## 2 Related Works
### IoT Applied in Waste Classification  
An IoT-driven waste management model using a hybrid predictive framework to track waste in a smart city employs a random forest algorithm to analyze waste data from interconnected smart bins, predicting when bins will fill and prioritizing waste collection. Each bin has sensors to monitor waste levels and detect hazardous gases. The model automates waste regulation, reducing manual intervention and sending alerts when bins are full or dangerous gases are detected [16]. The multimodal cascaded convolutional neural network (MCCNN) for efficient domestic waste classification, utilizing DSSD, YOLOv4, and Faster-RCNN, with a dataset of 30,000 images and 52 categories, is employed for the same purpose [17]. [PERSON] et al. [18] introduce an intelligent waste sorting device with a lightweight GECM-EfficientNet model for waste classification. The model achieves 94.54% accuracy and a 146 ms inference time, deployed on an embedded system without internet dependence. The device automates waste sorting into four categories. The system is validated on two datasets, outperforming mainstream models in accuracy and real-time performance. A Knowledge Extraction Model (KEM) for scheduling waste disposal from IoT-based smart waste bins using LoRa network media is introduced by [PERSON] et al. [19]. Key contributions include using various ML techniques to optimize waste management decisions. The Decision Tree (DT) method achieved the highest performance metrics, with an accuracy of 88.13%, a recall of 86.89%, and a precision of 93.61%. The study underlines the importance of using historical data for knowledge extraction and generating new rules for waste disposal scheduling. [PERSON] et al. [20] benchmark six state-of-the-art DL models (AlexNet, ResNet-50, Res-NeXt, MobileNet_v2, DenseNet, and SqueezeNet) on the WaDaBa dataset for plastic waste classification using transfer learning. The dataset comprises five plastic types (PET, HDPE, PP, PS, and other) and contains 4000 images. ResNeXt achieved an 87.44% accuracy. MobileNet_v2, though slightly behind ResNeXt, demonstrated faster training times. The ResNet-152 architecture for waste classification identifies different types of waste, such as plastic, paper, and metal. The process involves data collection, model training, evaluation, and fine tuning, followed by deployment for real-world waste classification. The model uses DL techniques, particularly transfer learning. The results indicate that ResNet-152 outperforms other models in waste classification tasks [21]. The TrashNet dataset is employed to classify six categories of waste, including cardboard, glass, plastic, paper, metal, and litter. The dataset includes 2527 high-resolution images. Several models are examined, combining DenseNet201 and MobileNet-v2 to create RWC-Net, a novel architecture with exceptional performance. RWC-Net outperformed existing models, achieving an F\({}_{1}\)-Score of 95.01% and strong results across all waste categories, particularly excelling in distinguishing \"cardboard\" and \"plastic\". The model was further validated using ClassActivation Mapping (CAM) to visualize the focus areas of the model, confirming its ability to concentrate on relevant regions [22]. The article by [PERSON] et al. [23] follows the Kitchenham guidelines for systematic literature reviews (SLRs) and focuses on the automatic segregation of waste using ML and physical enablers. Several studies have been analyzed, revealing a trend toward integrating AI technologies, such as image classification and sensor fusion, for waste identification. The importance of datasets, particularly the scarcity of data and the need for large, balanced datasets, is introduced in the paper as a direct path to high accuracy of the results. The paper also shows that material misclassification due to similarities between waste types, limited data, and real-world implementation issues, especially in uncontrolled environments, affects the model's accuracy.