Networking Projects

An advanced Software-Defined Networking (SDN) project that implements both Dijkstra and Bellman-Ford shortest-path algorithms using the RYU controller. This project demonstrates the practical application of network routing algorithms in SDN environments.

Key Features:

• Implementation of both Dijkstra and Bellman-Ford algorithms for path computation
• Network topology visualization using NetworkX library
• Support for multiple link cost metrics:
  • Bandwidth-based routing
  • Delay-based routing
  • Combined bandwidth and delay metrics
  • Hop count-based routing
• Integration with Mininet for network emulation
• Real-time path computation and flow table updates

Technical Implementation:

• Python-based RYU controller application
• NetworkX for graph representation and algorithm implementation
• OpenFlow protocol for SDN control
• Mininet for network emulation and testing

The project showcases the practical implementation of network routing algorithms in SDN environments, providing a flexible and efficient solution for path computation in software-defined networks.

A sophisticated load balancing solution for face recognition video streaming in wireless networks, implemented using Mininet-WiFi and RYU controller. This project addresses the challenges of video streaming in wireless environments while maintaining high-quality face recognition performance.

Key Features:

• Dynamic load balancing for video streaming traffic
• Integration with face recognition systems
• Real-time network monitoring and path optimization
• Support for multiple wireless access points
• Quality of Service (QoS) management
• Performance metrics monitoring:
  • PSNR (Peak Signal-to-Noise Ratio) analysis
  • Throughput measurement
  • Network latency monitoring

Technical Implementation:

• Mininet-WiFi for wireless network emulation
• RYU controller for SDN-based network management
• Custom load balancing algorithms
• Integration with face recognition systems
• Performance analysis tools and metrics

The project demonstrates the effective application of SDN principles in wireless networks, particularly for resource-intensive applications like video streaming and face recognition. It provides a robust solution for maintaining high-quality video transmission while optimizing network resources.