Energy Optimization in IoT Networks Using AI
DOI:
https://doi.org/10.22105/scfa.v1i3.45Keywords:
Energy optimization, Internet of things networks, Artificial intelligence, Machine learning, Reinforcement learning, Resource allocation, Predictive maintenanceAbstract
The rapid expansion of Internet of Things (IoT) networks poses a considerable challenge in managing energy efficiency due to the large number of connected devices. This study tackles the urgent requirement for effective energy management in IoT networks by utilizing Artificial Intelligence (AI) methodologies. We introduce a strategy that employs machine learning and reinforcement learning techniques to optimize energy consumption in real-time, thereby improving device lifespan and lowering operational expenses. The approaches developed concentrate on adaptive scheduling, predictive maintenance, and smart resource allocation to ensure optimal energy distribution among devices. Experimental assessments indicate a significant decrease in energy use while preserving network performance. This research underscores the capability of AI-based solutions to transform IoT energy management, offering a pathway toward sustainable IoT ecosystems.
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