<![CDATA[Batteryless IoT devices will be key in sixth-generation (6G) networks in the future because they allow massive, maintenance-free implementation of smart cities, agriculture, healthcare, and industrial monitoring. Even though the use of ambient energy sources of solar, thermal, vibration, and radio-frequency (RF) does not require using batteries, the randomized and unpredictable characteristics of harvested energy considerably decrease the communication reliability and performance. Current communication schemes on energy harvesting are mostly fixed and do not adjust themselves to the varying energy situations. The paper will suggest an AI-based, energy-conscious communication architecture of the battery-free IoT devices in 6G settings. The construction is a lightweight model using TinyML to predict the short-term forecasted energy, which was collected by using real-time and historical environmental data. Based on these predictions, a reinforcement learning (RL)-based scheduler would then trade-off spectral energy consumption and communication throughput by dynamically optimizing transmission power and data rate and duty cycle. The proposed method allows reliable and autonomous communication within severe and strict energy constraints through combined energy prediction and adaptive scheduling. Evaluation Simulation allows us to conclude that the given framework is much more effective in terms of throughput stability, packet delivery reliability, latency, as well as using energy in an efficient manner, when compared to traditional fixed-energy-harvesting-based communication approaches. The publication offers a long-lasting and smart background on self-enhanced IoT communications in the next-generation 6G networks.]]>
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