iotlabkit — Beginner-Friendly IoT Lab Code Generator

A complete, beginner-friendly Python package and CLI tool designed specifically for MSc / MCA Semester 3 IoT Laboratory practicals.

iotlabkit generates complete, readable, production-grade project code for Programs 5 through 17, supporting two interchangeable presentation modes:

• Learning Mode (--mode learning): Rich architectural diagrams, plain-English comments, troubleshooting tips, function walkthroughs, and viva questions.
• Exam Mode (--mode exam): Concise, minimal, practical code ready for university laboratory examination submission.

---

Table of Contents

1. What is iotlabkit?
2. What Can It Do?
3. Supported Practical Programs
4. Python Installation Guide
5. Creating a Virtual Environment
6. Installing iotlabkit
7. Listing Practicals (list)
8. Inspecting a Practical (info)
9. Generating a Project (create)
10. Installing Project Dependencies
11. Configuring Cloud Services & Credentials
12. Dataset Management & Offline Testing
13. Running Each Practical
14. Running Diagnostics (doctor)
15. Troubleshooting & Common Errors
16. Uninstalling the Package

---

1. What is iotlabkit?

In university IoT laboratory curricula, students often face hurdles configuring diverse communication protocols (HTTP REST, MQTT, CoAP), cloud databases (Supabase, Firebase, ThingsBoard), and machine learning pipelines (Decision Trees, Linear Regression, Random Forests).

iotlabkit solves this by automating the boilerplate generation while maintaining 100% genuine code, real APIs, and authentic datasets. It does not generate fake sensor readings or synthetic ML data. Every project generated is self-contained and immediately runnable.

---

2. What Can It Do?

• Generates complete, runnable projects with a single command: iotlabkit create <id>.
• Supports 13 university practicals across Communication, Analytics, and Machine Learning layers.
• Built-in Pure-Python RFC 7252 CoAP implementation: Runs CoAP client and server on UDP port 56830 with zero external pip dependencies.
• Real live API integrations: Connects directly to Open-Meteo Weather, Open-Meteo Air Quality, USGS Earthquakes, and ISS Orbital coordinates.
• Authentic datasets bundled & downloadable: Pre-bundles balanced genuine subsets of UCI Occupancy (#357), UCI Beijing PM2.5 (#501), and Kaggle India Air Quality (city_day.csv), with official downloaders for the full datasets.
• Dual Presentation Modes: Switch effortlessly between verbose educational code (learning) and concise exam submission code (exam).
• Interactive Web Dashboards: Generates instant Streamlit dashboards for real-time visualization.

---

3. Supported Practical Programs

Strictly implements Programs 5 through 17 from the syllabus. Programs 1–4 and 18 are excluded.

ID	Title	Layer	External Dependencies	Internet Needed?	Cloud Keys?
5	HTTP REST API + Cloud DB + Dashboard	Communication	Flask, Requests, Streamlit	Yes	Yes (Supabase/Firebase)
6	MQTT + Cloud DB + Dashboard	Communication	Paho-MQTT, Requests, Streamlit	Yes	Yes (Supabase/Firebase)
7	ThingsBoard + MQTT Telemetry	Communication	Paho-MQTT, python-dotenv	Yes	Yes (Device Token)
8	CoAP Client and Server	Communication	None (Standard Library)	No	No
9	Online & Local MQTT Brokers	Communication	Paho-MQTT, python-dotenv	Online only	No
10	CoAP Environmental Monitoring	Communication	Requests, Streamlit	Yes	Yes (Supabase/Firebase)
11	ISS Real-Time Orbit Tracking	Data Analytics	Requests, Pandas, Matplotlib, Streamlit	Yes	No
12	USGS Global Earthquake Analysis	Data Analytics	Requests, Pandas, Matplotlib, Streamlit	Yes	No
13	Kochi Weather Trend Analysis	Data Analytics	Requests, Pandas, Matplotlib, Streamlit	Yes	No
14	Kochi Hourly Air Quality Analysis	Data Analytics	Requests, Pandas, Matplotlib, Streamlit	Yes	No
15	Decision Tree Occupancy Detection	Machine Learning	Scikit-learn, Pandas, Joblib, Matplotlib	No (Bundled)	No
16	Linear Regression Temp Prediction	Machine Learning	Scikit-learn, Pandas, Joblib, Matplotlib	No (Bundled)	No
17	Random Forest Air Quality Classifier	Machine Learning	Scikit-learn, Pandas, Joblib, Matplotlib	No (Bundled)	No

