IoT-Based Automated Waste Segregation System for Smart Cities: A Human-Centric Approach to Sustainable Waste Management

Abstract

Urbanization and a booming population are behind the global garbage challenge requiring innovative and sustainable solutions. The conventional way of waste management has deteriorated with disastrous results for the environment and society. In the context of smart cities, innovative practices in trash segregation automation using IoT technology are discussed in this study. This research critically analyzes the social, ethical, and environmental implications of these systems while also focusing on a human-centered perspective, rather than only technical considerations. Besides covering the inherent limitations and challenges, it also provides a comprehensive examination of the intelligent algorithms, field-level implementations, and technology background of IoT-based automated trash segregation. In this last chapter, we elaborate on future directions and policy suggestions for all stakeholders to create an enabling environment for the adoption of more sustainable waste management practices and, in turn, cleaner, healthier, and liveable urban environments.

Introduction

1. Global Waste Crisis

• Rapid urbanization, population growth, and rising consumption have caused global waste levels to surge—expected to hit 3.4 billion tonnes annually by 2050 (World Bank).

• Traditional waste management methods—open dumping, burning, and landfilling—pose serious health, environmental, and climate risks.

• Current systems are often inefficient, leading to pollution, fuel wastage, and uncollected garbage.

2. Rise of Smart Waste Management

• Smart technologies, especially those using the Internet of Things (IoT), offer real-time monitoring, intelligent sorting, and route optimization.

• These innovations support a circular economy by enhancing resource reuse and operational efficiency.

• However, they raise ethical, social, and technical challenges, including data privacy and workforce displacement.

3. Evolution of Waste Systems

• Waste management has evolved from manual dumping to intelligent systems driven by networked sensors and AI.

• Early efforts faced challenges like poor sorting accuracy and contamination.

• Cities like Singapore and Amsterdam use smart bins, ultrasonic sensors, and automated sorting to improve recycling efficiency and cut emissions.

4. Core Technologies in IoT-Based Waste Segregation

A. Sensors:

• Use infrared, ultrasonic, and camera-based sensors (CNNs) for material detection and classification.

• These act as the system’s sensory organs, enabling real-time waste recognition.

B. Microcontrollers:

• Act as the "brains" of the system, processing sensor data, and triggering actions like sorting or notification.

• Suitable for AI tasks like image classification and machine learning.

C. Data Transmission Protocols:

• Common protocols: MQTT, HTTP, LoRaWAN, and Cellular (4G/5G).

• Chosen based on range, data size, reliability, and power consumption.

D. Cloud Platforms:

• Platforms like AWS IoT Core, Azure IoT Hub, and Google Cloud IoT offer scalable storage, analytics, and remote access.

• Enable machine learning, real-time dashboards, and route optimization.

5. AI Algorithms for Waste Classification

• Convolutional Neural Networks (CNNs): Most accurate for image-based classification.

• Support Vector Machines (SVM): Efficient for smaller datasets.

• Random Forests: Good for mixed sensor data and noise resilience.

• Transfer Learning & Data Augmentation: Reduce training time and improve generalization with limited datasets.

6. Real-World Smart City Case Studies

• Singapore: High-tech MRFS, robotic sorters, smart bins—achieves high recycling rates.

• Amsterdam: Underground bins with fill sensors; public engagement enhances recycling rates.

• Copenhagen: Smart garbage cans provide feedback to citizens; advanced analytics for waste hotspot mapping.

• San Francisco: Strong composting programs reduce landfill waste; residents receive personalized waste feedback.

7. Key Challenges

• Interoperability: Lack of standard data formats/protocols hinders system integration.

• Data Security: Risk of privacy breaches from household-level tracking.

• Environmental Limits: Sensors may malfunction in extreme weather or dusty environments.

• Costs: Infrastructure, training, and maintenance can be high.

• Public Adoption: Resistance to change and lack of awareness hinder effectiveness.

• Social Impact: Automation may displace waste workers—requires retraining and support programs.

8. Vision for the Future

• Inclusive Design: Systems must be accessible and equitable for all communities.

• Behavioral Change: Public awareness campaigns are essential for successful adoption.

• Edge-Cloud Integration: Combining fast local processing with cloud analytics for efficiency.

• Scalability: Flexible sensor networks and modular systems can be expanded city-wide.

Conclusion

This is a study on waste sorting that brings in some ingenuity in the application of IoT technology as an answer to ever-increasing waste challenge human face in waste management. It details the limitations of existing systems for waste development and about the IoT-based approaches which are likely to enhance efficiency and to advocate for circular economy, and also about intelligent algorithms and the choices of data controlling as important aspects. The study has offered a thorough and objective perspective on the promise and limitations of these systems by close examination of theoretical columns, real applications, and particular issues. These systems can potentially improve the safety of employees while also reducing pollution, increasing recycling rates, and cutting down costs to run them by automating the sorting process. Municipalities usually take a better decision for waste, resource, and directory development collection due to real-time information generated from these systems. Moreover, new models of circular economy based on efficient waste management would be able to protect limited resources, mitigate their negative effects on the environment, and generate new business opportunities. This must bring with it standardization, safe security settings, and routine maintenance to sort out the technical problems such as sensor dependability, data security, and system integration. Long-term benefits and environmental savings should be compared against economic factors such as ROI and up-front capital costs. Human education, worker transition assistance programs, and the design of systems integration should be put into gear to address social and morale-related issues which include public acceptability, worker displacement, and equity and fairness. Some methods include chemically based imaging techniques and hyperspectral imaging. These make waste identification and sorting with maximum accuracy and reliability. They ensure the traceability and transparency of the waste supply chain while allowing for effective tracking of recyclables and discouraging illegal disposal. All in all, an effective architecture or system in terms of practice. The paper concludes with an appeal to local government bodies and their industrial interest groups, researchers, and political decision-makers to adopt much more humane and sustainable modes of waste management. Communities would do well to tackle the associated issues and parameters and invest in an IoT-based automated waste management system so that they could minimize waste, conserve resources, and steer everyone toward a cleaner, healthier, and more valued future. The transition to a circular economy, motivated by social conscience and technological advances, creates the enabling environment for developing resilient, equitable, and prosperous community’s alongside positive environmental action.

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Copyright

Copyright © 2025 Ayush Sharma, Jatin Kumar, Sachin Rawat, Yatindra Nath Pandey, Vatika Jalali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.