BinSight is an IoT-based system for real-time waste bin monitoring using multi-sensor integration. It enables data-driven waste collection with alerts, analytics, and scalable deployment models.
🗑️ Urban waste management systems rely on manual inspection and fixed schedules, leading to inefficiencies and hygiene risks. ♻️ BinSight introduces a smart, scalable, and data-driven solution using IoT and analytics.
- No real-time visibility into bin fill levels
- Dependence on fixed collection schedules
- Overflowing bins and odor complaints
- Lack of gas and fire hazard detection
- No real-time alerts or notifications when bins / waste zones require servicing
- Inefficient manual monitoring
- Poor workforce utilization
💡 BinSight enables:
- 📊 Real-time fill level tracking
- ☁️ Gas detection for harmful emissions
- 🔥 Flame detection for fire safety and removal of flammable waste
- 📲 Centralized dashboard for tracking with bin / zone & user locations on a live map, bin / zone status cards, and real-time alerts for servicing
- 🔮 Predictive analytics (next overflow time, anomaly detection - gas spike, fire risk) using ML
- Real-time Monitoring: Continuous tracking of waste levels and environmental hazards.
- Multi-sensor Integration: Combines ultrasonic, gas, and flame sensors for 360° safety.
- Predictive Analytics: ML-driven forecasts for bin overflow and cleaning schedules.
- Scalable Design: From individual apartments to city-wide modular campus deployments.
- Modular System: Can be employed for single bins as well as multi-bin waste rooms/shafts.
- Cost-effective: Built using low-power, affordable IoT hardware for maximum ROI.
| 📦 Version | Power | Architecture | Best For | Components |
|---|---|---|---|---|
| Single Bin – Battery + Solar | 🔋 18650 Battery + ☀️ Solar | Device in single bin | Apartments, Retrofits | - ESP32 ✅ - Ultrasonic Sensor ✅ - Gas Sensor ✅ - Flame Sensor ✅ - Battery ✅ - Solar Panel ✅ |
| Single Bin – Mains + Backup + Solar | 5V + 🔋 Battery (+ ☀️ Optional Solar) | Device in single bin | Larger Residential Complexes, Hospitals, etc. | - ESP32 ✅ - Ultrasonic Sensor ✅ - Gas Sensor ✅ - Flame Sensor ✅ - Battery ✅ - Solar Panel |
| Modular System – Mains | 5V Adapter | Central unit + wired sensors | Campuses, Corporate Parks, Malls, Large Event Venues | - Central ESP32 ✅ - Central Gas Sensor ✅ - Ultrasonic Sensors (per bin) ✅ - Flame Sensors (per bin) ✅ - Battery ❌ - Solar Panel ❌ |
- ESP32-WROOM-32
- Ultrasonic Sensor (HC-SR04)
- Gas Sensor (MQ-135)
- Flame Sensor (KY-026)
- Battery (Samsung INR18650-30Q 3.7V 3000mAh Li-NiMnCoO2) + Charge controller / Mains Power System
- Solar Panels (Optional)
- WiFi
- LoRa (optional)
- MQTT Protocol
- Firmware: Arduino / ESP-IDF
- Backend: FastAPI
- Database: MongoDB
- Frontend: Flutter
- Maps: Leaflet.js
- ML: NumPy, Pandas
- Low-Cost Infrastructure: Utilizes affordable, mass-produced sensors and microcontrollers (ESP32), significantly lowering entry barriers.
- Standardized Protocols: Implementation of MQTT and REST APIs ensures compatibility with existing cloud infrastructures.
- Energy Efficiency: Deep-sleep modes in ESP32 allow for long-term battery operation, making the system viable for locations without power mains.
- Modular Scalability: The decoupled architecture (Edge to Cloud) allows for easy expansion from 1 bin to 1000s without re-engineering the core logic.
Sensors → ESP32 Microcontroller → MQTT → FastAPI Backend → MongoDB Database → ML → Application/Dashboard → Alerts (Sensor thresholds crossed)
- Perception Layer: Sensors
- Edge Layer: ESP32
- Network Layer: MQTT/WiFi
- Cloud Layer: Backend + DB
- Intelligence Layer: ML
- Application Layer: UI
- Sensor → ESP32 connections
- GPIO / ADC mapping
- Power regulation
- Initialize sensors
- Connect WiFi
- Read data
- Process
- Publish via MQTT
- Repeat
Topics: bins/{bin_id}/data | bins/{bin_id}/alert
Payload Example: { "fill": 78, "gas": 120, "flame": false }
{ "bin_id": "string", "location": "coords", "fill_level": 0, "gas_level": 0, "flame_status": false, "timestamp": "ISO8601" }
POST /dataGET /binsGET /alerts
- 🗑 Fill Level Prediction: Weighted Moving Average, ARIMA
- 📈 Anomaly Detection: Z-score
- Real-time monitoring of bins/zones.
