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The efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput. [ABSTRACT FROM AUTHOR]

In a large-scale group communication networks, we propose a slotted-Aloha-based access control (SA-AC) scheme with the optimal transmission probability (TP) using the stochastic geometry. Since the performance of SA-AC scheme depends on the TP of the members in a group, the analytical model presents a joint view for the downlink (DL) and the uplink (UL) with TP of the members in a group. We present simple and closed form expressions for the DL and UL joint probability by using Poisson point process (PPP). Furthermore, we analyze the dynamic TP and the optimal TP to maximize the DL/UL joint probability, for which the inclusion of a member and the transmission of the member is determined by the DL and UL thresholds. Since the requirement of the DL/UL joint probability varies with the type of services, the optimal TP has to be determined for an efficient group communication service. The performance of the SA-AC scheme with the optimal TP is demonstrated, and it is shown to be superior over the other schemes. [ABSTRACT FROM AUTHOR]