Enhancing Ice-Snow Consumer Electronics With AIoT: A Deep Reinforcement Learning Approach for Robust and Energy-Efficient Performance

The rapid advancement of Artificial Intelligence of Things (AIoT) technology has revolutionized the consumer electronics industry, particularly in the domain of ice-snow consumer electronics. However, the harsh environmental conditions and high-performance demands of ice-snow sports pose significant challenges to the reliability and robustness of these devices. This paper explores integrating AIoT capabilities into ice-snow consumer electronics to enhance performance, reliability, and user experience. We propose a novel system model that incorporates task modeling, channel modeling, energy consumption modeling, and reliability constraint modeling to address the unique challenges ice-snow consumer electronics face. Furthermore, we formulate a mathematical optimization problem to minimize the total energy consumption of AIoT-enabled devices while ensuring data transmission reliability and meeting deadline constraints. To solve this problem, we develop an AIoT-enabled resource allocation algorithm based on deep reinforcement learning. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm in improving the reliability and energy efficiency of ice-snow consumer electronics. This research contributes to developing robust and reliable AIoT-enabled consumer electronics for extreme environments.