Scholarlink

<p>Our proposed architecture, AutoSortBin, is based on the Cyber Physical System (CPS) and Internet of Things (IoT) technology which emphasizes the importance of proper waste disposal and segregation. To tackle the problem of waste segregation and management, AutoSortBin automatically classifies and sorts the waste into six broad categories namely metal, paper, plastic, glass, organic, and e-waste. The technical idea behind this segregation is implemented by the DenseNet-121 model for transfer learning integrated into the Wokwi simulator using ThingSpeak. The proposed framework takes an image input through a camera and identifies the waste as one of the main categories. The output from the waste identification model serves as an input for the IoT-based circuit and it opens the corresponding waste bin lid via servo motors. It also uses ultrasonic distance sensor to monitor the storage level in the waste bins. The proposed framework demonstrates an automation system that alerts the authorities to empty a bin whenever a bin is full by sending emails, to solve the problem of waste management efficiently thus contributing to environmental sustainability. The proposed framework has high potential for scalability and integration with different CPS and IoT platforms for enhanced performance and features. The setup exhibited an exceptional accuracy of 94.63% across the dataset collected.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AutoSortBin: Integrating CPS and IoT with Densely Connected Convolutional Networks for Sustainable Waste Management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月19日

查看全文
  • Mayank Sangwan,
  • Avin Chaudhary,
  • Ayush Gupta,
  • Himank Gupta,
  • Vinay Kharb,
  • Shweta Sharma,
  • Sweeti Sah

摘要

Our proposed architecture, AutoSortBin, is based on the Cyber Physical System (CPS) and Internet of Things (IoT) technology which emphasizes the importance of proper waste disposal and segregation. To tackle the problem of waste segregation and management, AutoSortBin automatically classifies and sorts the waste into six broad categories namely metal, paper, plastic, glass, organic, and e-waste. The technical idea behind this segregation is implemented by the DenseNet-121 model for transfer learning integrated into the Wokwi simulator using ThingSpeak. The proposed framework takes an image input through a camera and identifies the waste as one of the main categories. The output from the waste identification model serves as an input for the IoT-based circuit and it opens the corresponding waste bin lid via servo motors. It also uses ultrasonic distance sensor to monitor the storage level in the waste bins. The proposed framework demonstrates an automation system that alerts the authorities to empty a bin whenever a bin is full by sending emails, to solve the problem of waste management efficiently thus contributing to environmental sustainability. The proposed framework has high potential for scalability and integration with different CPS and IoT platforms for enhanced performance and features. The setup exhibited an exceptional accuracy of 94.63% across the dataset collected.