Energy-Efficient Automatic Street Lighting System for Smart Cities

Abstract

Street lighting automation has emerged as a crucial strategy for maximizing energy efficiency while maintaining sufficient illumination for visibility and safety. Due to lights being left on throughout the day or turned on too late at night, traditional street light management systems that rely on mechanical timers or manual operations frequently waste electricity. An autonomous public lighting management system has been created to address these inefficiencies. It uses motion sensors, microcontrollers, and Lighting Dependence Resistance (LDRs) to dynamically adjust street lighting in response to human activity and natural light intensities. The device is intended to function autonomously, eliminating the need for human involvement by deploying an LDR sensor that detects differences in natural light intensity. The microprocessor turns on the street lights when the ambient light level drops below a preset threshold. To further optimize energy use, a photoelectric sensor is also incorporated to detect the movement of pedestrians or vehicles. This sensor makes sure that streetlights are only turned on when necessary. The MikroC IDE is used to program the microcontroller, which interprets sensor data and manages the relay mechanism to turn on and off the lights as needed. This strategy greatly lowers energy waste and advances the creation of intelligent, energy-efficient urban infrastructure. A survey of the literature emphasizes how crucial it is to include innovations in road lighting designs, such as solar energy panels, GSM modules, and Zigbee wireless connectivity. Prior studies have indicated that the integration of smart sensors and wireless communication systems can enable predictive maintenance, remote monitoring, and enhanced street lighting network dependability. The study's methodology includes creating a control circuit that dynamically modifies street light operation to save energy by continuously monitoring motion and ambient light levels. When evaluated in a controlled setting, the system showed up to 50% energy savings over traditional lighting systems. Furthermore, the design is adaptable and may be expanded for sizable cities with little change. Studies show that the suggested method improves long-term viability dependability, and operational effectiveness. To further increase productivity and ecological responsibility, future improvements might incorporate powered AI maintenance planning, IoT-based remote surveillance, and green energy incorporation. By supporting computerized, cost-effective lighting strategies that reduce expenditures and ecological impact while maintaining safety for residents, these studies advance smart city ambitions.