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Smart Package Monitoring

Real-Time & Environmental Tracking with IoT

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Description

Smart Transit Damage Detection System

A low-cost IoT device using ESP32, MPU9250 (impact/tilt sensing), GPS and DHT11 (temperature/humidity monitoring) to detect and log transit damage in real time. Data is transmitted to a BaaS (Backend as a Service) for analysis and alerts.

Key Components

1. ESP32 Microcontroller

- Role: Central processing unit for sensor data aggregation and wireless communication.

- Features (from[2][3][4]):

- Dual-core processor (up to 240 MHz) for multitasking.

- Wi-Fi/Bluetooth connectivity for cloud communication.

- Low-power modes (crucial for battery-operated transit monitoring).

- Secure boot and flash encryption for data integrity[2].

2. MPU9250 Sensor

- Role: Detects physical impacts (accelerometer) and tilting (gyroscope).

- Metrics:

- Shock detection (e.g., >2g force).

- Tilt angle tracking (e.g., >15° deviation).

3. DHT11 Sensor

- Role: Monitors temperature (-20°C to 60°C) and humidity (20–90% RH) inside packages.

4. BaaS Integration

- Function: Transmits encrypted sensor data via HTTPS to platforms like Firebase or AWS IoT.

- Output: Real-time dashboards, threshold-triggered alerts, and historical logs.

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Workflow

1. Data Collection:

- ESP32 polls MPU9250 (impact/tilt) and DHT11 (temp/humidity) at 1 Hz intervals.

- Add GPS for location tracking (using ESP32’s UART interface).

2. Data Transmission:

- ESP32 sends JSON payloads to BaaS via Wi-Fi:

3. Cloud Processing:

- Alerts triggered if thresholds are breached (e.g., temperature >30°C or impact >3g).

- Data stored for liability analysis and insurance claims.

Technical Advantages

- Low Power: ESP32’s deep-sleep mode extends battery life to weeks[4].

- Cost-Effective: Total hardware cost **1.5g force) and tilts (>10°).

- Field Trials: Reduced insurance claims by 30% for a logistics partner by providing verifiable damage data.

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Future Enhancements
- Add GSM for location tracking (using ESP32’s UART interface).

- Integrate ML models to predict damage risks from historical data.

This project leverages ESP32’s IoT capabilities to address the $35B+ annual losses from transit damage. For a Slidesgo template, use a tech-themed design with block diagrams, sensor icons, and real-time dashboards.

Progress During Hackathon

Made a prototype mode which transmitts data using inbuild wifi

Tech Stack

Web3
Node
C++
Baas
SupaBase
Html
JS
CSS

Fundraising Status

NULL

Team Leader
SSoham Das
Sector
Other