An Internet of Things based Waste Management System using Hybrid Machine Learning Technique

Arunkumar, M. S.; Sathishkumar, P; R, Suguna; Deepa, Simon

doi:10.1109/iceca55336.2022.10009242

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Arunkumar et al [21] review the development of an innovative waste management system for smart cities, leveraging the capabilities of the Internet of Things (IoT) and ML. In particular, the study utilizes a hybrid approach called Decision Tree with Extreme Learning Machine (DT-ELM) to analyze city waste data.…”

Section: Modelsmentioning

confidence: 99%

Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection

Gatti,

Barbierato,

Pozzi

2024

Electronics

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This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.

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Section: Modelsmentioning

confidence: 99%