Date of Submission
5-2025
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Electrical & Computer Engineering and Computer Science
Advisor
Hussein Sayed, Ph.D.
Committee Member
Dikran Meliksetian, Ph.D.
Committee Member
Moinuddin Bhuiyan, Ph.D.
Keywords
Software Defined Networking (SDN), URLLC, Network Topology, Low Latency, IoT Scalability
LCSH
Software-defined networking (Computer network technology), Internet of things
Abstract
The Internet of Things (IoT) is expanding faster than ever, and with that growth comes a real need for networks that can handle more devices while keeping connections fast, reliable, and efficient. Traditional methods are starting to fall short, especially for applications where even a slight delay can cause problems. That’s why combining Edge Computing with Software-Defined Networking (SDN) has become such an interesting solution, particularly for systems that need ultra-reliable, low-latency communication (URLLC). This thesis explores how SDN and Edge Computing together affect IoT network performance. Using Mininet-WIFI and the Ryu controller, I tested both tree and mesh topologies across different scales, measuring end-to-end latency and flow rule installation times under URLLC-like traffic. I compared SDN-enabled networks with non-SDN baselines to reveal real-world performance differences. Results showed that SDN networks—especially mesh-based—delivered lower latency through multipath routing and fast flow installations (under 1 ms). However, mesh topologies required more complex control plane management than trees. Ultimately, combining SDN's adaptability with edge computing’s proximity offers a powerful approach to building scalable, latency-sensitive IoT infrastructures ready for future demands.
Recommended Citation
Kumari Jha, Roshani, "Analysis of Integrating Edge Computing in IoT Networks With SDN" (2025). Master's Theses. 288.
https://digitalcommons.newhaven.edu/masterstheses/288