A Bio-inspired Approach to Self-organization of Mobile Nodes in Real-Time Mobile Ad Hoc Network Applications

  • Cem Şafak Şahin
  • Elkin Urrea
  • M. Ümit Uyar
  • Stephen Gundry


In this chapter, we study the applicability and effectiveness of an evolutionary computation approach to a topology control problem in the domain of mobile ad hoc networks (manets). We present formal and practical aspects of convergence properties of our force-based genetic algorithm, called fga, which is run by each mobile node to achieve a uniform spread. Our fga is suitable for manet environments since mobile nodes, while running the fga, only use local neighborhood information. An inhomogeneous Markov chain is used to analyze the convergence speed of our bio-inspired algorithm. To demonstrate our topology control algorithm’s applicability to real-life problems and to evaluate its effectiveness, we have implemented a simulation software system and two testbed platforms. The simulation and testbed experiment results indicate that, for important performance metrics such as the normalized area coverage and convergence rate, the fga can be an effective mechanism to deploy mobile nodes with restrained communication capabilities in manets operating in unknown areas. Since the fga adapts to the local environment rapidly and does not require global network knowledge, it can be used as a real-time topology controller for realistic military and civilian applications.


Virtual Machine Mobile Node Mobile Agent Mobility Model Communication Range 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Boston (1998)zbMATHGoogle Scholar
  2. 2.
    Yuret, D., de la Maza, M.: Dynamic hill climbing: Overcoming the limitations of optimization techniques. In: The Second Turkish Symposium on Artificial Intelligence and Neural Networks, pp. 208–212 (1993)Google Scholar
  3. 3.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)zbMATHCrossRefGoogle Scholar
  4. 4.
    Holland, J.H.: Evolutionary swarm robotics: Evolving Self-organizing Behaviors in groups of Autonomous Groups. University of Michigan Press, Ann Arbor (1975)Google Scholar
  5. 5.
    Bekey, G., Agah, A.: A genetic algorithm-based controller for decentralized multi-agent robotic systems. In: Proc. of the IEEE International Conference of Evolutionary Computing, pp. 431–436 (1996)Google Scholar
  6. 6.
    Miryazdi, H.R., Khaloozadeh, H.: Application of genetic algorithm to decentralized control of robot manipulators. In: ICAIS 2002: Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002), p. 334. IEEE Computer Society, Washington, DC, USA (2002)CrossRefGoogle Scholar
  7. 7.
    Ping-An, G., Zi-Xing, C., Ling-Li, Y.: Evolutionary computation approach to decentralized multi-robot task allocation. In: International Conference on Natural Computation, vol. 5, pp. 415–419 (2009)Google Scholar
  8. 8.
    Song, P., Li, J., Li, K., Sui, L.: Researching on optimal distribution of mobile nodes in wireless sensor networks being deployed randomly. In: International Conference on Computer Science and Information Technology, pp. 322–326 (2008)Google Scholar
  9. 9.
    Heo, N.: An intelligent deployment and clustering algorithm for a distributed mobile sensor network. In: Proceedings of the IEEE International Conference on Systems Man And Cybernetics, pp. 4576–4581 (2003)Google Scholar
  10. 10.
    Chen, Y.M., Chang, S.-H.: Purposeful deployment via self-organizing flocking coalition in sensor networks. International Journal of Computer Science & Applications 4(2), 84–94 (2007)Google Scholar
  11. 11.
    Cayirci, E., Coplu, T.: Sendrom: Sensor networks for disaster relief operations management. Journal Wireless Networks 13, 409–423 (2007)CrossRefGoogle Scholar
  12. 12.
    Heo, N., Varshney, P.K.: A distributed self spreading algorithm for mobile wireless sensor networks. IEEE Wireless Communications and Networking (WCNC) 3(1), 1597–1602 (2003)Google Scholar
  13. 13.
    Wang, H., Crilly, B., Zhao, W., Autry, C., Swank, S.: Implementing mobile ad hoc networking (manet) over legacy tactical radio links. In: Military Communications Conference, MILCOM 2007, pp. 1–7. IEEE, Los Alamitos (2007)CrossRefGoogle Scholar
  14. 14.
    Sahin, C.S., Urrea, E., Umit Uyar, M., Conner, M., Ibrahim, G.B., Pizzo, C.: Genetic algorithms for self-spreading nodes in manets. In: GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 1141–1142. ACM, New York (2008)CrossRefGoogle Scholar
  15. 15.
    Urrea, E., Sahin, C.S., Umit Uyar, M., Conner, M., Hokelek, I., Bertoli, G., Pizzo, C.: Bioinspired topology control for knowledge sharing mobile agents. Ad Hoc Netw. 7(4), 677–689 (2009)CrossRefGoogle Scholar
  16. 16.
    Sahin, C.S., Urrea, E., Umit Uyar, M., Conner, M., Bertoli, G., Pizzo, C.: Design of genetic algorithms for topology control of unmanned vehicles. International Journal of Applied Decision Sciences 3(3), 221–238 (2010)CrossRefGoogle Scholar
  17. 17.
    Sahin, C.S., Urrea, E., Umit Uyar, M., Conner, M., Hokelek, I., Bertoli, G., Pizzo, C.: Uniform distribution of mobile agents using genetic algorithms for military applications in manets. In: IEEE International Conference on Military Communications Conference (IEEE/MILCOM), pp. 1–7 (2008)Google Scholar
  18. 18.
    Hokelek, I., Umit Uyar, M., Fecko, M.A.: A novel analytic model for virtual backbone stability in mobile ad hoc networks. Wireless Networks 14, 87–102 (2008)CrossRefGoogle Scholar
