TEJAS Journal of Technologies and Humanitarian Science

Vijay Kumar Tiwari
Assistant Professor, Babu Banarasi Das University, Lucknow, India

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Abstract:

The scenario of municipal solid waste management is becoming increasingly complex with rapid urbanization and population growth, which is heavily affecting the environment. Conventional waste management systems have inefficiencies in routing, collection, and tracking, as well as a lack of public engagement. To overcome these challenges, this paper proposes an intelligent waste management system that combines Artificial Intelligence, Internet of Things, and computer vision. The system integrates real-time GPS location tracking for garbage trucks and AI-driven route optimization to optimize efficiency. Smart bins, enabled with IoT technology and ultrasonic sensors for fill-level measurement, automate waste level determination. Computer vision analysis indicates waste buildup and illegal dumping, while predictive analysis determines future trends in waste generation, allowing dynamic route adjustment. A citizen mobile app enables users to monitor collection schedules, lodge complaints, and engage in reward incentives for cleanliness. Noise pollution monitoring to reduce the environmental footprint of garbage trucks is also included, along with a government dashboard that provides insights through data analytics. Simulated urban testing showed considerable increases in efficiency, lowering collection times by 26%, fuel consumption by 31%, and boosting citizen satisfaction levels by 42% compared to existing systems. Utilizing the power of smart technology, this model can change the face of waste management, making it eco-friendly, an affordable solution, and inclusive of the citizens. This research paper emphasizes the role of AI, IoT integration, and data solutions in the development of smart cities.

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