Enhanced Smart Souq: IoT, AI, and Fuzzy Logic for Modernizing Traditional Markets in Oman

This paper proposes a smart souq management system that leverages IoT, AI, and fuzzy logic technologies to enhance the operation and visitor experience of traditional markets in Oman. The system aims to optimize stall operations, improve visitor engagement, and preserve the cultural essence of Omani souqs. IoT sensors collect data on inventory levels, sales, environmental conditions, and visitor interactions. This data is processed using a cloud platform that employs AI algorithms and fuzzy logic rules to generate actionable insights and recommendations. AI models, specifically Random Forest Regressors, Decision Tree Classifier, and Gradient Boosting Machines (GBM), are trained to predict key metrics such as sales and customer satisfaction. Fuzzy logic rules are applied to manage inventory, optimize stall operations, and enhance visitor engagement. The research utilizes our own synthetic data generated based on historical data, expert input, and statistical distributions to demonstrate the effectiveness of the proposed approach. The system's performance is evaluated through detailed analysis and case studies, showcasing its potential to improve the efficiency and cultural preservation of traditional markets in Oman.