Distributed Frame Size Selection for a Q learning based Slotted ALOHA Protocol

This paper proposes a novel distributed frame size selection (DFS) algorithm for assigning an optimum frame size in a Q-learning based Slotted ALOHA medium access control protocol wireless sensor network. To evaluate the effectiveness of DFS, corresponding theoretical analysis and simulations are carried out. According to the results, the DFS algorithm can help individual node to independently establish the best frame size in order to achieve optimum system performance.