Enhancing the e-commerce shopping experience with IoT-enabled smart carts in smart stores

E-commerce has seen remarkable growth in recent years, facilitated by advancements in digital networks and technologies. However, traditional retail environments often face inefficiencies, such as long payment queues, that hinder customer experience. In this paper explores the application of Internet of Things (IoT) technology in retail settings, leveraging low-cost radio frequency identification (RFID) tags attached to products. These tags enable smart shopping carts equipped with RFID readers to automatically scan items, allowing seamless billing directly from the cart and eliminating the need for manual payment processes. The study compares IoT architectures implemented within cloud and fog computing frameworks to optimize smart store shopping scenarios. A neuro-fuzzy approach is employed to evaluate the efficiency of the proposed model. The neural network model is trained using a dataset split into three segments: 65% for training, 15% for validation, and 20% for testing. Key performance metrics, including the correlation coefficient and mean squared error, are used to assess the model. The results indicate that higher regression and convergence levels, approaching one, correspond to improved network performance and reduced error rates. The findings demonstrate that the proposed IoT-enabled model efficiently balances classification tasks, reduces congestion during algorithm execution, and minimizes energy waste. This research highlights the significance of integrating IoT with cloud and fog computing frameworks to enhance the efficiency and customer experience in retail environments.