Wi-Fi Latency Tracker

A real-time IoT visualization system that demonstrates the implementation of a complete sensor data monitoring solution. The project showcases the integration of embedded IoT devices with modern data visualization technologies, creating a production-ready dashboard for network performance monitoring.

The system continuously measures Wi-Fi network latency and device temperature, providing microsecond-precision data visualization through a comprehensive Grafana dashboard. For the time series storage and visualization infrastructure, I have explained already how I used Docker for both InfluxDB and Grafana in my OpenSky project.

Project Goal: While this implementation uses a Raspberry Pi Pico W as the sensor node, the architecture is hardware-agnostic and can be adapted to any IoT device capable of Wi-Fi communication, such as ESP32 or similar microcontrollers. The focus is on demonstrating a complete IoT data pipeline from sensor to visualization.

The system implements a robust data pipeline:

• IoT Device → Data Collection → InfluxDB → Grafana Dashboard
• Containerized services for consistent deployment across environments
• Automated data ingestion and real-time processing capabilities

The time series database schema is optimized for network monitoring metrics:

measurement: wifi_measurements
tags:
  - host: PicoW
fields:
  - rtt_avg (microseconds)
  - rtt_min (microseconds)
  - rtt_max (microseconds)
  - jitter (microseconds)
  - loss (percentage)
  - temperature (Celsius)

The core ICMP implementation provides precise latency measurements:

typedef struct __attribute__((packed)) {
    uint8_t type;
    uint8_t code;
    uint16_t checksum;
    uint16_t id;
    uint16_t sequence;
    uint32_t timestamp;
} ICMP_EchoHeader_t;

This implementation uses a Raspberry Pi Pico W as the sensor node, but the system is designed to work with any Wi-Fi-capable IoT device. The architecture abstracts the hardware layer, making it easy to adapt to different platforms.

Raspberry Pi Pico W Hardware Platform

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Key hardware features utilized:

• Wi-Fi connectivity - CYW43439 wireless chip for data transmission
• Internal temperature measurement - via ADC for environmental monitoring
• Low power design - optimized for continuous IoT operation