---

4. Python Installation Guide

Verify you have Python 3.9 or higher installed:

python3 --version
# Output should be Python 3.9.x, 3.10.x, 3.11.x, 3.12.x, or 3.13.x

Linux (Ubuntu / Debian)

sudo apt update
sudo apt install -y python3 python3-pip python3-venv

Linux (Fedora / RHEL)

sudo dnf install -y python3 python3-pip

Windows (PowerShell)

Download the official Python installer from python.org. During installation, make sure to check the box: "Add Python to PATH".

---

5. Creating a Virtual Environment

Always use a Python virtual environment to keep your system packages clean.

On Linux / macOS (Bash)

# Create a virtual environment named .venv
python3 -m venv .venv

# Activate the virtual environment
source .venv/bin/activate

On Windows (PowerShell)

# Create a virtual environment named .venv
python -m venv .venv

# Activate the virtual environment
.\.venv\Scripts\Activate.ps1

(If PowerShell shows an execution policy error, run: Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned)

---

6. Installing iotlabkit

Install iotlabkit in editable development mode directly from this repository:

Linux / macOS

python3 -m pip install -e .

Windows

python -m pip install -e .

To install optional extra dependencies (e.g. for ML or dashboards):

pip install -e ".[all]"         # Install all dependencies
pip install -e ".[analytics]"   # Pandas, Matplotlib, Requests
pip install -e ".[ml]"          # Scikit-learn, Joblib, Pandas, Matplotlib
pip install -e ".[iot]"         # Paho-MQTT, Requests, Python-dotenv
pip install -e ".[dashboard]"   # Streamlit

Verify installation:

iotlabkit --version
# Output: iotlabkit 1.0.0

---

7. Listing Practicals (list)

To view all supported programs and their operational flags:

iotlabkit list

Sample output:

==========================================================================================
           iotlabkit — MSc/MCA IoT Laboratory Practicals (Programs 5-17)
==========================================================================================
ID   | Title                                         | Net  | Keys  | Broker | Data  | Server
------------------------------------------------------------------------------------------
--- Communication and Application Layer ---
5    | HTTP REST API + Cloud Database + Dashboard    | Yes  | Yes   | No     | No    | Yes   
6    | MQTT + Cloud Database + Dashboard             | Yes  | Yes   | Yes    | No    | No    
7    | ThingsBoard + MQTT Telemetry                  | Yes  | Yes   | Yes    | No    | No    
8    | CoAP Client and Server Environmental Monit... | No   | No    | No     | No    | Yes   
...

---

8. Inspecting a Practical (info)

To view detailed prerequisites, objectives, generated files, and viva questions for any program:

iotlabkit info 8
iotlabkit info 15

---

9. Generating a Project (create)

Generate a practical project using its program number:

Learning Mode (Default)

Generates complete educational code with detailed docstrings, architecture diagrams, step-by-step instructions, and comprehensive viva Q&A:

iotlabkit create 5
iotlabkit create 12
iotlabkit create 15

Exam Mode (--mode exam)

Generates concise, focused, production-grade code designed for laboratory examination submission:

iotlabkit create 5 --mode exam
iotlabkit create 15 --mode exam

Custom Output Directory & Overwrite Flags

# Generate into a custom directory name
iotlabkit create 8 --output my_coap_lab

# Overwrite existing files without interactive prompt
iotlabkit create 8 --force

---

10. Installing Project Dependencies

Each generated project contains a self-contained requirements.txt:

cd lab05
pip install -r requirements.txt

(Note: Program 8 uses pure standard library and requires zero external pip packages!)

