
neha-priya-1171/sensor-telemetry-pipeline
API developmentこのページは AI により自動翻訳されています。技術的な詳細は原文をご確認ください。 原文を見る
A personal project by a 2nd-year ECE student demonstrating how structured prompt engineering can bridge embedded hardware to cloud backends — without writing a single line of server code from scratch.
概要
A personal project by a 2nd-year ECE student demonstrating how structured prompt engineering can bridge embedded hardware to cloud backends — without writing a single line of server code from scratch. An ESP32 microcontroller reads 6-axis IMU sensor data, timestamps it using NTP, and sends it over Wi-Fi to a FastAPI server hosted on Replit — which validates the data and stores it in a SQLite database. Every packet returns HTTP 201 Created with a sequential record_id. The entire backend was — specifically applying the prompt engineering techniques from my coursework: role framing, iterative refinement, error-driven prompting, and schema anchoring. In Electronics & Communication Engineering, the gap between hardware and software is shrinking fast. Cyber-physical systems, IoT, and smart devices are increasingly central to the field — and they all require this exact stack: a microcontroller producing data, a network layer transporting it, and a backend storing it.
README
以下は原文 README の抜粋です。原文を見る。
ESP32 IoT Telemetry Pipeline
Built entirely with AI — no prior backend experience required.
A personal project by a 2nd-year ECE student demonstrating how structured prompt engineering can bridge embedded hardware to cloud backends — without writing a single line of server code from scratch.
What is this?
An ESP32 microcontroller reads 6-axis IMU sensor data, timestamps it using NTP, and sends it over Wi-Fi to a FastAPI server hosted on Replit — which validates the data and stores it in a SQLite database. Every packet returns HTTP 201 Created with a sequential record_id.
The entire backend was scaffolded, debugged, and deployed using AI — specifically applying the prompt engineering techniques from my coursework: role framing, iterative refinement, error-driven prompting, and schema anchoring.
Why this matters for ECE
In Electronics & Communication Engineering, the gap between hardware and software is shrinking fast. Cyber-physical systems, IoT, and smart devices are increasingly central to the field — and they all require this exact stack: a microcontroller producing data, a network layer transporting it, and a backend storing it.
This project proves you don’t need a full software engineering background to build that pipeline. You need to know what to ask for and how to read the errors.
System Architecture
ESP32 Dev Module
│
├── Reads IMU (accel_x/y/z, gyro_x/y/z)
├── Syncs clock via NTP → pool.ntp.org
├── Builds JSON payload with 13-digit epoch timestamp
└── HTTP POST ──► /api/v1/telemetry
│
├── X-API-Key header check
├── Pydantic schema validation
├── Replay attack timestamp check
└── SQLite INSERT → returns record_id
sequenceDiagram
participant ESP32
participant NTP as pool.ntp.org
participant API as FastAPI (Replit)
participant DB as SQLite
ESP32->>NTP: Sync clock on boot
NTP-->>ESP32: Unix epoch (ms)
loop Every 5 seconds
ESP32->>API: POST /api/v1/telemetry + X-API-Key
API->>API: Validate key → schema → timestamp
API->>DB: INSERT record
DB-->>API: record_id
API-->>ESP32: 201 Created {record_id: 286}
end
The Payload Schema
This is the exact JSON structure the Pydantic model expects. Any deviation returns HTTP 422.
{
"device_id": "esp32-node-01",
"firmware_version": "1.0.0",
"timestamp_ms": 1781670054657,
"imu": {
推奨ツール
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インストール
npx skillfish add neha-priya-1171/sensor-telemetry-pipeline