Connect & Use NEO-6M GPS Module with Arduino: Tutorial

This tutorial demonstrates how to build a GPS data logger using an Arduino microcontroller, Adafruit Ultimate GPS module, and Virtualbotix SD card reader. The system continuously logs GPS coordinates (latitude, longitude, and altitude) along with raw NMEA sentences to an SD card when the GPS has a satellite fix. The project involves connecting the GPS to pins 2 and 3 of the Arduino, configuring the SD card reader with SPI communication, checking for existing files before logging, and writing data only after achieving a GPS fix. The logged data can be viewed in Google Earth by converting decimal-degree coordinates to degrees-minutes format.

This tutorial demonstrates how to build a GPS data logger using an Arduino Uno, GPS shield, and micro-SD card module, which records location coordinates (latitude and longitude) every five seconds along with date and time, storing the data in a comma-separated file on the SD card; the LED indicator on the GPS shield provides visual status feedback—steady light indicates SD card issues, rapid flashing means no GPS fix, and slow blinking confirms successful data logging; the recorded GPS data can be converted to a .kml file using GPS Visualizer and visualized on Google Earth to track movement paths, such as a kayak journey covering approximately 6 kilometers.

This tutorial demonstrates how to log GPS data to an SD card using Arduino by connecting a Neo-7M GPS module via UART and an SD card via SPI, parsing the NMEA RMC sentences to extract time, position, velocity, and date information, and writing the data to a CSV file for spreadsheet analysis.

This tutorial demonstrates how to build a GPS tracking system using an Arduino Mega, GPS shield, and SD card to log latitude and longitude coordinates to a CSV file. The system uses the TinyGPS library to parse NMEA 0183 data from the GPS module, which transmits satellite position information at 4800 baud. The Arduino Mega is preferred over the Uno because it has multiple hardware serial ports, allowing simultaneous debugging and GPS communication. The logged data can be visualized using Google Fusion Tables or Google Earth by importing the CSV file, which contains timestamped GPS coordinates that can be plotted as a map overlay showing the tracked path.

This tutorial demonstrates how to connect and program an SD card module with Arduino Uno using SPI protocol, covering voltage level considerations (3.3V module with 5V Arduino requires regulator and level shifter), pin connections (MISO to pin 12, MOSI to pin 11, SCK to pin 13, CS to pin 10), and basic file operations (create, write, read) using the SD.h library for data logging and storage applications.

A GPS tracking system can be built using LoRa (Long Range) wireless communication technology instead of GSM, enabling reliable tracking in areas without cellular coverage such as mountains or wilderness areas. The system consists of a transmitter with a microcontroller, GPS module, and LoRa module that sends location data, and a receiver that forwards the data to a smartphone application for visualization. The Android app uses Unity, OpenStreetMap with Leaflet, and serial communication to display the tracked object's location and movement history.

The A9G module is a compact IoT board (40x25mm) featuring an integrated RDA8955 chip with built-in quad-band GSM/GPRS (Class 10) and GPS capabilities, along with on-board microphone, speaker connector, battery charging circuit, and 29 GPIO pins; it supports standard AT commands for making calls, sending/receiving SMS, retrieving GPS coordinates, performing HTTP GET requests, and offers power-saving modes including low-power GPS (30mA vs 60mA normal) and sleep mode (17-27mA) to minimize battery consumption for portable GPS tracker applications.

This video demonstrates how to build a low-power GPS tracker using LoRa technology, where a transmitter (using ATmega328 microcontroller, SX1276 LoRa module, and L80 GPS module) sends location data to a receiver (ESP8266 with SX1276), which then forwards the information to the Blynk server for real-time Google Maps visualization; the system employs sleep mode functionality to conserve battery power and uses a 3.7V lithium-ion battery with BMS IC for energy-efficient operation.

This video demonstrates how to create a GPS/GPRS tracking system using the A9G module (which combines GPRS and GPS functionality) with an ESP32 LoRa board. The system uses UART serial communication to transmit GPS coordinates and location data wirelessly. The presenter shows the hardware setup, including a lithium battery with voltage regulator, and explains the software implementation using Arduino libraries for GPS data parsing and display. The video also presents a real-world case study of CASAN's water treatment monitoring system in Florianópolis, which uses similar technology to monitor 430 treatment facilities with 9,770 endpoints, demonstrating how IoT technology can be applied for environmental protection and infrastructure management.

