## 1 Introduction  
Anaerobic digestion (AD) is a key biological process for energy and agricultural sustainability. In AD, microorganisms break down organic matter, such as animal manure, food waste, and agricultural residues, in the absence of oxygen [1]. Biogas is produced from the AD process, a mixture primarily composed of methane (CH\({}_{4}\)) and carbon dioxide (CO\({}_{2}\)), which can be captured and used as a renewable energy source mainly for electricity generation, heating, and even as a vehicle fuel [2]. Additionally, AD effluent can be used as a natural fertilizer to enhance soil fertility and crop yields [3]. AD is a key process that produces biogas as a renewable energy source, reduces emissions, recycles nutrients, and promotes agricultural sustainability, all of which offer important environmental advantages. AD is a complex biochemical process that relies on the activity of diverse microbial communities to break down organic matter through a series of biochemical reactions, including hydrolysis, acidogenesis, acetogenesis, and methanogenesis [4]. AD instability can arise due to various factors such as feedstock composition fluctuations, overloading, inhibitory substances such as ammonia, and others [5]. Therefore, process indicators and their monitoring are essential to detect and tackle forthcoming inhibition incidents towards maximization efficiency. An ideal indicator should be easy to measure, provide clear insights into the process' current state, and reliably predict potential instability, enabling prompt corrective actions [6]. Key monitor indicators of AD are pH, biogas production and composition, volatile fatty acids (VFAs), temperature, and alkalinity [7]. These indicators can be measured by reliable analytical equipment, such gas chromatography, mid-infrared spectroscopy, high performance liquid chromatography, and bench-scale pH meter, among others. However, these methods are often complex, time-consuming, require manual operation, entail high maintenance costs, and are typically offline measurements [8]. In contrast, online sensors offer significant advantages by providing continuous, real-time data, reducing the need for manual sampling and analysis. This not only enhances the efficiency and accuracy of monitoring but also facilitates immediate data storage and retrieval for trend analysis and process optimization. Moreover, online systems can be equipped with alarms to alert operators when sensor measurements deviate from the preferred range, allowing rapid corrective actions to maintain the stability and performance of the AD process [9]. Despite extensive research on AD indicators, there is no consensus on their applicability, as selected indicators may not be suitable for different reactor types or substrates. Additionally, some indicators are complex to measure, and a limited understanding of their interrelationships can lead to ineffective monitoring and process inefficiencies [10]. However, a comprehensive understanding of the anaerobic bioreactor's state can be achieved by monitoring the AD process through multiple indicators, including the use of online sensors that measure various parameters in real time [11]. Therefore, it is important to develop a system that is relatively easy to integrate with various sensors, cost-effective to implement, and low maintenance to ensure reliable and efficient monitoring of the AD process. Internet of Things (IoT)-based wireless monitoring holds great potential for wastewater treatment, enabling more effective real-time monitoring and control of processes, leading to improved efficiency [12]. The general idea of IoT technology is based on the connection between objects or \"things\" that interact with sensors, software, services and other technologies, enabling the collection and exploitation of data [13; 14]. Through IoT technology, these devices communicate with each other in real time via the internet and other communication protocols [15].