## 1 Introduction
Most research to date has leveraged IoT technology with Programmable Logic Controllers (PLC), an industry-approved solution for reliable data generation widely adopted across various sectors. [PERSON] et al. used a PLC to perform remote monitoring of pH, temperature, and Oxidation-Reduction Potential (ORP) of a pilot reactor with a working volume of 657 L that anaerobically processed food waste to produce biogas [16]. It was concluded that the IoT system contributed significantly to the process performance improvement resulting in optimized operation and maintenance. [PERSON] et al. developed a biogas-pH automation monitoring and control system based on a PLC to optimize the organic loading rate (OLR) of an anaerobic membrane bioreactor treating high Chemical Oxygen Demand (COD) starch wastewater. However, PLC systems require specialized personnel for programming, while the implementation of complex changes often demands reprogramming [17]. Additionally, securing these systems involves obtaining specific financial resources and arranging repairs through external technicians, who are usually not part of the university or research center staff, leading to significant delays in research. The ESP32 microcontroller developed by Espressif Systems (Shanghai, China) offers an affordable and versatile solution for IoT applications, featuring built-in Wi-Fi and Bluetooth capabilities that enable real-time data transmission and remote monitoring [18]. It can be easily programmed by leveraging the Arduino@ Integrated Development Environment software and can utilize various peripherals such as sensors and actuators for online monitoring and controlling process systems [19]. [PERSON] et al. used an ESP32 to monitor the temperature, pH, pressure, and biogas volume of a 7.5 L anaerobic bioreactor treating cassava wastewater [20]. They concluded that it helped automatically save data, reducing both the effort and time required for data acquisition. Nowadays, open-source Artificial Intelligence platforms are valuable tools for coding and learning the appropriate programming languages for microcontrollers like the ESP32 [21]. Additionally, the ESP32, when equipped with suitable sensors, can efficiently generate datasets that can be stored online. The quantity and quality of this data are the backbone of neural network training [22], which is crucial for developing future AI applications aimed at improving anaerobic bioreactor efficiency. Modern microcontrollers like the ESP32 provide low acquisition cost, high quality, and reliability, as well as the ability to connect via communication protocols, like Wi-Fi and Bluetooth, for data transmission and logging. Nevertheless, their adoption among researchers remains limited, which might be due to the time investment required to learn how to program new devices. However, with the ongoing development of the IoT, programming and controlling these devices offer unprecedented monitoring and control capabilities that were previously challenging to implement due to high costs. In this direction, a low-cost and relatively accessible solution was developed for data-driven research and AD process monitoring by integrating IoT-compatible microcontroller technology. This study aimed to achieve a low-cost transformation of a 60 L pilot anaerobic bioreactor into an IoT-enabled device. Pilot reactors are typically installed in locations where access to analytical instruments is limited. The purpose of this study was to evaluate the capabilities and reliability of an IoT-based monitoring system for the AD process using online sensor instruments. Key parameters, including temperature, pH, reduction-oxidation (REDOX) potential, ammonium concentration, biogas production, and CH\({}_{4}\) content in biogas, were measured and logged into a database. Analytical methods were used to validate the data collected by the IoT system's sensors and devices. The study also explored the efficiency, capabilities, advantages, and potential future upgrades of an IoT-based bioreactor.