  19. 19.
    Sahin, C.S., Gundry, S., Urrea, E., Umit Uyar, M., Conner, M., Bertoli, G., Pizzo, C.: Markov chain models for genetic algorithm based topology control in mANETs. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 41–50. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Sahin, C.S., Gundry, S., Urrea, E., Umit Uyar, M., Conner, M., Bertoli, G., Pizzo, C.: Convergence analysis of genetic algorithms for topology control in manets. In: 2010 IEEE Sarnoff Symposium, pp. 1–5, 12–14 (2010)Google Scholar
  21. 21.
    Sahin, C.S.: Design and Performance Analysis of Genetic Algorithms for Topology Control Problems. PhD thesis, The Graduate Center of the City Univeristy of New York (2010)Google Scholar
  22. 22.
    Hu, Y., Yang, S.X.: A knowledge based genetic algorithm for path planning of a mobile robot. In: Proc. of the 2004 IEEE Int. Conference on Robotics & Automation (2004)Google Scholar
  23. 23.
    Ma, X., Zhang, Q., Lip, Y.: Genetic algorithm-based multi-robot cooperative exploration. In: Proceedings of the IEEE International Conference on Control and Automation, pp. 1018–1023. IEEE Computer Society Press, Guangzhou (2007)Google Scholar
  24. 24.
    Leigh, R., Louis, S.J., Miles, C.: Using a genetic algorithm to explore a*-like path finding algorithms. In: IEEE Symposium on Computational Intelligence and Games, pp. 72–79. IEEE, Honolulu (2007)CrossRefGoogle Scholar
  25. 25.
    Winfield, A.F.: Distributed sensing and data collection via broken ad hoc wireless connected networks of mobile robots. Distributed Autonomous Robotic Systems 4, 273–282 (2000)Google Scholar
  26. 26.
    Howard, A., Mataric, M.J., Sukhatme, G.S.: Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In: Proceedings of the International Conference on Distributed Autonomous Robotic Systems, pp. 299–308 (2002)Google Scholar
  27. 27.
    Tang, F., Parker, L.: Asymtre: Automated synthesis of multi-robot task solutions through software reconfiguration. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1501–1508 (2005)Google Scholar
  28. 28.
    Franchi, A., Freda, L., Oriolo, G., Vendittelli, M.: A randomized strategy for cooperative robot exploration. In: Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy, pp. 768–774 (2007)Google Scholar
  29. 29.
    Stewart, R.L., Russell, A.: A distributed feedback mechanism to regulate wall construction by a robotic swarm. Adaptive Behavior 14, 21–51 (2006)CrossRefGoogle Scholar
  30. 30.
    Joordens, M.A., Shaneyfelt, T., Nagothu, K., Eega, S., Jaimes, A., Jamshidi, M.: Applications and prototype for system of systems swarm robotics. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Singapore, pp. 2049–2055 (2008)Google Scholar
  31. 31.
    Xue, S., Zeng, J.: Sense limitedly, interact locally: the control strategy for swarm robots search. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control, pp. 402–407 (2008)Google Scholar
  32. 32.
    Werfel, J.: Robot search in 3d swarm construction. In: Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems, pp. 363–366. IEEE Computer Society, Washington, DC, USA (2007)CrossRefGoogle Scholar
  33. 33.
    Saska, M., Macas, M., Preucil, L., Lhotska, L.: Robot path planning using particle swarm optimization of ferguson splines. In: Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation, Prague, pp. 833–839 (2006)Google Scholar
  34. 34.
    Jarvis, J.P., Shier, D.R.: Graph-theoretic analysis of finite Markov chains. CRC Press, Cambridge (2000)Google Scholar
  35. 35.
    De Jong, K.A., Spears, W.M., Gordon, D.F.: Using markov chains to analyze gafos. In: Foundations of Genetic Algorithms, vol. 3, pp. 115–137. Morgan Kaufmann, San Francisco (1995)Google Scholar
  36. 36.
    Horn, J.: Finite markov chain analysis of genetic algorithms with niching. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 110–117. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  37. 37.
    Suzuki, J.: A markov chain analysis on a genetic algorithm. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 146–154. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  38. 38.
    Baras, J.S., Tan, X.: Control of autonomous swarms using gibbs sampling. In: CDC – 43rd IEEE Conference on Decision and Control, vol. 5, pp. 4752–4757. IEEE, Los Alamitos (2004)Google Scholar
  39. 39.
    Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, 483–502 (2002)Google Scholar
  40. 40.
    Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks 5 (1994)Google Scholar
  41. 41.
    Winkler, G.: Image Analysis, Random Fields and Markov Chains Monte Carlo Methods. Springer, Heidelberg (2006)Google Scholar
  42. 42.
    Dogan, C., Sahin, C.S., Umit Uyar, M., Urrea, E.: Testbed for node communication in manets to uniformly cover unknown geographical terrain using genetic algorithms. In: Proc. of the NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2009), pp. 273–280 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cem Şafak Şahin
    • 1
  • Elkin Urrea
    • 1
  • M. Ümit Uyar
    • 2
  • Stephen Gundry
    • 2
  1. 1.Department of Electrical EngineeringThe Graduate Center of The City University of New YorkUSA
  2. 2.Department of Electrical EngineeringThe City College of The City University of New YorkUSA

Personalised recommendations