V3I5P160

AI-Based Approach for Optimal Waste Collection Bin Placement for Efficient Urban Waste Management

Dr. Nayeemuddin Ahmed^1*

Abstract

The rapid development of smart cities has increased the need for efficient, sustainable, and technology-driven urban services. Solid Waste Management (SWM) is a critical component of this transformation, as improper waste handling directly affects public health and environmental sustainability. Recent advances in the Internet of Things (IoT) have enabled real-time monitoring and intelligent control of waste collection systems, reducing issues such as bin overflow and inefficient collection routes. In this study, an AI-driven approach is proposed to determine the optimal placement of IoT-enabled smart waste bins in urban environments. A computerized optimization algorithm is developed to identify locations that maximize accessibility and coverage, ensuring that the greatest number of residents benefit from the system. Additionally, Machine Learning (ML) techniques are integrated to analyse historical waste data, allowing the system to learn usage patterns and support informed decision-making. The proposed framework enhances waste collection efficiency, minimizes operational costs, and contributes to the development of sustainable and intelligent smart city infrastructure.

Keywords:

Site_weight, Minimum Vertex Cover, Problem Weight, Sensors, Vertex Cover, Smart Dustbin, IoT, Machine Learning