PALROC - Wearable IoT Gas Meter
My recent independent project involves the conceptualization, design, and prototyping of a complete, end-to-end IoT system: a multi-sensor gas and particle meter aimed at enhancing safety for first responders. This project demonstrates a broad scope of capabilities across the entire IoT technology stack:
Hardware Design & Prototyping:
The scope included the complete hardware development cycle for the portable sensing device. This involved selecting appropriate gas, LEL (Lower Explosive Limit), and particulate matter sensors, designing the schematic and multi-layer PCB using KiCad, and choosing the core processing and communication component – a Nordic Semiconductor nRF9151 System-in-Package (SiP) optimized for low-power cellular IoT applications. Prototyping involved PCB assembly and initial hardware validation.
Embedded Firmware Development:
Significant effort was dedicated to the firmware, developed in C/C++ on the Zephyr RTOS. The scope encompassed interfacing with multiple complex sensors, managing device power states for low-power operation, implementing the LTE-M cellular communication stack (including module control via AT commands ), and handling data packetization using protocols like MQTT over TCP/IP.
Wireless Connectivity & Networking:
I am a resilient professional, committed to continuous learning and adaptation in this rapidly evolving technological landscape. I consistently stay abreast of the latest developments in electronics and robotics, and I leverage AI as a tool to enhance my work efficiency.
Cloud Backend & Data Processing:
Ingestion: Configuring a HiveMQ MQTT broker to receive real-time data from the devices.
Storage: Utilizing a TimescaleDB time-series database, suitable for handling large volumes of timestamped sensor data, hosted on Oracle Cloud Infrastructure (OCI).
Processing: Developing server-side logic within the OCI environment to process, analyze, and potentially trigger alerts based on the incoming sensor readings.
Data Access:Creating APIs to allow processed data to be securely accessed.
Frontend Application & Visualization:
To make the data useful, a web-based dashboard and landing page were developed using Node.js. This interface consumed the APIs to plot sensor readings and device locations, providing a visualization platform for monitoring personnel safety.
Development Operations (DevOps):
Implemented DevOps practices using Git for version control and Docker for containerization. While initially utilizing a local Linux homelab for setting up build/test environments, the project's backend services (HiveMQ, TimescaleDB, Node.js application) were subsequently deployed, hosted, and tested more extensively on Oracle Cloud Infrastructure (OCI) for greater practicality and scalability.
