Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach

Today’s networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well.