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Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks

Alakesh Kalita

TL;DR

The paper addresses the problem of efficient IoT communication under near-capacity channels by leveraging semantic communication through large language models (LLMs) deployed at the network edge. It proposes an edge-based framework where border routers forward IoT data to an edge device hosting an LLM, which uses historical context and multimodal prompts to generate meaningful, actuator-ready commands, reducing bandwidth and latency. The work details a modular system design, including data collection, processing, storage, prompt creation, and response handling, and explores use cases in smart homes, industrial IoT, and healthcare. Key challenges identified include privacy and security concerns and data heterogeneity/quality across IoT devices, with guidance on leveraging edge processing and robust data management to enable practical deployment. Overall, the approach aims to realize context-aware, natural-language-enabled IoT systems that align with evolving 5G/6G edge ecosystems, offering tangible gains in efficiency, responsiveness, and user experience.

Abstract

With the advent of Fifth Generation (5G) and Sixth Generation (6G) communication technologies, as well as the Internet of Things (IoT), semantic communication is gaining attention among researchers as current communication technologies are approaching Shannon's limit. On the other hand, Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters. Considering the recent near-source computational technologies like Edge, in this article, we give an overview of a framework along with its modules, where LLMs can be used under the umbrella of semantic communication at the network edge for efficient communication in IoT networks. Finally, we discuss a few applications and analyze the challenges and opportunities to develop such systems.

Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks

TL;DR

The paper addresses the problem of efficient IoT communication under near-capacity channels by leveraging semantic communication through large language models (LLMs) deployed at the network edge. It proposes an edge-based framework where border routers forward IoT data to an edge device hosting an LLM, which uses historical context and multimodal prompts to generate meaningful, actuator-ready commands, reducing bandwidth and latency. The work details a modular system design, including data collection, processing, storage, prompt creation, and response handling, and explores use cases in smart homes, industrial IoT, and healthcare. Key challenges identified include privacy and security concerns and data heterogeneity/quality across IoT devices, with guidance on leveraging edge processing and robust data management to enable practical deployment. Overall, the approach aims to realize context-aware, natural-language-enabled IoT systems that align with evolving 5G/6G edge ecosystems, offering tangible gains in efficiency, responsiveness, and user experience.

Abstract

With the advent of Fifth Generation (5G) and Sixth Generation (6G) communication technologies, as well as the Internet of Things (IoT), semantic communication is gaining attention among researchers as current communication technologies are approaching Shannon's limit. On the other hand, Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters. Considering the recent near-source computational technologies like Edge, in this article, we give an overview of a framework along with its modules, where LLMs can be used under the umbrella of semantic communication at the network edge for efficient communication in IoT networks. Finally, we discuss a few applications and analyze the challenges and opportunities to develop such systems.
Paper Structure (14 sections, 3 figures)

This paper contains 14 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Semantic Communication
  4. Large Language Models (LLMs)
  5. Edge Computing for LLMs
  6. Internet of Things (IoT)
  7. A Framework for LLM-Based semantic Communication in IoT Networks
  8. System Model
  9. Advantages of using LLM for Semantic Communication in IoT Networks
  10. Use Cases
  11. Open Challenges and Issues
  12. Privacy and Security
  13. Data Heterogeneity and Quality
  14. Conclusion

Figures (3)

  • Figure 1: Working of (a) Transitional communication and (b) semantic communication. The latter ensures bit-wise (syntax) transmission, whereas semantic communication ensures the meaning of the transmitted information and works even if there are some bit-level errors in the receiver.
  • Figure 2: A framework enabling LLM-based semantic communication in Edge-based IoT system
  • Figure 3: Proposed framework is divided into multiple structured modules that handle data collection, processing, prompt creation, response handling, and actuator management.