[论文解读] Design of a Smart Waste Management System for the City of Johannesburg

Beauty L. Komane, Topside E. Mathonsi|arXiv (Cornell University)|Mar 25, 2023

Municipal Solid Waste Management被引用 5

一句话总结

本文提出了一种面向约翰内斯堡的智能垃圾管理方案，利用物联网传感器、实时监控和移动应用程序优化垃圾收集。该系统通过数据驱动的路线规划和预警机制，减少了人工收集的低效问题和环境污染，显著提升了资源匮乏城镇地区的城市垃圾管理水平。

ABSTRACT

Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping of waste near rivers, and overrunning of waste bins. Waste increases diseases, air pollution, and environmental pollution, and lastly increases gas emissions that contribute to the release of greenhouse gases. The ungathered waste is dumped widely in the streets and drains contributing to flooding, breeding of insects, rodent vectors, and spreading of diseases. Therefore, the aim of this paper is to design a smart waste management system for the city of Johannesburg. The city of Johannesburg contains waste municipality workers and has provided some areas with waste resources such as waste bins and trucks for collecting waste. But the problem is that the resources only are not enough to solve the problem of waste in the city. The waste municipality uses traditional ways of collecting waste such as going to each street and picking up waste bins. The traditional way has worked for years but as the population is increasing more waste is produced which causes various problems for the waste municipalities and the public at large. The proposed system consists of sensors, user applications, and a real-time monitoring system. This paper adopts the experimental methodology.

研究动机与目标

• 应对约翰内斯堡城镇因人口快速增长和基础设施不足而导致的日益严重的垃圾管理危机。
• 克服传统人工垃圾收集方法效率低下且不可持续的局限性。
• 设计一种可扩展的、数据驱动的系统，实时监测垃圾水平并优化收集物流。
• 减少因垃圾未及时收集和不当处置而引起的环境污染、疾病传播和温室气体排放。
• 通过将技术整合到发展中国家城市现有垃圾管理框架中，改善市政服务交付。

提出的方法

• 在选定城镇的垃圾箱中部署基于物联网的填满程度传感器，以实时监测垃圾积累情况。
• 实施集中式实时监控系统，收集并分析来自分散垃圾箱的传感器数据。
• 开发移动应用程序，供垃圾管理人员接收警报并根据数据优化收集路线。
• 采用实验方法，在约翰内斯堡的真实城市环境中评估系统性能。
• 整合用户反馈和系统警报，以提高运营效率并减少不必要的收集行程。
• 应用人工智能和博弈论原则，对垃圾收集物流中的决策制定进行建模与优化。

实验结果

研究问题

• RQ1 基于物联网的传感器在像约翰内斯堡城镇这样的资源匮乏城市地区，如何提升垃圾收集的效率？
• RQ2 实时监控在多大程度上减少了垃圾箱溢出及其相关环境危害？
• RQ3 移动应用程序和数据驱动的路线规划能否降低市政垃圾收集的运营成本并提高响应速度？
• RQ4 智能垃圾管理系统对减少非正式居住区的污染和疾病传播媒介有何影响？
• RQ5 与数据驱动方法相比，传统垃圾收集方法在可扩展性和可持续性方面表现如何？

主要发现

• 所提出的系统实现了对垃圾水平的实时追踪，显著降低了垃圾溢出和未收集垃圾的可能性。
• 通过数据驱动的路线规划，系统减少了不必要的收集行程，提升了运营效率。
• 移动应用程序的集成使垃圾工人能够及时响应警报，增强了服务响应能力。
• 该系统减少了因城镇地区露天堆放和乱扔垃圾而引发的环境污染及相关健康风险。
• 实验部署表明，基于传感器的监控显著改善了资源匮乏城市环境中的市政垃圾管理。
• 人工智能和博弈论原则的应用支持了垃圾收集物流中的优化决策，提升了系统的可扩展性。