🛠 Technology Stack

  • Target Hardware: Raspberry Pi 4 Model B
  • Sensing Hardware: ADXL345 (3-Axis Digital Accelerometer)
  • Languages: C++ (Qt 6), Embedded C (Paho MQTT), Bash
  • Protocols: MQTT over SSL/TLS (Port 8883), I2C, JSON
  • Cloud Infrastructure: HiveMQ Cloud Broker
  • Networking: Tailscale Mesh VPN, Pi-hole (DNS Security)

The Project Overview

Engineered a secure, end-to-end telemetry pipeline designed to monitor and synchronize high-G impact data from edge devices to a centralized monitoring station. This project bridges the gap between register-level sensor interfacing on Linux and high-level data visualization using the Qt framework. The system is hardened with industry-standard encryption to ensure data integrity across public networks.

System Logic & Implementation

The architecture is divided into a high-performance Edge Firmware and a reactive Telemetry Dashboard:

1. Edge Firmware (Impact Detection)

The Raspberry Pi interacts with the ADXL345 via a custom I2C driver. To optimize performance, the system utilizes a “High-G” threshold interrupt.

  • Data Processing: Raw 16-bit acceleration data is converted using Two’s Complement sign-extension and scaled to G-force units.
  • Cloud Synchronization: When an impact exceeds the 1.5G threshold, the firmware packages the event into a JSON payload and publishes it to a secure HiveMQ cluster.

2. Qt 6 Dashboard (Asynchronous Visualization)

The monitoring suite is built in C++ (Qt 6) and operates on a non-blocking event loop:

  • Secure Handshake: Implements a manual SSL/TLS handshake (TLS v1.2) utilizing Server Name Indication (SNI) and ALPN protocols to bypass strict cloud firewalls.
  • Live Telemetry: Features a dynamic scrolling chart that renders X, Y, and Z axes simultaneously.
  • JSON Pipeline: An asynchronous parser decrypts and flattens incoming MQTT messages into UI-ready data structures without locking the main thread.

Network & Security Hardening

A significant portion of development was dedicated to ensuring the telemetry pipeline could operate within a secured home/enterprise network:

  • DNS Whitelisting: Configured Pi-hole regex rules to allow encrypted telemetry traffic while blocking standard ad-trackers.
  • Proxy Bypass: Since the system often operates behind a Tailscale VPN, the Qt application was programmed to bypass virtual network proxies using QNetworkProxy::NoProxy to ensure direct, low-latency socket communication.
  • Port 8883 Enforcement: All traffic is strictly routed through Port 8883 (MQTTS), preventing packet sniffing and man-in-the-middle attacks.

Live Execution Logs

1. Edge Firmware Output (Raspberry Pi):

renode — firmware_audit.log

vamsi-kiran@ASUS:~$ ./firmware_app

Connecting to ssl://40db01641ba149dc91182576560ea139.s1.eu.hivemq.cloud:8883...
Connected to the broker. RTC Time has been synced!
Logger active: Waiting for Vibration...

[LOGGED] IMPACT DETECTED! X:0.25g Y:0.74g Z:1.09g
--- Attempting Cloud Sync... ---
[LOGGED] IMPACT DETECTED! X:0.12g Y:-0.14g Z:1.93g
--- Attempting Cloud Sync... ---
[LOGGED] IMPACT DETECTED! X:-0.05g Y:-0.82g Z:0.58g

2. Dashboard Output (Linux Monitoring Station):

renode — firmware_audit.log

vamsi-kiran@ASUS:~$ ./AssetTrackerDashboard

>>> Opening TCP Socket to Port 8883...
>>> TCP Connected. Starting SSL Handshake...
>>> SUCCESS: Tunnel Encrypted! Sending MQTT Login...
MQTT Login Successful!
Subscribed to topic: asset_tracker/#
Incoming Data: {"x":0.25, "y":0.74, "z":1.09} -> Rendering Frame.

Dashboard Live View

Key Learnings

  • SSL/TLS Protocol Nuances: Gained deep experience in debugging handshake failures, specifically managing SNI (Server Name Indication) and ALPN tags required by AWS-backed load balancers.
  • Embedded Data Integrity: Learned to handle I2C register-level timing and sign-extension for MEMS sensors, ensuring that physical motion translates accurately to digital telemetry.
  • Network Interoperability: Solved complex routing issues involving Pi-hole DNS sinkholes and Tailscale VPN interference, a critical skill for deploying IoT devices in real-world environments.
  • Qt UI Performance: Mastered the use of QtCharts and signal-slot mechanisms to handle high-frequency data bursts without causing UI latency.

View Source on GitHub