Our smart city project features an innovative IoT device designed for environmental monitoring in urban areas. Central to this device is the Pycom LoPy4 board, equipped with an ESP32 chipset and programmed using MicroPython, running on FreeRTOS for efficiency. The device is fitted with sensors to measure air quality, specifically PM2.5 and CO2 levels, and it also monitors noise pollution and detects structural movements, important for urban health and safety. The device is powered by a Voltaic solar panel, supporting eco-friendly operations with renewable energy. It includes a durable Li-Ion battery, managed by a Texas Instruments controller for effective power usage. An integrated algorithm optimizes the use of solar energy, adjusting to varying sunlight conditions. Data handling in this device is efficient, with algorithms that compress sensor data to conserve power. It transmits environmental data reliably using BLE and LoRa technologies, contributing to smarter and more environmentally conscious urban management.
At the core of our IoT device is the Pycom LoPy4 board, which operates using an ESP32 chipset.
This board is programmed with MicroPython, chosen for its effectiveness and user-friendly nature for developing the device’s software. This advanced programming is backed by FreeRTOS, a real-time operating system pre-installed on the ESP32. FreeRTOS handles essential tasks like scheduling activities in real time and allocating resources efficiently. Our MicroPython code interacts with the hardware, sensors, and communication methods while FreeRTOS manages background operations.
The device employs the Panasonic SN-GCJA5 sensor for PM2.5 detection.
This sensor utilizes laser technology for precise measurement of particulate matter, even particles as small as 0.3μm. An internal fan assists in particle detection, and the sensor offers real-time data updates. Its compact design and prolonged lifespan make it suitable for space-efficient devices.
CO2 levels are monitored using the S-300-3V 2000PPM UART I2C ELT SENSOR.
This sensor uses Non-Dispersive Infrared (NDIR) technology to provide accurate CO2 readings. It functions effectively over various temperature and humidity conditions and offers a compact size for easy integration. Its communication versatility (I2C and UART) aligns well with our device’s design.
Noise and Shock Detection
The EC1080 sensor in our device is capable of detecting noise pollution and shock events.
It operates efficiently in a low-power mode, activating to measure noise or motion based on specific thresholds. This functionality is essential for monitoring urban environments for noise pollution and structural disturbances, contributing to battery conservation and device longevity.
Humidity and Temperature
We also integrated the SHTC3 sensor for advanced humidity and temperature measurements.
It features Sensirion’s CMOSens® Technology, combining a single chip’s humidity and temperature sensors. The sensor’s wide measurement range, high accuracy, and low power consumption were integral to our choice.
The device’s power source is a Voltaic 5-watt, 6-volt solar panel, selected for its efficiency and outdoor suitability.
The panel is lightweight, waterproof, and UV-resistant, ensuring durability. It is compact (148 x 223 x 4 mm) and does not add significant bulk. The panel’s peak output of 5.75 Watts at 6.12V more than suffices for the device’s power needs, even after adjusting the voltage to the device’s 3.6V operational level.
Battery and Charging Management
A 5200mAh Li-Ion battery was chosen for its high energy storage capacity and compact size.
The battery’s charging process is managed by the Texas Instruments BQ24259 controller, which efficiently handles different charging stages.
This process enhances the battery’s lifespan and ensures a consistent power supply to the device.
MPPT Algorithm Integration
The device includes a specially developed Maximum Power Point Tracking (MPPT) algorithm in its firmware. This algorithm continually adjusts the solar panel’s load to ensure operation at the Maximum Power Point, maximizing energy harvesting. It adapts to changes in solar intensity and other environmental factors, ensuring efficient power extraction. The algorithm also effectively manages the voltage transformation from the panel’s 6.12V output to the device’s 3.6V operation.
Custom algorithms for sensor data normalization and compression have been implemented. These algorithms optimize the size of the data for transmission, playing an important role in conserving power during wireless communication.
Data transmission utilizes BLE for short-range, power-efficient communication and LoRa for longer-range transmissions. This dual-method approach provides flexibility in reliable and adaptable data transmission in various urban scenarios.