---

11. Configuring Cloud Services & Credentials

For programs requiring cloud integration (Programs 5, 6, 7, and 10), each generated project includes a .env.example template.

1. Navigate to the project folder and copy the template:

   # Linux / macOS:
   cp .env.example .env
   
   # Windows (PowerShell):
   Copy-Item .env.example .env
   

2. Open .env in any text editor (VS Code, Notepad, Nano).

Supabase Setup (Programs 5, 6, 10)

• Sign up at supabase.com.
• Create a project.
• Open the SQL Editor and run the provided supabase_schema.sql file.
• Copy your Project URL and anon public key into .env:

  SUPABASE_URL=https://your-project-ref.supabase.co
  SUPABASE_KEY=your-anon-public-key
  

Firebase Setup (Programs 5, 6, 10 Alternative)

• Open Firebase Console.
• Create a project -> Build -> Realtime Database.
• In .env:

  CLOUD_PROVIDER=firebase
  FIREBASE_DATABASE_URL=https://your-project.firebaseio.com
  

ThingsBoard Setup (Program 7)

• Sign up at thingsboard.cloud.
• Navigate to Entities -> Devices -> Add new device (e.g. Lab_Sensor_01).
• Copy the Access Token from Device Details.
• In .env:

  THINGSBOARD_ACCESS_TOKEN=your-device-access-token
  

Offline Fallback Guarantee: If cloud credentials are not supplied, Programs 5, 6, and 10 automatically fall back to local JSON caching (data/local_*.json) so you can complete and test the practical offline without internet!

---

12. Dataset Management & Offline Testing

• Program 15 (Occupancy Detection): Comes pre-bundled with 800 authentic records from UCI Dataset #357 in data/datatraining.txt. Run python data/download_dataset.py to fetch the full 8,143 records.
• Program 16 (Beijing Temperature): Comes pre-bundled with 800 authentic records from UCI Dataset #501 in data/beijing_sample.csv. Run python column_inspection.py to inspect features.
• Program 17 (India Air Quality): Comes pre-bundled with 900 balanced records across all 6 CPCB categories in data/city_day_sample.csv. Run python data/download_dataset.py for full Kaggle dataset instructions.

---

13. Running Each Practical

Program 5 — HTTP REST API + Cloud DB + Dashboard

# Terminal 1: Start REST Server
python server.py

# Terminal 2: Stream Sensor Telemetry
python client_sender.py

# Terminal 3: Launch Web Dashboard
streamlit run dashboard.py

Program 6 — MQTT + Cloud DB + Dashboard

# Terminal 1: Start Subscriber
python subscriber.py

# Terminal 2: Publish Real Readings
python publisher.py

# Terminal 3: View Dashboard
streamlit run dashboard.py

Program 7 — ThingsBoard + MQTT

python main.py
# Follow `thingsboard_dashboard_guide.md` to configure ThingsBoard widgets.

Program 8 — CoAP Client and Server (Zero Dependencies!)

# Terminal 1: Start CoAP Server
python server.py

# Terminal 2: Query CoAP Server
python client.py

# Continuous live observation:
python client.py --watch 3

Program 9 — Online & Local MQTT Brokers

# Online Mode (HiveMQ Public Cloud Broker):
python subscriber.py --broker online
python publisher.py --broker online

# Local Mode (Mosquitto Edge Broker):
python subscriber.py --broker local
python publisher.py --broker local

Program 10 — CoAP Environmental Monitoring (Open-Meteo)

