Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions

Искусственный интеллект и Интернет вещей для интеллектуального управления отходами: проблемы, возможности и перспективные направления
Sameh Fuqaha, Nursetiawan Nursetiawan
2025-04-06

Artificial intelligenceAutomated waste sortingInternet of ThingsPredictive analyticsSmart waste management
Indonesia’s waste management system struggles to keep pace with rapid population growth and urbanization, resulting in inefficient waste collection, environmental degradation, and low recycling rates. The country predominantly relies on open dumping and landfilling, which contribute significantly to pollution and greenhouse gas emissions. This study explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in waste management, focusing on smart waste collection, automated sorting, real-time monitoring, and predictive analytics. AI-driven waste classification enhances recycling efficiency, while IoT-enabled smart bins optimize collection routes, reducing operational costs and landfill dependency. Despite these advantages, challenges such as high implementation costs, digital infrastructure limitations, and data privacy concerns hinder widespread adoption. This study highlights that policy support, investment in digital infrastructure, and stakeholder collaboration are crucial for successful implementation. By leveraging AI and IoT, Indonesia can significantly improve waste management efficiency, minimize environmental impact, and advance circular economy initiatives. The findings suggest that, with the right policies and investments, AI-driven waste management can drive sustainability, reduce waste mismanagement, and promote resource optimization, making it a vital strategy for future urban development in Indonesia.
1
AI and IoT support smart waste collection, automated sorting, real-time monitoring, and predictive analytics as integrated approaches to improving waste-management performance.
2
AI-based waste classification can improve recycling efficiency, while IoT-enabled smart bins can optimize collection routes, reduce operational costs, and decrease landfill dependency.
3
High implementation costs, limited digital infrastructure, and data-privacy concerns are major barriers to widespread adoption of AI- and IoT-based waste management.
4
Indonesia’s rapid population growth and urbanization have contributed to inefficient waste collection, environmental degradation, low recycling rates, and continued reliance on open dumping and landfilling.
5
Policy support, digital-infrastructure investment, and collaboration among stakeholders are identified as essential for improving efficiency, reducing environmental impacts, and advancing circular-economy initiatives in Indonesia.

Indonesia’s waste management system

the role and implementation challenges of AI- and IoT-enabled smart waste management, including collection optimization, automated sorting, real-time monitoring, predictive analytics, and impacts on recycling efficiency and environmental sustainability

Publication Details
Publication Date
2025-04-06
Journal
Publisher
ISSN
Cited by
19
Access Type
Author Information
Authors
Sameh Fuqaha
Nursetiawan Nursetiawan
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%