Department of Civil Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia.
Faculty of Architecture and Planning, Landscape Architecture Department, King Abdulaziz University, Jeddah, Saudi Arabia.
PLoS One. 2024 Jul 31;19(7):e0307608. doi: 10.1371/journal.pone.0307608. eCollection 2024.
Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0.
废物管理是全球城市面临的主要挑战，对环境、经济和社会都有重大影响。本文提出了一种利用物联网（IoT）、算法和云分析最新进展的新型废物管理系统，以实现智慧城市中更高效、可持续和环保的废物收集和处理。设计了一个超声传感器原型，用于可靠的满罐监测。LoRaWAN 和蜂窝网络架构提供全市范围的连接。云平台处理传感器数据存储、处理和分析。动态路线优化算法根据实时垃圾桶数据最小化时间、距离和燃料使用。在巴基斯坦拉合尔的 10 个不同地点进行了广泛的试点研究，验证了该系统，处理了超过 2 亿个数据点。结果表明，路线效率提高了 32%，燃料消耗和排放减少了 29%，废物处理吞吐量增加了 33%，与传统做法相比，车辆维护节省了 18%。这表明在运营、经济和可持续性方面都有可量化的收益。所提出的物联网支持的废物管理系统代表了在全球智慧城市中实现可持续和生态责任废物管理的重大进展。这项研究为智慧城市整体解决方案提供了一个可复制的模型，该模型集成了传感、算法和分析，将公民运营转变为数据驱动、高效的模式。它代表了全球智慧城市可持续废物管理的重大进展。进一步的工作可以应用新兴技术，如自动化和人工智能，以创建废物管理 3.0。
