Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

Abstract

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.