IJIET 2025 Vol.15(11): 2405-2412
doi: 10.18178/ijiet.2025.15.11.2436
doi: 10.18178/ijiet.2025.15.11.2436
An Instructional Home Network Testbed for Undergraduate Networking Education
Andy Zheng1,*, Adam Beauchaine2, and Mira Yun1
1. Department of Computer Science, Boston College, Chestnut Hill, MA, USA
2. Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
Email: zhengno@bc.edu (A.Z.); ajbeauchaine@wpi.edu (A.B.); yunmd@bc.edu (M.Y.)
*Corresponding author
2. Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
Email: zhengno@bc.edu (A.Z.); ajbeauchaine@wpi.edu (A.B.); yunmd@bc.edu (M.Y.)
*Corresponding author
Manuscript received March 21, 2025; revised May 19, 2025; accepted July 4, 2025; published November 13, 2025
Abstract—Teaching undergraduates about home networking challenges is difficult, as applied networking concepts are often complex to demonstrate in a traditional classroom setting. Many existing simulation tools lack fidelity, transparency, or require expensive hardware and software. Moreover, industry-standard and research-grade tools tend to be overly complex for undergraduate students, making them hard to grasp and inefficient to teach. To address these challenges, this paper presents an instructional home network testbed built with affordable Raspberry Pi 4Bs, enabling hands-on and accessible learning. We introduce three learning activities designed to showcase the ability of this testbed: (1) leveraging Reinforcement Learning (RL) to dynamically optimize Smart Queue Management (SQM) settings, (2) applying supervised learning to classify network flows for traffic prioritization, and (3) simulating attack scenarios to analyze network vulnerabilities. By integrating Artificial Intelligence (AI) into networking education, this testbed provides a scalable and cost-effective platform for teaching a wide range of networking concepts. It offers students hands-on experience with real-world networking challenges, including working with machine learning models, conducting traffic analysis, and simulating attacks, all while utilizing simple, efficient, and cost-effective hardware.
Keywords—educational testbed for undergraduate, AI-driven network optimization, learning activities for undergraduate, network security education
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—educational testbed for undergraduate, AI-driven network optimization, learning activities for undergraduate, network security education
Cite: Andy Zheng, Adam Beauchaine, and Mira Yun, "An Instructional Home Network Testbed for Undergraduate Networking Education," International Journal of Information and Education Technology, vol. 15, no. 11, pp. 2405-2412, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).