![]() IEEE Journal on Selected Areas in Communication 26(1) (2008) Ji, Z., Liu, K.J.R.: Multi-stage pricing game for collusion-resistant dynamic spectrum allocation. ACM/Springer Mobile Networks and Applications 11(3), 405–418 (2006) Huang, J., Berry, R.A., Honig, M.L.: Auction-based spectrum sharing. Distributed Autonomous Robot Systems 5, 299–308 (2002) Howard, A., Mataric, M.J., Sukhatme, G.S.: Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Holland, J.H.: Adaptation in Natural and Artificial Systems. Van Hoesel, S.: An overwiew of Stackelberg pricing in networks. Gundry, S., Urrea, E., Sahin, C.S., Zou, J., Kusyk, J., Uyar, M.U.: Formal convergence analysis for bio-inspired topology control in manets. In: ACM Symposium on Parallel Algorithms and Architectures, pp. Gairing, M., Monien, B., Tiemann, T.: Selfish routing with incomplete information. In: European Conference on Complex Systems (ECCS), p. Mobile Networks and Applications 11(2), 143–159 (2006), doi įischer, S., Vöcking, B.: Evolutionary game theory with applications to adaptive routing. IEEE Transactions on Robotics and Automation 20(2), 243–255 (2004)Įidenbenz, S., Kumar, V.S.A., Zust, S.: Equilibria in topology control games for ad hoc networks. Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (1995)Ĭortés, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. 175–179 (2003)Ĭhen, M., Zalzala, A.: Safety considerations in the optimization of the paths for mobile robots using genetic algorithms. In: Proceedings of the 14th International Workshop on Database and Expert Systems Applications (DEXA), pp. IEEE Transactions on Evolutionary Computation 6(6), 566–579 (2002)īarolli, L., Koyama, A., Shiratori, N.: A qos routing method for ad-hoc networks based on genetic algorithm. ![]() This process is experimental and the keywords may be updated as the learning algorithm improves.Īhn, C., Ramakrishna, R.S.: A genetic algorithm for shortest path routing problem and the sizing of populations. These keywords were added by machine and not by the authors. BioGame is a good candidate for self-spreading autonomous nodes that provides a power-efficient solution for many military and civilian applications. ![]() Our BioGame outperforms FGA and successfully distributes mobile nodes over an unknown geographical terrain without requiring global network information nor a synchronization among the nodes. Simulation experiments demonstrate that BioGame performs well with respect to network area coverage, uniform distribution of mobile nodes, the total distance traveled by the nodes, and convergence speed. We prove the basic properties of BioGame, including its convergence and area coverage characteristics. In this chapter, we present the FGA and the spatial game elements of our BioGame. Next, favorable locations identified by FGA are evaluated by the spatial game set up among a moving node and its current neighbors. ![]() First, our force-based genetic algorithm (FGA) finds a set of preferred next locations to move. Each mobile node runs BioGame autonomously to make movement decisions based solely on local data. BioGame is fully distributed, scalable, and does not require synchronization among nodes. The goal of the BioGame is to maximize the area covered by mobile ad hoc network nodes to achieve a uniform node distribution while keeping the network connected. We introduce a new node spreading bio-inspired game (BioGame) which combines genetic algorithms and traditional game theory. ![]()
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