CleanCity-BinSense：一种具有可配置实时填充监测和最近邻路线优化的物联网智能废物管理系统
CleanCity-BinSense: An IoT-Enabled Smart Waste Management System with Configurable Real-Time Fill Monitoring and Nearest-Neighbor Route Optimization
- Islamic University of Technology（伊斯兰理工大学）
- Jahangirnagar University（贾汉吉尔纳加尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对发展中城市废物收集低效问题，提出CleanCity-BinSense物联网智能废物管理系统，采用可配置传感模型和最近邻路线优化，实现低成本实时监测与需求驱动收集。
AI中文摘要：
CleanCity-BinSense解决了发展中城市废物管理中的低效问题，在这些城市中，固定时间表的收集路线导致垃圾箱溢出和燃料浪费。本文介绍了CleanCity-BinSense，一种低成本、端到端的物联网智能废物管理系统，用于可扩展的实时废物监测和需求驱动的收集。该系统集成了一个太阳能供电的传感器节点和超声波传感器，用于实时垃圾箱填充水平监测。一个关键贡献是基于两个校准参数FULL_DISTANCE和EMPTY_DISTANCE的可配置传感模型，使得无需固件修改即可部署在不同尺寸和几何形状的垃圾箱上。填充百分比使用几何可配置的线性归一化算法计算，通过硬件实验验证，平均绝对误差（MAE）为0.38厘米，在制造商规定的传感器容差范围内。传感器读数通过Wi-Fi传输到集中式网络平台，为管理员、操作员和司机提供基于角色的仪表板，以及公共实时垃圾箱状态地图。该系统还采用了一种轻量级最近邻路线规划算法，使用SQL Server的空间函数生成基于邻近度的收集路线，计算开销低。实验评估显示，平均端到端系统延迟为5.3秒，主要由传感间隔而非网络开销主导，而典型城市收集区的路线生成在100毫秒内完成。这些结果证明了在资源受限环境（如孟加拉国达卡）中，为异构城市废物网络部署低成本、可配置、基础设施轻量化的智能废物管理系统的可行性。
英文摘要：
CleanCity-BinSense addresses inefficiencies in urban waste management in developing cities, where fixed-schedule collection routes lead to overflowing bins and wasted fuel. This paper presents CleanCity-BinSense, a low-cost, end-to-end IoT-enabled smart waste management system for scalable real-time waste monitoring and demand-driven collection. The system integrates a solar-powered sensor node with an ultrasonic sensor for real-time bin fill-level monitoring. A key contribution is a configurable sensing model based on two calibration parameters, FULL_DISTANCE and EMPTY_DISTANCE, enabling deployment across bins of varying sizes and geometries without firmware modification. Fill percentage is computed using a geometry-configurable linear normalization algorithm, validated through hardware experiments with a mean absolute error (MAE) of 0.38 cm, within the manufacturer-specified sensor tolerance. Sensor readings are transmitted via Wi-Fi to a centralized web platform providing role-based dashboards for administrators, operators, and drivers, along with a public real-time bin-status map. The system also incorporates a lightweight nearest-neighbor route planning algorithm using SQL Server's spatial function to generate proximity-based collection routes with low computational overhead. Experimental evaluation shows an average end-to-end system latency of 5.3 seconds, dominated by the sensing interval rather than network overhead, while route generation for typical urban collection zones completes in under 100 ms. These results demonstrate the feasibility of a low-cost, configurable, infrastructure-light smart waste management system for heterogeneous urban waste networks in resource-constrained environments such as Dhaka, Bangladesh.
