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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]

Vehicular ad hoc networks (VANETs) provide information and entertainment to drivers for safe and enjoyable driving. Wireless Access in Vehicular Environments (WAVE) is designed for VANETs to provide services efficiently. Since the control messages transmitted in WAVE are broadcast, the probability of collision increases by increasing the number of vehicle nodes. In this paper, we propose a coordinated multichannel medium access control (C-MAC) protocol for VANETs. The proposed C-MAC protocol provides contention-free broadcasting for safety messages by the coordination of roadside units (RSUs). An RSU schedules the broadcasting order of transmissions for safety messages. Contention-free broadcasting lowers the probability of collision and reduces the required time for transmissions of safety messages. An RSU may play the role of harvesting energy wirelessly to coordinated VANET terminals, which are gateways for Internet-of-things devices in the car. The contention interval for service channels is optimized to provide maximum throughput. The proposed C-MAC protocol is shown to enhance throughput and delay via analysis and simulation. [ABSTRACT FROM AUTHOR]

Summary: In a wireless powered communication network (WPCN), sensor nodes harvest energy to transmit information. By a harvest‐then‐transmit (HT) protocol, nodes can be classified into either energy receiving (ER) or data transmitting (DT) nodes depending on the current level of the harvested energy. Since nodes may join or leave a network any time and energy levels vary, the distribution of ER and DT nodes changes over time. As the number of contending DT nodes is highly dynamic, a quick learning mechanism is required for an access point (AP). We propose a learning AP that learns from experience and adapts the frame size according to the changes in the number of DT nodes. The proposed learning AP is also shown to learn well and react to the situation. We compare the performances of the proposed learning mechanism with a WPCN and conventional HT FSA schemes. The proposed RL scheme outperforms the comparative schemes in terms of success rate and delay. [ABSTRACT FROM AUTHOR]