AXON

IASI · EDGE-AI · INFRASTRUCTURE ANOMALY DETECTION

AXON (Adaptive eXtended-bridge Operational Network) — A three-layer Edge-AI IIoT framework for real-time infrastructure anomaly detection in transportation systems.

Edge Node (Pi Zero 2W) → Android Termux Bridge → Central Server → Live Dashboard

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⚠️ Prototype Notice

This is a Phase 1 simulation prototype.

• All sensor data (vibration, obstacle detection, IASI scores) is synthetically generated in the browser
• No physical hardware is connected in this phase
• The full system (Raspberry Pi Zero 2W + Android Termux Bridge + Ubuntu Server) will be integrated in Phase 2
• This prototype demonstrates the complete dashboard UI, data visualization, and IASI scoring pipeline

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🚆 Project Overview

Transportation infrastructure — railway tracks, urban roads, industrial pathways — degrades continuously between inspection cycles. Manual railway track inspection is conducted only once every 3–6 months, leaving a significant window during which cracks, joint failures, loose fasteners, and other structural faults go undetected.

AXON is a domain-agnostic edge-AI IIoT framework that monitors infrastructure health on every vehicle run. A low-cost edge node (Raspberry Pi Zero 2W + MPU6050 vibration sensor) captures structural signals continuously. Data travels through a resilient Android Termux MQTT bridge — with local SQLite offline buffering — to a central server running FFT analysis, Isolation Forest anomaly detection, and Random Forest severity classification.

At the core of the system is the IASI (Infrastructure Anomaly Severity Index) — a generic two-component composite score that quantifies infrastructure risk in real time. The same architecture applies to railway track monitoring, urban road quality assessment, and fleet vehicle health monitoring.

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🏗️ System Architecture

Layer	Component	Role
Layer 1	Raspberry Pi Zero 2W	Edge sensing node — MPU6050 vibration at 500Hz, MQTT publishing
Layer 2	Android Phone (Termux)	Resilient MQTT bridge — SQLite offline buffer, automatic replay on reconnect
Layer 3	Laptop / Ubuntu Server	ML processing — FFT, Isolation Forest, Random Forest, IASI engine, Flask REST API
Layer 4	Vercel + Render	Dashboard + API hosting — live browser visualization and operator console

In this Phase 1 prototype, Layers 1–3 are simulated synthetically inside the browser. Layer 4 (this dashboard) is fully functional and live.

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📊 IASI — Infrastructure Anomaly Severity Index

The IASI is a generic two-component composite score computed as:

IASI = (Vibration Anomaly Score × W1) + (Signal Continuity × W2)

Where W1 and W2 are domain-specific weights derived via the Analytic Hierarchy Process (AHP). For railway track monitoring: W1 = 0.85, W2 = 0.15.

Classification	IASI Range	Meaning
SAFE	0 – 25	Normal operating conditions
MONITOR	26 – 50	Early degradation detected — increase inspection frequency
ALERT	51 – 75	Significant anomaly — schedule maintenance within 48 hours
CRITICAL	76 – 100	Severe structural risk — halt traffic, dispatch field crew immediately

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🎮 Simulation Scenarios

Five fault severity scenarios with realistic IASI ranges and FFT frequency signatures:

Scenario	Description	IASI Range	Classification
NORMAL	Healthy infrastructure, baseline condition	5 – 18	SAFE
JOINT_FAULT	Rail joint loosening — periodic impulse signature at 18Hz	22 – 38	MONITOR
LOOSE_FASTENER	Loose fastener — localized vertical oscillation at 14Hz	15 – 30	MONITOR
RAIL_CRACK	Rail crack forming — sub-surface defect signature at 26Hz	38 – 54	ALERT
SEVERE_DAMAGE	Major structural failure — multi-band anomaly at 40Hz + 46Hz	60 – 92	CRITICAL