The A7 module represents an integrated solution combining GSM cellular communication and GPS positioning capabilities in a single compact device priced under $10. This integration enables cost-effective GPS tracking applications when combined with microcontrollers like Arduino Pro Mini or ESP8266. The module requires two serial connections for full functionality: one at 115,200 bps for general control and another at 9,600 bps for GPS data output. Power supply operates within 3.3V to 4.2V range, compatible with standard power banks. This technology enables affordable GPS tracking systems suitable for logistics and asset monitoring applications.

MPLleaflet is a Python library that enables embedding interactive OpenStreetMap maps directly in Jupyter notebooks or web pages. It integrates with matplotlib plots and converts them into interactive slippy maps similar to those found on web mapping services. The library requires tracks to be simplified first (ideally using Douglas-Peucker) because large datasets cause performance issues. Usage involves creating a matplotlib plot of the GPS track, then calling mplleaflet.display() to embed it as an interactive widget. This provides zoomable, clickable maps with minimal code—typically just two lines—to visualize GPS data in customer-friendly presentations.

GPS point data is plotted using addCircleMarkers() with ~lon and ~lat specifying longitude and latitude columns. This is useful for tracking animal movements, incident locations, or any point-based spatial data where each observation has coordinates.

This comprehensive tutorial covers the end-to-end process of building a web application that visualizes GPX track data on a Leaflet map. The project demonstrates key software engineering practices including creating reusable classes with proper encapsulation, defining typed properties for map and location data, implementing constructors with default parameters, and exporting classes for module usage. The application integrates Leaflet map initialization with GPX file parsing to extract geographic coordinates, storing them as arrays of two-dimensional points for polyline creation. The workflow shows how to verify extracted data through console logging before proceeding to visualization, establishing a complete development pipeline from data parsing to map rendering.

Google Fusion Tables is a web-based tool that can import and visualize GPS data from CSV files. The tool automatically detects comma-separated data and can map latitude and longitude coordinates. Users can create maps showing the path taken by a GPS device, with the ability to view the data in tabular format or as a visual map. The tool requires a Google account and can handle time-stamped GPS data for creating tracks.

To visualize GPS logs on Google Maps: Go to Google Maps, access 'Your Places' > 'Maps', create a new map, import the CSV file, specify latitude and longitude columns as location data, and assign timestamps as labels. Google Maps processes the CSV and displays the recorded path as a series of connected points. The example demonstrates tracking a park walk, showing how the GPS logger accurately captured the path despite some variations in wooded areas where signal quality might degrade.

This tutorial demonstrates how to interface an SSD1306 128x32 OLED display with Arduino using I2C communication. The process involves downloading and installing the Adafruit SSD1306 library, connecting the display's four pins (GND, VCC, SCL to A5, SDA to A4), configuring the display resolution (128x32), setting the I2C address (typically 0x3C), and using the library functions to display text, shapes, and live sensor values on the OLED screen.

To connect an I2C LCD display to an Arduino Uno, wire the display's ground to Uno's ground, VCC to 5V, SDA to pin A4, and SCL to pin A5; install the New LiquidCrystal library, use the I2C scanner sketch to find the device address, then initialize the LCD with that address and appropriate pin configurations in your Arduino code.

To connect an I2C OLED display (such as the SSD1306 128x64 model) to an Arduino Uno, you need to install the Adafruit SSD1306 and Adafruit GFX libraries from GitHub, wire the display using VCC, GND, SDA, and SCL pins, and adjust the I2C address in the code from the default 0x3D to 0x3C if necessary; the process typically takes around 15-30 minutes and requires troubleshooting common issues like pull-up resistors and address configuration.

The I2C interface for LCD displays reduces Arduino pin usage from 6 to 2 pins (SDA and SCL) by using the PCF8574 chip, which manages all screen connections; the display can be controlled using the LiquidCrystal_I2C library with methods like lcd.init(), lcd.backlight(), and lcd.print(), while backlight brightness can be adjusted using PWM pins.

An I2C interface module simplifies LCD screen connections by reducing wiring from 12-16 pins to just 4 pins (Ground, VCC, SDA, SCL), which connect to Arduino's analog pins A4 and A5; the module includes a potentiometer for adjusting screen contrast and a jumper for backlight control, and can be programmed using the LiquidCrystal_I2C library in Arduino IDE.