Context-Aware Synthetic IoT Dataset for Smart Waste Management in Dhaka, Bangladesh: 43.8 Million Hourly Observations

This dataset provides a one-year, context-aware synthetic IoT benchmark for smart waste-management research in Dhaka, Bangladesh. It represents 5,000 persistent synthetic smart-bin digital twins monitored hourly from 1 January to 31 December 2025, yielding exactly 43,800,000 hourly observations. The simulation incorporates land-use-specific activity, synthetic catchment population, population density, institutional operating status, market and recreation patterns, source-specific waste composition, informal material recovery, seasonal environmental behaviour, cultural-event scenarios, collection accessibility, overflow dynamics, and IoT sensor behaviour. The simulated urban contexts include residential and high-density settlements, fish markets, vegetable markets, general markets, commercial areas, restaurant and food zones, educational institutions, offices, garment factories, parks, playgrounds, recreation areas, and informal settlements. Waste composition is represented using source-dependent components rather than a single generic waste category. Waste mass and volume are modeled separately, enabling composition-dependent bin-filling and overflow behaviour. Contextual temporal patterns include institutional closure effects, market activity periods, recreation-area weekend behaviour, Friday-related activity, Ramadan and Eid scenarios, and annual weather seasonality. IoT-related variables include true and sensor-observed fill states, waste-generation and composition variables, collection and accessibility variables, overflow-related variables, battery telemetry, controlled sensor noise, packet-loss indicators, and anomaly indicators. The dataset is entirely synthetic and scenario-based. It is intended as a reusable urban digital-twin benchmark and should not be interpreted as field-observed municipal smart-bin measurements from Dhaka. The public release is distributed in Parquet format. The deposited archive contains the partitioned representation of the complete 43.8-million-observation dataset, and all partitions together constitute one logical dataset.本数据集为孟加拉国达卡市的智能废物管理研究提供了一款为期一年、具备上下文感知能力的合成物联网（IoT）基准数据集。该数据集包含5000个持久化的合成智能垃圾箱数字孪生体，于2025年1月1日至12月31日期间按小时进行持续监测，累计生成精准的4380万条小时级观测样本。本次模拟整合了土地利用特异性活动、合成垃圾收集服务区人口、人口密度、机构运营状态、市场与休闲活动模式、源特异性废物成分、非正规物资回收、季节性环境行为、文化事件场景、收运可达性、溢出动态以及物联网传感器行为等核心要素。模拟覆盖的城市场景包括居民区与高密度聚居区、水产市场、蔬菜市场、综合市场、商业区、餐饮与食品专区、教育机构、办公楼、制衣厂、公园、运动场、休闲区域以及非正规聚居区。废物成分采用源相关组分进行表征，而非单一通用废物类别。废物质量与体积分别建模，可实现基于成分的垃圾箱装填与溢出行为模拟。上下文时间模式涵盖机构停业影响、市场活动时段、休闲区域周末行为、周五相关活动、斋月与开斋节场景以及年度天气季节性变化。物联网相关变量包括真实装填状态与传感器观测装填状态、废物产生与成分变量、收运与可达性变量、溢出相关变量、电池遥测数据、可控传感器噪声、数据包丢失指示器以及异常指示器。本数据集完全为合成生成且基于场景构建，旨在作为可复用的城市数字孪生基准数据集，不得被解读为孟加拉国达卡市的实地观测市政智能垃圾箱实测数据。公开版本以Parquet格式进行分发。存档文件包含完整4380万条观测数据集的分区存储形式，所有分区共同构成一个逻辑完整的数据集。