# Terminal 1: CoAP Server (fetches Open-Meteo & serves CoAP)
python coap_env_server.py

# Terminal 2: CoAP Client (queries CoAP & stores to DB)
python coap_env_client.py

# Terminal 3: Dashboard
streamlit run dashboard.py

Program 11 — ISS Location Tracking

# Collect 100 live orbital points at 5-second intervals (~8.3 minutes):
python collector.py --count 100 --interval 5

# Compute orbital statistics & generate trend plots:
python analyzer.py

# View interactive orbit map:
streamlit run dashboard.py

Program 12 — USGS Earthquake Analysis

# Retrieve past 7 weeks of live seismic data from USGS:
python fetch_data.py

# Compute statistical distribution & plots:
python analyzer.py

# Launch interactive seismic dashboard:
streamlit run dashboard.py

Program 13 — Weather Analysis (Kochi)

# Fetch hourly meteorological data from Open-Meteo:
python fetch_weather.py

# Compute Pearson correlation & trend curves:
python analyzer.py

# Launch dashboard:
streamlit run dashboard.py

Program 14 — Air Quality Analysis (Kochi)

# Fetch hourly PM10, PM2.5, and US AQI:
python fetch_air_quality.py

# Calculate particulate statistics & WHO threshold curves:
python analyzer.py

# Launch dashboard:
streamlit run dashboard.py

Program 15 — Decision Tree Room Occupancy Classifier

# Train Decision Tree model and export artifacts:
python train.py

# Classify occupancy for a new sensor reading:
python predict.py --temp 23.2 --humidity 27.2 --light 426 --co2 721

# Launch Streamlit predictor:
streamlit run dashboard.py

Program 16 — Linear Regression Temperature Prediction

# Inspect actual dataset columns:
python column_inspection.py

# Train Linear Regression model (MAE, RMSE, R²):
python train.py

# Predict temperature for new readings:
python predict.py --dewp -16.0 --pres 1020.0 --pm25 120.0 --wind 2.0

Program 17 — Random Forest Air Quality Classification

# Train Random Forest classifier across 6 CPCB classes:
python train.py

# Predict air quality bucket for new pollutant readings:
python predict.py --pm25 85 --pm10 140 --no2 32 --co 1.2 --so2 15 --o3 45

# Launch interactive dashboard:
streamlit run dashboard.py

---

14. Running Diagnostics (doctor)

Check your Python environment, installed tools, and optional packages:

iotlabkit doctor

The doctor tool safely checks environment variables without exposing sensitive secret values.

---

15. Troubleshooting & Common Errors

Errno 111: Connection Refused (Programs 5, 8, 10)

Cause: The client was started before the corresponding server was active.
Fix: Keep Terminal 1 running with the server (python server.py) before running the client script in Terminal 2.

ModuleNotFoundError: No module named 'iotlabkit'

Cause: Package was not installed in editable mode or virtual environment is not activated.
Fix: Activate your virtual environment and run pip install -e ..

Mosquitto Connection Refused (Port 1883) (Program 9 Local Mode)

Cause: Mosquitto service is not running on your computer.
Fix:

• Linux: sudo systemctl start mosquitto
• Windows: Start Mosquitto from the Services window or run net start mosquitto.

Supabase 401 Unauthorized (Programs 5, 6, 10)

Cause: Invalid SUPABASE_KEY or missing anon key.
Fix: Check that your .env contains the full anon key from Project Settings -> API.

---

16. Uninstalling the Package

To remove iotlabkit from your environment:

pip uninstall iotlabkit -y

---

License

This project is licensed under the MIT License.
Datasets and APIs used belong to their respective creators:

• UCI Machine Learning Repository (Occupancy Detection #357, Beijing PM2.5 #501) — CC BY 4.0
• Rohan Rao Indian Air Quality Dataset (city_day.csv) — CC0: Public Domain
• Open-Meteo & Open-Meteo Air Quality APIs — Non-commercial educational use
• USGS Earthquake API — U.S. Geological Survey (Public Domain)
• Open-Notify & WhereTheISS APIs — Open satellite telemetry data