Category Archives: CCR July 2024

TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

Girma M. Yilma, Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez

Abstract

Large Language Models (LLMs) have immense potential to transform the telecommunications industry. They could help professionals understand complex standards, generate code, and accelerate development. However, traditional LLMs struggle with the precision and source verification essential for telecom work. To address this, specialized LLM-based solutions tailored to telecommunication standards are needed. This Editorial Note showcases how Retrieval-Augmented Generation (RAG) can offer a way to create precise, factual answers. In particular, we show how to build a Telecommunication Standards Assistant that provides accurate, detailed, and verifiable responses. We show a usage example of this framework using 3GPP Release 16 and Release 18 specification documents. We believe that the application of RAG can bring significant value to the telecommunications field.

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Towards Immersive Cloud-Based IoT Education

Fan Gabriella Xue, Matthew Caesar

Abstract

An increasing number of students are becoming interested in learning about the Internet of Things (IoT) space. However, today, we lack scalable and efficient ways to bring hands-on IoT learning to many due to hardware accessibility, system complexity, and deployment environment constraints. This paper presents ThingVisor, an IoT learning platform that enables hands-on IoT development in an immersive virtual space. Specifically, it allows users to design, test, and deploy IoT devices virtually in a simulated IoT world with static and dynamic software verification as a complementary tool to IoT education. ThingVisor consists of (1) a Device Design Stack to configure virtual IoT devices, (2) an Immersive Runtime Stack to interact with devices and environment, and (3) a Device Emulator, which is a runtime environment used to execute virtual devices to get their behaviors. Our experiments confirm the learning effectiveness and user satisfaction of our platform. Additionally, we have demonstrated the scalability and usability of the system through load testing and application of the System Usability Scale. Our results indicate that students can achieve up to a 32% improvement in their scores after engaging with ThingVisor for two weeks, irrespective of their prior experience.

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A Survey on Packet Filtering

Nik Sultana, Hyunsuk Bang, Elena Yulaeva, Ricky K. P. Mok, Kc Claffy, Richard Mortier

Abstract

Packet filtering has remained a key network monitoring primitive over decades, even as networking has continuously evolved. In this article we present the results of a survey we ran to collect data from the networking community, including researchers and practitioners, about how packet filtering is used. In doing so, we identify pain points related to packet filtering, and unmet needs of survey participants. Based on analysis of this survey data, we propose future research and development goals that would support the networking community.

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The July 2024 issue

This July 2024 issue contains one technical paper, one educational paper, and one editorial note.

The technical paper, A Survey on Packet Filtering, by Nick Sultana and colleagues, was originally submitted as an editorial. Given that CCR does not usually consider survey papers, it went through a thorough reviewing process. Given its value to the community, we felt that it deserves to be accepted as a technical paper, not an editorial. The topic of this work is important to the community, namely packet filtering. The authors present the results of a survey they ran to collect data from the networking community, including researchers and practitioners, about how packet filtering is used. They identify pain points related to packet filtering, and unmet needs of survey participants. Based on analysis of this survey data, they propose future research and development goals that would support the networking community.

The second paper, an educational contribution, Towards Immersive Cloud-Based IoT Education, by Fan Gabriella Xue and Matthew Caesar, presents ThingVisor, an IoT learning platform that enables hands-on IoT development in an immersive virtual space. Specifically, it allows users to design, test, and deploy IoT devices virtually in a simulated IoT world with static and dynamic software verification as a complementary tool to IoT education. The experiments confirm the learning effectiveness and user satisfaction of the platform, as well as the scalability and usability of the system.

Finally, the editorial note, TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs by Girma M. Yilma and colleagues, discusses the very timely topic of Large Language Models (LLMs), and discuss the potential they have in transforming the telecommunications industry.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online (https://ccronline.sigcomm.org) or by email at ccr-editor at sigcomm.org.