Unix postman software umit uyar6/20/2023 ![]() ![]() Sahin, C.S., Urrea, E., Umit Uyar, M., Conner, M., Ibrahim, G.B., Pizzo, C.: Genetic algorithms for self-spreading nodes in manets. In: Military Communications Conference, MILCOM 2007, pp. Wang, H., Crilly, B., Zhao, W., Autry, C., Swank, S.: Implementing mobile ad hoc networking (manet) over legacy tactical radio links. Heo, N., Varshney, P.K.: A distributed self spreading algorithm for mobile wireless sensor networks. International Journal of Computer Science & Applications 4(2), 84–94 (2007)Ĭayirci, E., Coplu, T.: Sendrom: Sensor networks for disaster relief operations management. 4576–4581 (2003)Ĭhen, Y.M., Chang, S.-H.: Purposeful deployment via self-organizing flocking coalition in sensor networks. In: Proceedings of the IEEE International Conference on Systems Man And Cybernetics, pp. Heo, N.: An intelligent deployment and clustering algorithm for a distributed mobile sensor network. In: International Conference on Computer Science and Information Technology, pp. 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 Natural Computation, vol. 5, pp. Ping-An, G., Zi-Xing, C., Ling-Li, Y.: Evolutionary computation approach to decentralized multi-robot task allocation. IEEE Computer Society, Washington, DC, USA (2002) ![]() In: ICAIS 2002: Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002), p. ![]() Miryazdi, H.R., Khaloozadeh, H.: Application of genetic algorithm to decentralized control of robot manipulators. of the IEEE International Conference of Evolutionary Computing, pp. University of Michigan Press, Ann Arbor (1975)īekey, G., Agah, A.: A genetic algorithm-based controller for decentralized multi-agent robotic systems. Holland, J.H.: Evolutionary swarm robotics: Evolving Self-organizing Behaviors in groups of Autonomous Groups. Kluwer Academic Publishers, Dordrecht (1997) In: The Second Turkish Symposium on Artificial Intelligence and Neural Networks, pp. Yuret, D., de la Maza, M.: Dynamic hill climbing: Overcoming the limitations of optimization techniques. Mitchell, M.: An Introduction to Genetic Algorithms. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. 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. 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. 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. An inhomogeneous Markov chain is used to analyze the convergence speed of our bio-inspired algorithm. ![]() Our fga is suitable for manet environments since mobile nodes, while running the fga, only use local neighborhood information. 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. 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). ![]()
0 Comments
Leave a Reply. |