We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts.
Distributed ledger technology is an immutable data storage and transparent system, which is a constituent component that empowers the FSCM (Food supply Chain Management). Due to the autonomous and immutable data feature, the scalability of blockchain technology is quite a challenge. A supply chain is a network that flows product from suppliers to the final consumer, which produces a high volume of data. In the blockchain, processing high volume of data is quite a significant issue and may affect business profit. To overcome the issue, Big Data and User Controllable Code for Smart Contracts (UCCSC) will be integrated with the blockchain-based FSCM system. Big data technology is typically used to analyze high volume of both structured and unstructured data, which is very difficult to process using habitual database and software techniques. The UCCSC integrates the blockchain and big data and manage the user in the FSCM system. This paper mainly discusses the integration of blockchain-based FSCM and Big Data, and the mechanism that supports such system.
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