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Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential requirement for the Medium Access Control (MAC) protocols to increase system performance. A collision occurs in wireless channel when two or more nodes try to access the channel at the same time. In this paper, a reinforcement learning-based MAC protocol was proposed to provide high throughput and alleviate the collision problem. A collaboratively predicted Q-value was proposed for nodes to update their value functions by using communications trial information of other nodes. Our proposed protocol was confirmed by intensive system level simulations that it can reduce convergence time in 34.1% compared to the conventional Q-learning-based MAC protocol.

Summary The design of robust and high‐performance hydrogen evolution reaction (HER) catalysts is crucial for the scalable production of hydrogen by electrochemical water splitting. In this work, we fabricated hierarchically porous Co‐Ru catalysts with excellent catalytic activity and durability in acidic media. The morphology of the Co‐Ru catalysts was successfully controlled through an optimized electrochemical process. Among all Co‐Ru samples prepared in this study, the Co72Ru28 catalyst exhibited the largest catalytic surface area, as well as excellent HER activity (overpotential of 26.4 mV at −10 mA cm−2) and durability. X‐ray photoelectron spectroscopy measurements revealed the electron transfer between Co and Ru, indicating that the formation of the Co‐Ru alloy improved the activity and durability of the catalyst. Furthermore, a proton exchange membrane water electrolyzer (PEMWE) prepared with the Co72Ru28 sample showed excellent performances (3.4 A cm−2 at 2.0 Vcell), illustrating the promising potential of the present Co‐Ru catalysts as cathode materials for PEMWE systems.