This repository contains the implementation of "seeSea", a Vessel Traffic Services (VTS) app, with real-time vessel tracking and maritime monitoring developed for the course Software Technology [YΣ09] – Spring Semester 2025.

The platform leverages AIS (Automatic Identification System) data to support real-time vessel location tracking, zone violation alerts, and fleet management functionality. This system aims to improve maritime situational awareness and supports secure, and scalable monitoring for various user roles.

You can find screenshots and showcase images here

As defined in the assignment, the goal was to develop a complete web platform with the following phases:

• ./docs/srs.md: Software Requirements Specification (SRS) including functional and non-functional requirements.
• ./docs/ui.md: UI wireframes and user interface prototypes (designed with Figma).

• ./docs/presentation.pdf: Presentation with UML diagrams, detailed implementation plans, and updated design decisions based on Part 1.

• ./src/: Full-stack implementation of the system using the technologies described below.

• Frontend: React.js with HeroUI components and Leaflet.js for interactive mapping
• Backend: Spring Boot (Java) exposing RESTful APIs, integrated with WebSocket for real-time updates
• Streaming: Apache Kafka with a Python AIS data producer and Java consumer
• Database: MSSQL, containerized via Docker and managed with Flyway for schema versioning
• Security: HTTPS (self-signed cert), JWT-based authentication, role-based access
• Version Control: Git (hosted on GitHub)

• 📡 Live Vessel Map: Real-time AIS data stream replayed through Kafka for live ship tracking
• 🛟 Zone of Interest (ZOI):
  • User-defined circular zones
  • Speed thresholds and violation alerts
  • Single active ZOI per user
• 📜 Vessel History: Past 12 hours of vessel movements
• 📁 Fleet Management: Users manage personal fleets, viewable on map
• 🔍 Filtering & Search: By vessel type/status, fleet, MMSI
• 🔔 Notification System: Real-time, persistent user alerts
• 🔐 Role-Based Access:
  • Admins can edit static vessel attributes (e.g., vessel type)
  • Registered and admin users have access to more features in the app

• HTTPS secured communication
• GDPR-compliant user data handling
• Role-based permission management

SOFTWARETECHNOLOGY/
├─ be/ships-spring-example/        # Backend Spring Boot application
├─ docs/
│  ├─ assets/
│  ├─ Presentation.pdf             # Project presentation slides
│  ├─ srs.md                       # Software Requirements Specification
│  ├─ ui.md                        # UI mockups and design
│  └─ UML-ClassDiagram.pdf         # UML Class Diagram
├─ fe/ships-map/                   # Frontend (React)
├─ kafka/VTS/
│  ├─ dataset/                     # AIS dataset files
│  └─ kafka_2.12-3.9.0/            # Apache Kafka distribution
├─ .gitignore
├─ README.md                       # Project overview and documentation

In order to run properly user must have:

1. A Unix-like environment, including Linux distributions and macOS
2. Kafka 3.9.0 and Zookeeper must be installed
3. Docker must be installed (Docker Desktop is also useful)
4. Node.js must be installed
5. Java 21 must be installed
6. Python, Pandas, and Confluent Kafka must be installed
7. Database must be configured as described below:

   • Pull Image:

     docker pull mcr.microsoft.com/mssql/server:2019-latest

   • Run docker:

     docker run -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=Password()2003" -p 1433:1433  --name seesea --hostname sqlserver -d mcr.microsoft.com/mssql/server:2019-latest

   • Connect project data source (IntelliJ recommended):

     • Host: localhost
     • Port: 1433
     • User: sa
     • Password: Password()2003
     • Driver: MSSQL Server

   • Run query to create database (IntelliJ Query Console recommended):

     CREATE DATABASE SeeSea;

8. Folder dataset must be downloaded independently
   • Download Kafka_intro.zip folder from: https://owncloud.skel.iit.demokritos.gr/index.php/s/9EsxBK0Bk4ebudk
   • Export folder
   • Move dataset folder from Kafka_intro/VTS/ to /kafka/VTS/

Follow these steps to run the full system locally:

1. Start the Docker Image using Docker

2. Start the Backend

   • Navigate to the backend directory:

     cd be/ships-spring-example

   • Build and run the Spring Boot application

3. Start Kafka

   • Go to the Kafka folder:

     cd kafka/VTS/kafka_2.12-3.9.0

   • Start Zookeeper and Kafka servers:

     ./start.sh

4. Run the Dataset Producer

   • Execute the Python script that streams AIS data to Kafka:

     cd kafka/VTS/dataset
     python3 producer.py

5. Start the Frontend

   • Navigate to the frontend directory:

     cd fe/ships-map

   • Install dependencies and start the development server:

     npm install
     ./start.sh

To log in as an admin, you can use the following:

• email: admin@seesea.com
• password: admin

Checks for collisions between 2 or more vessels and returns an alert.

• Motionless vessels are skipped from the calculations (cannot collide with others, but others can collide with them)
• If vessels have a distance greater than 10.000 meters collision check will not be calculated.
• A distance greater than 1.000 meters is considered safe (no alerts will be sent, but collision checks will be made).
• Collision detection is based on a 15-minute predictive horizon. (If a CPA distance smaller than 1.000 meters is to be reached in the next 15 minutes, an alert will be sent)

CPA Reference: On the closest point of approach (CPA)