Waste Management System Using IoT-Based Machine Learning in University

Abstract

Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.

Environmental survival of SARS-CoV-2 – A solid waste perspective

“…AI has emerged as a sustainable approach in the era of pandemics of COVID-19 by presenting smart solutions not only in the prevention and diagnosis of the virus but also providing innovative sorting processes in waste management ( Król et al, 2016 ). Proper identification, aggregation, classification, storage, transportation, recycling and disposal, in addition disinfection of the associated waste, personnel protection, and training can be effectively and efficiently managed by implanting AI with the existing technologies such as machine learning and Internet of Things (IoT) ( Anh Khoa et al, 2020 ). To serve this purpose, several models are used such as Adaptive Neurofuzzy Inference System (ANFIS), Support Vector Machine (SVM), Genetic Algorithm (GA) and Artificial Neural Network (ANN), that mimics human traits such as learning, problem solving, understanding, perception, reasoning and awareness of surroundings.…”

Solid waste management during COVID-19 pandemic: Recovery techniques and responses

“…In the time of COVID-19 pandemic, AI can be used as sustainable smart approach for preventing and diagnosing virus as well as providing sorting processes in waste management ( Król et al, 2016 ). AI establishment can effectively manage the waste management process, such as waste identifying, classifying, storage, transporting, recycling and disposal ( Anh Khoa et al, 2020 ). Artificial Neural Network ( ANN), Genetic Algorithm (GA), Support Vector Machine (SVM) and Adaptive Neuro fuzzy Inference System (ANFIS) are some models, which are used for this purpose ( Younes et al, 2015 ).…”

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

“…Computer vision is not the only way in which AI may be applied within smart waste management systems: Convolutional Neural Networks (CNNs) [ 42 , 43 , 44 ], decision forest regression models [ 45 ] or random forest classifiers [ 46 ] are some other examples. Filling level of public waste bins may be also estimated by making use of logistic regression models set up over machine learning techniques [ 47 ] or combined with graph theory [ 48 ]: these are other methods so as to optimize routes during depletion procedures. In addition, such an optimization problem may be also worked out by adopting deep neuroevolutionary techniques so as to build up recurrent neural networks predicting the waste generation in a robust fashion (i.e., by taking into account uncertainty) [ 49 ] or even by employing data analytics platforms [ 50 ].…”