Adaptive Energy Management for Smart Parking Sensors: Enhancing Efficiency and Sustainability

Abstract

Energy efficiency in sensors used for smart parking applications is crucial for reducing operational costs and environmental impact in modern cities. This paper introduces an adaptive method to optimize the energy consumption of sensors. Our approach adjusts the sleep cycles of sensors based on daily demand fluctuations, identifying peak hours and off-peak periods to minimize unnecessary activity. Additionally, it introduces a conditional data transmission mechanism, which limits information sending to instances when a change in parking space occupancy is detected. Simulation demonstrate that this method can significantly reduce energy consumption while maintaining high system performance and reliability. Thus, our study contributes to the development of more sustainable strategies for energy management in urban parking infrastructures.