ESP32-Based Dual-Connectivity Data Logger for Continuous Environmental Monitoring for AI Applications

Pollution, global warming, and natural disasters are some of the current environmental problems facing humanity. Appropriate technologies are introduced to effectively solve environmental problems, and conditions are regularly monitored to collect reliable data. The Internet of Things (IoT) has revolutionized environmental protection systems by enabling real-time monitoring, automation, and data-driven decision-making. Present data loggers rely on one kind of communication, either Wi-Fi or Global System for Mobile Communications (GSM) technology, for IoT solutions. Wi-Fi offers high data rates with minimal latency; however, its performance is limited to a confined coverage area, making it unsuitable for long-distance or mobile applications. In contrast, GSM provides wide-area coverage and reliable connectivity in remote regions but suffers from slower data speeds, higher latency, and greater energy consumption. In Artificial Intelligence (AI) applications, continuous data acquisition is vital to ensure accurate training, reliable predictions, and effective automated decision-making. This paper introduces the ESP32 microcontroller-based dual-connectivity data logger, combining GSM and Wi-Fi communication in order to enable smooth data acquisition and transmission of environmental parameters for AI-based IoT applications with reduced data losses. This hybrid method will improve the performance of AI-based models in predictive maintenance, environmental monitoring, and smart agriculture by ensuring that data flows continuously and is of high quality. The experimental results showed that, when using Wi-Fi, the ESP32 delivers rapid, uniform data transfer with an elevated success rate, while GSM connectivity preserves logging functionality during Wi-Fi outages or fluctuations. Overall, the proposed dual-connectivity logger achieved a 92% success rate, reduced latency by 400 ms, and maintained moderate power consumption, proving its superiority in data reliability, accessibility, and operational efficiency. The findings show that dual connectivity has an advantage in terms of data reliability, access, and efficacy.