Abstract
We consider a problem of lifetime optimization in Wireless Sensor Networks. The purpose of the system is to find a global activity schedule maximizing the lifetime of the Wireless Sensor Network while monitoring some area with a given measure of Quality of Service. The main idea of the proposed approach is to convert the problem of a global optimization into a problem of self-organization of a distributed multi-agent system, where agents take part in a game and search a solution in the form of a Nash equilibrium. We propose two game-theoretic models related to the problem of the lifetime optimization in Wireless Sensor Network and apply deterministic \(\epsilon \)-Learning Automata as players in the games. We present results of an experimental study showing the ability of reaching optimal solutions in the course of Learning Automata self-organization by local interactions in an iterated game.
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Gąsior, J., Seredyński, F., Hoffmann, R. (2018). Towards Self-organizing Sensor Networks: Game-Theoretic \(\epsilon \)-Learning Automata-Based Approach. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds) Cellular Automata. ACRI 2018. Lecture Notes in Computer Science(), vol 11115. Springer, Cham. https://doi.org/10.1007/978-3-319-99813-8_11
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