## 2 Research Scope, Methodology, Bibliometric, and Citation Analysis
### Composting Parameter Sensors for Process Control  
IoT-based composting process parameters sensors can detect and measure vital parameters such as moisture level, pH, temperature, and oxygen concentration. The collected data are critical to understanding the ongoing decomposition conditions and to optimizing the conditions for an accelerated decomposition. For example, smart composing tumblers equipped with composting process parameter sensors can obtain real-time data on organic matter decomposition [29]. This data feed can be used to capture real-time data of different composting stages and allow operators to perform precise adjustments to optimize the thermophilic phase and accelerate the transition from the mesophilic phase to the thermophilic phase [20]. This data feed from the composting process parameter sensors can be used as training data for computer vision, AR (Augmented Reality), AI (Artificial Intelligence), and neural networks [30]. Therefore, IoT-based composting process parameter sensors can serve a dual purpose in composing. One is parameter monitoring and detection of various counterparts that emanated during decomposition and serve as raw data platforms for the development of more advanced algorithms for composting, such as computer vision-enabled waste segregation. Machine learning (ML), AI, and computer vision significantly enhance compost production in IoT-based composting facilities by enhancing real-time composting monitoring, intelligent waste sorting, and process optimization. Intelligent waste segregation can be achieved by computer vision, which uses deep learning models to identify and segregate compostable urban waste from non-compostable materials accurately. This helps reduce contamination and enhances final product quality, as demonstrated by [24]. Real-time composting conditions can be monitored using ML algorithms that use data feeds from sensors tracking oxygen levels, moisture, and temperature and allow dynamic adjustments to optimize microbial decomposition, accelerate composting, and assure high-quality end products [22]. Furthermore, artificial neural networks (ANNs) can be effectively used for predictive modelling of compost quality, where historical data from composting processes can forecast nutrient content and maturity. A study by [31] demonstrated that ANN was employed to predict NH\({}_{3}\) emissions during composting with an R\({}^{2}\) of 0.97 using pH, electrical conductivity, temperature, carbon/nitrogen ratio, and ammoniacal nitrogen levels. This allows optimized composting with minimal harmful emissions. A study by [32] Utilized AR glasses to detect waste in real-time without user interaction to capture images. This approach allowed users to classify urban waste into compostable and non-compostable via 3D object detection with minimal reaction time. IoT-based sensors can be effectively used for real-time constant monitoring and control of critical composting parameters such as pH of 6.0 to 8.0 (electrode pH sensors), moisture content of 50% to 60% (capacitive or resistive moisture sensors), the temperature of 55 \({}^{\circ}\)C to 65 \({}^{\circ}\)C during thermophilic phase (thermocouples or fiber optic sensors (FOS)), aeration levels of 0.4 to 0.6 L/(min\(\cdot\)kg organic matter) during the initial stages, decreasing to 0.2 to 0.4 L/(min\(\cdot\)kg organic matter) during maturation (electrochemical/optical oxygen sensors), conductivity, and metal oxide semiconductor sensors (MOSs) for leachate monitoring and pollution control with minimal human supervision [33; 34; 35; 36]. Furthermore, deep learning approaches can automate and optimize composting parameters. Already existing composting optimization and automation algorithms by [37] can be used to identify all the types of physio-chemical constraints recurring in the feedstock and considered simultaneously during the composting process. A great deal of control over pollution control can be achieved using MOS via gaseous emissions detection of NH\({}_{3}\), CO\({}_{2}\), and volatile organic compounds (VOCs).