ESP32 as a Dual-Connectivity Data Logger: Exploring GSM and Wi-Fi Integration for IoT

The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) necessitates dependable and adaptable data collection systems to enable real-time analytics and decision-making. This paper investigates the use of ESP32 as a dual-connectivity data logger, combining GSM and Wi-Fi communication to facilitate seamless data acquisition and transmission for AI-driven IoT applications. Wi-Fi enables faster and more cost-effective data transfer in networked areas, whereas GSM connectivity ensures reliable operation in remote or off-grid environments. This hybrid approach improves the performance of AI models in predictive maintenance, environmental monitoring, and smart agriculture by ensuring continuous and high-quality data flow. This study presents the system description and performance of a data logger using an ESP32 microcontroller that works with WiFi and GSM connectivity. The results show that the ESP32 microcontroller with Wi-Fi connectivity transmits data quickly and consistently, resulting in a higher success rate, whereas GSM connectivity ensures that data is logged even in areas with intermittent or no Wi-Fi. The results demonstrate the benefits of dual connectivity in terms of data reliability, accessibility, and AI integration.