ResqNet

Next-Generation Real-Time Disaster Response & Coordination Platform

🌍 Project Intro

ResqNet is an advanced, real-time emergency management and coordination system designed to bridge the gap between distressed individuals, first responders, and command centers during critical disaster events. By leveraging real-time geospatial tracking, AI-assisted incident analysis, and high-availability websocket communications, ResqNet empowers teams to make data-driven decisions when every second counts.

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📸 Screenshots

Dashboard Overview	Live Tracking Map
Command Center Analytics	AI Assistant & Incident Triage

Shelter Capacity & Management System

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🏗️ Architecture Diagrams

ResqNet uses a distributed architecture to ensure high availability and low latency during peak traffic (disaster events).

System Flow Diagram

graph TD
    User([End User / Victim]) -->|SOS Signal| Client(ResqNet Client / PWA)
    Client -->|REST / WSS| LoadBalancer[Load Balancer]
    LoadBalancer --> API[API Gateway]
    LoadBalancer --> WSS[WebSocket Server]
    API --> Services[Backend Microservices]
    WSS --> PubSub[(Redis Pub/Sub)]
    Services --> DB[(PostgreSQL)]
    Services --> ML[AI Triage Engine]
    PubSub --> WSS
    WSS --> Responder([First Responder Terminal])

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Backend Services Diagram

graph LR
    Gateway[API Gateway] --> Auth[Auth Service]
    Gateway --> Incident[Incident Service]
    Gateway --> Geo[Geo-Spatial Service]
    Gateway --> Shelter[Shelter Management]
    Incident --> DB[(Primary DB)]
    Geo --> Redis[(Cache & Location)]
    Incident --> Kafka[Event Bus]

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WebSocket Flow (Real-Time Communication)

sequenceDiagram
    participant C as Client (Victim)
    participant W as WebSocket Node
    participant R as Redis Pub/Sub
    participant D as Dispatcher UI
    
    C->>W: Connect (Token)
    W-->>C: Ack & Subscribe
    C->>W: Emit 'location_update' {lat, lng}
    W->>R: Publish 'incident:123'
    R->>W: Fan-out to Subscribers
    W->>D: Push 'location_update'
    D-->>W: Acknowledge

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✨ Features

• Real-Time Incident Dashboard: Bird's-eye view of all active incidents, severity levels, and resource allocations.
• Geospatial Tracking (Map): Live tracking of responders, assets, and SOS signals on an interactive 3D map.
• Advanced Analytics: Post-incident reports, response time heatmaps, and resource utilization metrics.
• AI Assistant: NLP-based triage system that prioritizes incidents based on incoming distress text/audio.
• Shelter Management System: Real-time capacity tracking, inventory management, and routing for displaced persons.
• Offline Capabilities: Core functionalities are cached via PWA for intermittent connectivity zones.

🧪 Simulated Features

To demonstrate the full potential of the architecture without requiring hardware integrations, the following features are simulated within the platform:

• Drone Feeds: Pre-recorded aerial footage fed through the streaming pipeline.
• Satellite Monitoring: Historical thermal anomaly data replayed as real-time events.
• Blockchain Logging: Simulated immutable ledger transactions for audit trails.
• Thermal AI: Mocked object detection bounding boxes on thermal video feeds.

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🛠️ Tech Stack

Frontend:

• React (Vite) / Next.js
• Tailwind CSS
• Mapbox GL JS / Deck.gl
• Socket.io-client / Zustand

Backend:

• Node.js (Express / NestJS)
• PostgreSQL (PostGIS)
• Redis (Pub/Sub & Caching)
• Socket.io
• Prisma ORM

Infrastructure (Simulated/Planned):

• Docker & Kubernetes
• AWS (EC2, S3, RDS)
• NGINX

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🚀 Installation & Setup

1. Clone the repository

   git clone https://github.com/Rishisharma029/resqnet.git
   cd resqnet

2. Environment Variables

   cp .env.example .env
   # Update .env with your specific keys (DB, Mapbox, etc.)

3. Install Dependencies

   # Install server dependencies
   cd server
   npm install
   
   # Install client dependencies
   cd ../client
   npm install

4. Run the Application

   # In terminal 1 (Backend)
   cd server
   npm run dev
   
   # In terminal 2 (Frontend)
   cd client
   npm run dev

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🗺️ Roadmap

• Phase 1: Core authentication and basic SOS routing.
• Phase 2: Real-time map integration and WebSocket setup.
• Phase 3: AI Assistant NLP integration for automatic triage.
• Phase 4: Mobile App (React Native) deployment for offline-first usage.
• Phase 5: Hardware integration (IoT sensors, actual drone feeds).

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Note on Commits & Contribution: This repository follows conventional commit standards. Example: feat: implement realtime incident dashboard, refactor: optimize websocket event handling.