Optimizing Lighting Efficiency Through Automated Smart Lighting Systems: A Study on Network-Based Performance

Electrical lighting systems contribute to approximately 20% of global electricity consumption and 6% of carbon dioxide (CO2) emissions, underlining the urgent need for more efficient and adaptable lighting solutions. Addressing the inefficiency of traditional manual lighting controls is crucial for reducing energy waste, especially in the global energy crisis and increasing carbon emission reduction targets. Despite advances in lighting systems, existing solutions often lack integration of intelligent control and real-time adaptability across diverse networks. This research addresses these gaps by developing a novel automated smart lighting system to optimize energy usage through intelligent control across multiple network environments. The proposed prototype integrates Raspberry Pi modules, relay modules, JavaScript Object Notation (JSON), Thonny IDE, and a web-based Application Programming Interface (API) to enable seamless automation and remote operation. The proposed system was tested under various conditions: local area network (LAN), wide area network (WAN), offline functionality, and across multiple web browsers to evaluate latency and operational reliability. The research results demonstrate that the system performs efficiently online and offline within the same or different networks. For online operations within the same network, delay times for light A ranged between 27 to 39 seconds, light B from 37 to 39 seconds, and light C at 33 seconds. The fastest delay on different networks was 5 seconds, with the longest at 39 seconds. Chrome browser exhibited the fastest performance with a delay of 6 seconds, while Samsung Internet had the longest delay at 39 seconds. These findings highlight the system's adaptability and potential for energy savings, though further work is required to optimize latency and extend network coverage.