Smart waste management system in smart city

Abstract

Along side the emergence of the Internet of Things (IoT), waste management has emerged as a significant concern. Waste management is a daily task in urban areas, requiring a significant quantity of labor resources and affecting environmental, fiscal, and social factors. Numerous methods, including nearest neighbor search, colony optimization, genetic algorithm, and particle swarm optimization, have been proposed to optimize refuse management. However, the results are still too imprecise to be implemented in actual systems, such as universities or cities. Combining optimal waste management strategies with low-cost IoT architectures has become popular recently. In this paper, we propose a novel method that accomplishes waste management vigorously and efficiently by anticipating the probability of garbage receptacle overflow. Using machine learning and graph theory, the system can optimize the shortest path for refuse collection. Researchers have discovered that in this era of globalisation, waste management alone does not guarantee efficient treatment and disposal to maintain a clean and sustainable environment. IoT-based smart waste management solutions and waste management services are provided via technology-assisted research. And put in place initiatives to save time and energy to cut down on manufacturing waste. Unfortunately, various factors, including the socioeconomic climate in emerging nations, limit the application of current remedies. The main emphasis of this study is the development of intelligent waste management systems based on the Internet of Things for developing nations like India. To provide an IoT-based solution for rubbish collection from dumpsters; to use ML-based methodologies to create effective solutions for garbage collection/dumping and human/vehicle resource management. This guarantees efficient household garbage collection, transportation, disposal, and recycling.