- Location-based tracking via map.
- Alerts for overflow, gas, and fire hazards.
- ML-based prediction for overflow and anomalies.
- Historical data storage for trend analysis.
- Graphical representation of bin metrics.
- 📍 Live Map: User & Bin/Zone Markers.
- 🗑 Status Monitoring: Bin/Dumping Zone status.
- 📈 Predictive Analytics: Overflow time estimates and anomaly detection.
⚠️ Alerts: Real-time notifications when sensor thresholds crossed.- 👨💻 Management:
- 📝 Task List
- 👷 Worker management (View Workers & Tasks Assigned per Worker, Assign Tasks)
- 🔋 Device management (Locations, Battery Levels)
- 🗂️ Reports:
- Bin/Zone Statuses
- Damaged Bins/Zones (With photos taken & uploaded by workers)
- 🔎 Filters: Filter by Fill, Air Quality, or Flames.
- 📍 Live Map: User & Bin/Zone Markers.
- 🗑 Status Monitoring: Real-time bin/zone status.
- 📈 Predictive Analytics: Overflow time estimates and anomaly detection.
⚠️ Alerts: Real-time notifications when sensor thresholds crossed.- 📝 Task List: View assigned cleaning duties.
- ✅ Confirmation: Feature to mark bins as "Cleaned".
- 🔎 Quick Filters:
- Show Critical Only
- Show Nearby Bins/Zones (Within x radius)
- Show Assigned Tasks
- 📊 Fill Level: %
- ☁️ Air Quality Index
- 🕒 Next Predicted Overflow: “In Y hrs”
- 🚦 Status: Normal / Moderate / Critical
- 🗑 Number of Bins
- 📊 Fill Level
- ☁️ Air Quality Index
- 🕒 Next Predicted Overflow (Bin-specific): "In Y hrs"
- 🚦 Status: Normal / Moderate / Critical
| Status | Threshold | Single Bin Conditions | Zone Conditions |
|---|---|---|---|
| 🟢 Normal | Fill < 70% Gas: Safe Range Flame: None |
- Fill Level < 70% - Gas levels within safe range - No flame detected - No anomalies |
- All bins within safe limits - No gas/fire risk in zone - No anomalies |
| 🟠 Moderate | Fill: 70–90% Overflow < 4 hrs Minor Gas/Fire Risk |
- Fill Level between 70–90% - Predicted overflow in < 4 hrs - Slight increase in gas OR minor fire risk - Non-critical anomaly |
- Any bin predicted to overflow in < 4 hrs - Moderate gas levels in zone - Minor fire risk in any bin - Non-critical anomaly |
| 🔴 Critical | Fill ≥ 90% Gas: Unsafe Flame:Detected |
- Fill Level ≥ 90% (or full) - Gas exceeds safe limits - Flame detected - High-risk anomaly |
- Any bin is full - High gas levels in zone - Flame detected in any bin - Multiple bins in moderate/critical state |
- 🧑🔧 Facility Managers
- Apartments & Residential Complexes
- Hotels, Resorts, & Luxury Gated Communities
- Hospitals & Healthcare Facilities
- Corporate Offices & Industrial Parks
- Airports
- Factories & Industrial Facilities
- Shopping Malls
- Railway Stations, Metro Stations
- Event Venues - Stadiums, Banquet Halls, etc.
- Waste companies
- Municipal bodies
- ESG teams
- TAM: ₹29,000–₹33,000 Cr
- SAM: ₹5,000–₹6,200 Cr
- SOM: ₹58–₹125 Cr
- 🆚 Competitors: Bigbelly, Enevo, Ecube Labs, Nepra, Saahas Zero Waste.
- Limitations: Often only monitor fill levels; limited analytics.
- BinSight Advantage: Multi-sensor integration (fire/gas), predictive ML, and high scalability.
- Lack of real-time comprehensive systems.
- Weak predictive analytics in existing solutions.
- Poor integration between different sensor types.
- Hardware sales (Initial setup).
- SaaS Subscription (Software and analytics access).
- Annual Maintenance Contracts (AMC).
- Power Management: Maintaining long-term battery life for remote bins.
- Sensor Interference: Environmental factors (steam, moisture) affecting ultrasonic readings.
- Network Reliability: Connectivity issues in underground parking or high-density buildings.
- Durability: Protecting hardware against harsh waste environments and physical impact.
- VOC & Humidity Sensors: For precise odor detection and moisture monitoring.
- Smart Route Optimization: AI-driven path planning for collection vehicles based on real-time fill levels.
- Smart City Integration: Direct API integration with municipal waste management grids.
- Computer Vision: Advanced cameras for automated waste segregation at the source.
BinSight delivers a smart, scalable, and efficient waste monitoring solution combining IoT, analytics, and safety systems to modernize urban waste management.