Wireless Sensor Network-based Machine Learning Framework for Smart Cities in Intelligent Waste Management

Abstract: Abstract One of the major challenges that has been growing and has evolved in the last few decades is the production of waste. The types of waste produced has changed over the years and in the present day and age finding a constructive system for efficient and effective waste disposal has become a centre of concern. Application of AI, Machine Learning and other smart technologies have become focal points that play an extremely vital role in the management of different types of waste. This approach has optimized the process of handling waste. The methodology used in this work is the systematic literature review (SLR) to identify, evaluate, and synthesize studies on the application of artificial intelligence in waste management. The two major key contributing methods in this approach being-Neural Networks and Advanced Machine Learning Algorithms. The use of neural networks plays a crucial role in predicting the amount of waste produced. Whereas, the use of advanced machine learning algorithm will attain optimization in waste collection. This process of integration has reduced the waste quantity produced by 90%, landfill by 40% and transport costs by 15%. Implementation of AI and smart technologies in waste management process has reduced industrial waste by 30-50%, food waste by 40%, medical waste by 30% and has enhanced the recycling of plastics by 50%. AI has no doubt revolutionized the waste management process in multiple ways like reduction in waste, recycling of waste, smarter resource utilization and so forth. However, this reliance has to be limited to a considerable extent and major control of the whole process has to be taken care of with human aid. This study will play a crucial role in understanding the present trends and innovation, the effectiveness of this integration process and will help address environmental impact in a more efficacious approach.

TL;DR: The separation process of the waste will be faster and intelligent using the proposed waste material classification system without or reducing human involvement.