IoT-based Route Recommendation for an Intelligent Waste Management System
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Abstract (arXiv)
The Internet of Things (IoT) is a paradigm characterized by a network of embedded sensors and services. These sensors are incorporated to collect various information, track physical conditions, e.g., waste bins' status, and exchange data with different centralized platforms. The need for such sensors is increasing; however, proliferation of technologies comes with various challenges. For example, how can IoT and its associated data be used to enhance waste management? In smart cities, an efficient waste management system is crucial. Artificial Intelligence (AI) and IoT-enabled approaches can empower cities to manage the waste collection. This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints. It performs a thorough analysis based on AI-based methods and compares their corresponding results. Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account to address the routing problem. Such AI-based models can help engineers design a sustainable infrastructure system.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 51 references that are in the map. First posted on arXiv 1 Jan 2022.
Builds on
6 of 51 references.
References · 6
- 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 75
- 2020AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processescited by 9
- 2020Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challengescited by 5
- 2021Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern based on Socio-economic Determinantscited by 2
- 2021Leveraging Artificial Intelligence to Analyze Citizens' Opinions on Urban Green Spacecited by 1
- 2021Tales of a City: Sentiment Analysis of Urban Green Space in Dublincited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.