Leveraging CNN and IoT for Effective E-Waste Management

• URL: http://arxiv.org/abs/2506.16647v1
• Date: Thu, 19 Jun 2025 23:19:15 GMT
• Title: Leveraging CNN and IoT for Effective E-Waste Management
• Authors: Ajesh Thangaraj Nadar, Gabriel Nixon Raj, Soham Chandane, Sushant Bhat,
• Abstract summary: Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks.<n>This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing proliferation of electronic devices in the modern era has led to a significant surge in electronic waste (e-waste). Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks. This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials. By integrating a camera system and a digital weighing scale, the framework automates the classification of electronic items based on visual and weight-based attributes. The system demonstrates how real-time detection of e-waste components such as circuit boards, sensors, and wires can facilitate smart recycling workflows and improve overall waste processing efficiency.

Related papers

• Safeguarding the Future of Mobility: Cybersecurity Issues and Solutions for Infrastructure Associated with Electric Vehicle Charging [0.0]
  The vast amount of data that EV charging station management systems give is powered by the Internet of Things (IoT) ecosystem.<n>Intrusion detection is becoming a major topic in academia because of the acceleration of IDS development caused by machine learning and deep learning techniques.<n>The goal of the research presented in this paper is to use a machine-learning-based intrusion detection system with low false-positive rates and high accuracy to safeguard the ecosystem of EV charging stations.
  arXiv  Detail & Related papers  (2025-01-24T21:41:38Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Measuring the Recyclability of Electronic Components to Assist Automatic Disassembly and Sorting Waste Printed Circuit Boards [4.0998481751764]
  We focus on the measurement of recyclability of waste electronic components (WECs) from waste printed circuit boards (WPCBs) using mathematical innovation model. This innovative approach evaluates both the recyclability and recycling difficulties of WECs, integrating an AI model for improved disassembly and sorting.
  arXiv  Detail & Related papers  (2024-06-24T12:33:56Z)
• SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation [46.178512739789426]
  We present SpectralWaste, the first dataset collected from an operational plastic waste sorting facility. This dataset contains labels for several categories of objects that commonly appear in sorting plants. We propose a pipeline employing different object segmentation architectures and evaluate the alternatives on our dataset.
  arXiv  Detail & Related papers  (2024-03-26T18:39:38Z)
• Sorting of Smartphone Components for Recycling Through Convolutional Neural Networks [2.9904113489777826]
  We investigate using an image classification neural network as a potential means to control an automated material separation process in treating smartphone waste. We produced a dataset with 1,127 images of pyrolyzed smartphone components, which was then used to train and assess a VGG-16 image classification model. The model achieved 83.33% accuracy, lending credence to the viability of using such a neural network in material separation.
  arXiv  Detail & Related papers  (2023-12-27T16:16:15Z)
• EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classification [0.0]
  Proper disposal of e-waste poses global environmental and health risks. We have presented a comprehensive dataset comprised of eight different classes of images of electronic devices. We have also presented EWasteNet, a novel two-stream approach for precise e-waste image classification.
  arXiv  Detail & Related papers  (2023-09-28T13:12:45Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• MWaste: A Deep Learning Approach to Manage Household Waste [0.0]
  MWaste is a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass or cardboard. Its effectiveness was tested on various neural network architectures and real-world images, achieving an average precision of 92% on the test set.
  arXiv  Detail & Related papers  (2023-04-02T16:56:49Z)
• IoT-based Route Recommendation for an Intelligent Waste Management System [61.04795047897888]
  This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account.
  arXiv  Detail & Related papers  (2022-01-01T12:36:22Z)
• Classification of PS and ABS Black Plastics for WEEE Recycling Applications [63.942632088208505]
  This work is aimed at creating a system that can classify different types of plastics by using picture analysis, in particular, black plastics of the type Polystyrene (PS) and Acrylonitrile Butadiene Styrene (ABS) A Convolutional Neural Network has been tested and retrained, obtaining a validation accuracy of 95%. Using a separate test set, average accuracy goes down to 86.6%, but a further look at the results shows that the ABS type is correctly classified 100% of the time, so it is the PS type that accumulates all the errors.
  arXiv  Detail & Related papers  (2021-10-20T12:47:18Z)
• ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
  We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over1800fully segmented video frames collected from a real waste sorting plant. We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
  arXiv  Detail & Related papers  (2021-06-04T22:17:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.