A High Fidelity Synthetic IoT Dataset for Smart Waste Management (10 Million Records)
Description
This dataset provides 10 million simulated records of smart waste bin levels across major neighborhoods in Dhaka, Bangladesh (Gulshan, Motijheel, Dhanmondi, Mirpur, and Old Dhaka). Unlike generic datasets, this simulation incorporates regional socio-cultural factors, such as the "Friday Mosque Effect" (spikes in waste generation during prayer times), weekend patterns (Friday to Saturday), and extreme seasonal events like Eid-ul-Adha. The data also includes real-world IoT challenges, such as sensor noise, battery depletion patterns influenced by weather conditions, and missing data points resulting from connectivity failures. It is designed for researchers working on time-series forecasting, anomaly detection, and smart city logistics. Key Features: 1. 10 Million Records: High-volume time-series data. 2. Spatial Integrity: 5,000 unique bin IDs locked to specific Dhaka coordinates. 3. Socio-Cultural Seasonality: Includes the "Friday Mosque Effect" and "Eid-ul-Adha" seasonal spikes. 4. IoT Realism: Simulated sensor noise, battery drain, and connectivity failures. 5. Target Variables: Dual targets for ML Classification (needs_collection) and Regression (overflow_kg).
Files
Steps to reproduce
1. Simulation Environment & Framework: The dataset was generated using a custom-built Digital Twin Simulation Engine developed in Python 3.x. The primary libraries used for data synthesis and structural organization were Pandas for dataframe manipulation and NumPy for stochastic modeling and noise injection. 2. Spatial and Metadata Initialization: A bin registry of 5,000 unique IoT-enabled waste bins was initialized. Each bin was assigned fixed geospatial coordinates (Latitude/Longitude) within five major functional zones of Dhaka, Bangladesh: Gulshan/Banani (Commercial), Motijheel (Office), Dhanmondi (Residential), Mirpur (Mixed/Slum), and Old Dhaka (Dense Market). Metadata such as bin_capacity, placement (e.g., roadside, market), and population_density were locked to each bin_id. 3. Temporal and Seasonal Modeling: The simulation ran for 84 consecutive days at an hourly resolution. To ensure contextual realism, three specific temporal layers were applied: Weekly Cycle: Waste generation rates were multiplied by a factor of 8-10x for bins near mosques during Friday prayer hours (12:00–14:00). Annual Seasonality: A simulated festival window (Eid-ul-Adha) was modeled with a 12x multiplier for residential areas to mimic ritualistic waste spikes. Weather Effects: Hourly temperature and humidity were generated based on Dhaka’s tropical climate, directly influencing the battery_level discharge rate. 4. Stochastic Generation & Noise Injection: Waste accumulation followed a base growth rate influenced by the area_type. To simulate real-world IoT sensor behavior: Gaussian Noise: A normal distribution ($\sigma = 1.5$) was added to the fill_level. Outlier Generation: A 0.1% probability was set for sensor malfunctions (e.g., stuck values or impossible readings like -10% or 150%). Packet Loss: A 0.5% random data dropout was simulated to mimic network latency. 5. Export & Formatting: The final 10-million-row dataset was exported in both CSV (for universal access) and Apache Parquet (columnar format) to optimize for Big Data processing and Machine Learning workflows.