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✨ Dashboard Features

• 🔐 Operator authentication — secure login with session persistence
• 📡 Live device status — Pi Zero 2W, Android Bridge, Central Server status indicators
• 🎛️ Scenario control panel — switch between 5 fault conditions in real time
• 📈 IASI Gauge — animated semicircular SVG dial, 0–100, color-coded by risk class
• 🏷️ Risk Classification Badge — SAFE / MONITOR / ALERT / CRITICAL with live state
• 📉 IASI Trend Line — last 60 readings at 1Hz, rolling Recharts line chart
• 📊 FFT Power Spectrum — 51 frequency bins (0–50Hz), dominant frequency highlighted
• 🚧 Obstacle Detection Log — event log with class, confidence bars, and status flags
• 📡 Signal Continuity Gauge — Android bridge health indicator
• 🖥️ System Telemetry — Pi packet count, uptime, Android ping, server push latency
• 🌐 Responsive layout — desktop, tablet, and mobile

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🛠️ Tech Stack

Technology	Purpose
Next.js 14	React framework, App Router
Tailwind CSS	Utility-first styling, responsive layout
Recharts	IASI trend line and FFT vibration spectrum charts
Lucide React	SVG icon system
Google Fonts — Inter + JetBrains Mono	Clean sans-serif + monospace typography
Vercel	Hosting and continuous deployment from GitHub

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🚀 Getting Started

Prerequisites: Node.js 18+

git clone https://github.com/SaiSugeet/GuardRail-Central.git
cd GuardRail-Central
npm install
npm run dev

Open http://localhost:3000

Login credentials:

Operator ID: SaiSugeet Access Code: 2004

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📦 Deploy on Vercel

1. Push to GitHub
2. Import repository at vercel.com
3. Vercel auto-detects Next.js — click Deploy
4. Every git push to main auto-redeploys

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📁 Project Structure

axon/
├── app/
│   ├── layout.tsx              # Root layout — fonts, metadata
│   ├── page.tsx                # Entry point — login gate / dashboard router
│   └── globals.css             # CSS variables, Tailwind base, animations
├── components/
│   ├── LoginPage.tsx           # Operator authentication screen
│   ├── Dashboard.tsx           # Main dashboard shell and layout
│   ├── Navbar.tsx              # Top navigation bar
│   ├── DeviceStatusStrip.tsx   # Pi / Bridge / Server status cards
│   ├── ScenarioControl.tsx     # Fault scenario selector
│   ├── IRMSGauge.tsx           # IASI semicircular arc gauge
│   ├── RiskBadge.tsx           # Risk classification badge
│   ├── SignalContinuity.tsx    # Bridge health vertical gauge
│   ├── IRMSTrendChart.tsx      # 60-point rolling IASI trend line
│   ├── FFTSpectrumChart.tsx    # 51-bin FFT power spectrum
│   ├── ObstacleLog.tsx         # Obstacle detection event table
│   └── SystemTelemetry.tsx     # System stats readout panel
├── hooks/
│   └── useSimulation.ts        # Central simulation hook — all synthetic data
├── lib/
│   └── constants.ts            # Scenarios, IASI ranges, color maps, credentials
├── types/
│   └── index.ts                # TypeScript interfaces
├── public/                     # Static assets
├── vercel.json                 # Vercel deployment config
├── next.config.js              # Next.js configuration
├── tailwind.config.js          # Tailwind CSS configuration
└── README.md                   # Project documentation

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🗺️ Roadmap

Phase	Status	Description
Phase 1	✅ Complete	Simulation dashboard — full UI, IASI logic, 5 fault scenarios
Phase 2	🔄 In Progress	Pi Zero 2W + MPU6050 real sensor integration, axon-server backend
Phase 3	🔄 Planned	Android Termux CLI bridge, end-to-end pipeline validation
Phase 4	🔄 Planned	GPS fault tagging, road quality monitoring, multi-vehicle support

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👥 Team

Name	Role
G Sai Sugeet	Lead Developer & System Architect
S D Mukhesh	Backend Development & Testing
John Benny J	Hardware Integration & Testing
Neetha Udupa G	Hardware Integration & Communication

Guide: Dr. Agalya, Assistant Professor Institution: Dept. of Electrical & Electronics Engineering, New Horizon College of Engineering, Bengaluru — 560103 Subject Code: 22EEE65 | Academic Year: 2025–26

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📄 License

MIT License — free to use for educational and research purposes.

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AXON v2.0 | Phase 1 Simulation Prototype Built for IEEE Publication | New Horizon College of Engineering | 2025–26