “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems,” IEEE Trans. on Evolutionary Computation, Vol.12, pp. 171-195, 2008.
  10. [10] K. Passino, “Distributed Optimization and Control Using Only a Germ of Intelligence,” Proc. of the 2000 IEEE Int. Symp. on Intelligent Control, pp. 5-13, 2000.
  11. [11] K. Passino, “Biomimicry of Bacteria Foraging for Distributed Optimization and Control,” IEEE Control Systems Magazine, Vol.22, pp. 52-67, 2002.
  12. [12] G. Venayagamoorthy and R. Harley, “Swarm Intelligence for Transmission System Control,” IEEE Power Engineering Society General Meeting, pp. 1-4, 2007.
  13. [13] J. Kennedy and R. Eberhart, “Swarm Intelligence,” Morgan Kaufmann, 2001.
  14. [14] M. M. Millonas, “Swarms, Phase Transitions, and Collective Intelligence,” Artificial Life III, pp. 417-445, Addison-Wesley, 1994.
  15. [15] E. Bonabeau, G. Theraulaz, and M. Dorigo, “Swarm Intelligence: From Natural to Artificial Systems,” Oxford Unversity Press, 1999.
  16. [16] J. K. Eberhart and R. Eberhart, “Swarm Intelligence,” Morgan Kaufmann, 2001.
  17. [17] T. Schmickl, et. al., “CoCoRo – The Self-aware Underwater Swarm,” SASO 2011: 5th IEEE Conf.on Self-Adaptive and Self-Organizing Systems Workshops, 2011.
  18. [18] E. Sahin, “Swarm Robotics: From Sources of Inspiration to Domains of Applocation,” Swarm Robotics: State-of-the-art Survey, Lecture Notes in Computer Science, Vol.3342, pp. 10-20, Springer-Verlag, 2005.
  19. [19] S. Camazine, J. Deneubourg, N. Franks, J. Sneyd, G. Theraulaz, and E. Bonabeau, “Self-Organisation in Biological Systems,” Princeton University Press, 2001.
  20. [20] C. Osterloh and E. Maehle, “Low-Power Microcontroller-based Acoustic Modem for Underwater Robot Communication,” 2010 41st Int. Symp. and 2010 6th German Conf. on Robotics (ROBOTIK), 2010.
  21. [21] J. Heidemann, M. Stojanovic, and M. Zorzi, “Underwater sensor networks: applications, advances and challenges,” Philisophical Trans. of the Royal Society A, Vol.370, No.1958, pp. 158-175, 2012.
  22. [22] S. Z. a. C. J.-H. L Lanbo, “Prospects and problems of wireless communication for underwater sensor network,” Wireless Communication and Mobile Computing archive, Vol.8, No.8, pp. 977-994.
  23. [23] U. M. Cella, R. Johnstone, and N. Shuley, “Electromagnetic wave wireless communication in shallow water coastal environment: theoretical analysis and experimental results,” 4th ACM Int.Workshop on Underwater Networks (WUWNet), 2009.
  24. [24] N. Farr, A. Bowen, J. Ware, C. Pontbriand and M. Tivey, “An integrated, underwater optical/acoustic communications system,” OCEANS 2010 IEEE - Sydney, pp. 1-6, 2010.
  25. [25] I. Vasilescu, K. Kotay, D. Rus, M. Dunbabin and P. Corke, “Data collection, storage, and retrieval with an underwater sensor network,” Proc. 3rd ACM SenSys Conf., 2005.
  26. [26] J. Friedman, D. Torres, T. Schmid, J. Dong, and M. B. Srivastava, “A biomimetic quasistatic electric field physical channel for underwater ocean networks,” 5th ACM Int. Workshop on Underwater Network (WUWNET), 2010.
  27. [27] V. Gazi and K. M. Passino, “Swarm Stability and Optimization,” Springer, 2011.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024