This paper explores the development and implementation of smart street lighting systems through the integration of Internet of Things (IoT) technology and fuzzy logic control. As urbanization accelerates, the demand for efficient and adaptive street lighting solutions becomes increasingly crucial for enhancing energy efficiency, reducing operational costs, and improving public safety. Traditional street lighting systems often operate without real-time adaptability, leading to energy wastage and suboptimal performance. By leveraging IoT sensors and networks, this research enables real-time data acquisition and communication between street lights and a central control system. Fuzzy logic algorithms are employed to process the data, allowing for dynamic adjustment of lighting intensity based on environmental conditions, traffic flow, and pedestrian activity. The integration of fuzzy logic provides a robust mechanism to handle the uncertainties and variabilities inherent in urban environments. Our prototype system demonstrates significant improvements in energy efficiency, with adaptive lighting adjustments reducing power consumption by up to 40% compared to conventional systems. Moreover, the enhanced responsiveness of the lighting system contributes to increased safety and comfort for urban residents. The findings underscore the potential of IoT and fuzzy logic integration in creating intelligent, sustainable, and user-centric urban infrastructure.

Understanding human behavior in strategic interactions is crucial for various fields ranging from economics to artificial intelligence. Level-0 models, which assume minimal cognitive effort and use simple heuristics, provide a foundational framework for predicting human decisions in game theory. This paper explores the concept of Level-0 models and their application in analyzing human behavior in strategic games. Level-0 models hypothesize that individuals adopt straightforward decision-making strategies, often based on immediate incentives or basic rules of thumb. Despite their simplicity, these models can offer valuable insights into human behavior, particularly in settings where rationality assumptions may not fully apply. This study reviews prominent Level-0 models, such as random choice, imitation, and heuristic-based strategies, and evaluates their effectiveness in predicting behavior across different game scenarios. Emphasis is placed on how these models capture behavioral patterns that deviate from traditional rational choice theory, shedding light on the complexities of decision-making under uncertainty. Furthermore, the paper discusses practical implications and limitations of Level-0 models in various applications, including economics, psychology, and game theory. By bridging theoretical insights with empirical evidence, this research contributes to a deeper understanding of human behavior in strategic contexts and informs the development of predictive models in diverse domains.