AIoT-Based Waste Classification for Solid Waste Management to Accomplish the SDGs

The Fourth Industrial Revolution, known as IR4.0 technologies, has enhanced global economic capabilities and productivity, but there are negative environmental and health impacts due to industrialisation and urbanisation, such as greenhouse gas emissions and global warming. One effective method that reduces environmental impact is to conduct a waste classification program that incorporates the principles of 3R. The proposed work includes educating individuals and businesses on the importance of waste reduction, promoting reusable products and packaging, and implementing effective recycling systems. Additionally, Governments could also incentivise sustainable practices through tax breaks or invest in renewable energy sources to reduce greenhouse gas emissions associated with industrial processes. The proposed study aims to develop automated waste classification technology that can help reach SDGs 11, 12, and 13 by making waste management more efficient, increasing recycling and resource recovery rates, and cutting down on greenhouse gas emissions. The proposed system is developed using a deep learning algorithm with a microprocessor and microcontroller managing sensors and actuators to perform waste sorting based on the classification result. This distinguishes the proposed system from existing manual and RFID-based approaches by integrating AIoT with a user incentive mechanism, improving both accuracy and public adoption. This technology enhances overall sustainability and promotes a more circular economy by enabling the reuse and recycling of materials for their own well-being through process innovation.

@article{Shien2025,
  title     = {AIoT-Based Waste Classification for Solid Waste Management to Accomplish the SDGs},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {T. M. Shien and M. Batumalay and Balasubramaniam Muniandy and Pavan Kumar Pagadala and Vinoth Kumar. P},
  doi       = {10.14569/IJACSA.2025.0160866},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160866}
}