Using learning automata for adaptive push-based data broadcasting in asymmetric wireless environments

Push systems are not suitable for applications with a priori unknown, dynamic client demands. This paper proposes an adaptive push-based system. It suggests the use of a learning automaton at the broadcast server to provide adaptivity to an existing push system while maintaining its computational complexity. Using simple feedback from the clients, the automaton continuously adapts to the client population demands so as to reflect the overall popularity of each data item. Simulation results are presented that reveal the superior performance of the proposed approach in environments with a priori unknown, dynamic client demands.