Over the past two decades, the Internet-of-Things (IoT) has been a
transformative concept, and as we approach 2030, a new paradigm known as the
Internet of Senses (IoS) is emerging. Unlike conventional Virtual Reality (VR),
IoS seeks to provide multi-sensory experiences, acknowledging that in our
physical reality, our perception extends far beyond just sight and sound; it
encompasses a range of senses. This article explores existing technologies
driving immersive multi-sensory media, delving into their capabilities and
potential applications. This exploration includes a comparative analysis
between conventional immersive media streaming and a proposed use case that
lever- ages semantic communication empowered by generative Artificial
Intelligence (AI). The focal point of this analysis is the substantial
reduction in bandwidth consumption by 99.93% in the proposed scheme. Through
this comparison, we aim to underscore the practical applications of generative
AI for immersive media while addressing the challenges and outlining future
trajectories.

到 2030 年，一种新的名为感知互联网（Internet of Senses，IoS）的模式正在崛起，它旨在提供多感官体验，相较于传统虚拟现实（Virtual Reality，VR），IoS 承认我们在现实世界中的感知远远超过视觉和听觉，该文探讨了推动沉浸式多感官媒体的现有技术，重点研究了它们的功能和潜在应用，包括对比分析了传统沉浸式媒体流媒体和通过生成式人工智能（AI）赋予的语义通信的提案用例，该分析的重点是在提案方案中带来的 99.93％带宽消耗的大幅度降低，通过这种对比，我们旨在强调生成式人工智能在沉浸式媒体中的实际应用，并解决挑战并勾勒未来的发展方向。

基于生成式人工智能的沉浸式通信：通过 6G 进入感官互联网的下一个前沿

Generative AI for Immersive Communication: The Next Frontier in  Internet-of-Senses Through 6G

The swift advancement and widespread availability of foundational Large
Language Models (LLMs), complemented by robust fine-tuning methodologies, have
catalyzed their adaptation for innovative and industrious applications.
Enabling LLMs to recognize and interpret geospatial data, while offering a
linguistic access to vast cartographic datasets, is of significant importance.
OpenStreetMap (OSM) is the most ambitious open-source global initiative
offering detailed urban and rural geographic data, curated by a community of
over 10 million contributors, which constitutes a great potential for LLM
applications. In this study, we demonstrate the proof of concept and details of
the process of fine-tuning a relatively small scale (1B parameters) LLM with a
relatively small artificial dataset curated by a more capable teacher model, in
order to provide a linguistic interface to the OSM data of an arbitrary urban
region. Through this interface, users can inquire about a location's
attributes, covering a wide spectrum of concepts, such as its touristic appeal
or the potential profitability of various businesses in that vicinity. The
study aims to provide an initial guideline for such generative artificial
intelligence (AI) adaptations and demonstrate early signs of useful emerging
abilities in this context even in minimal computational settings. The
embeddings of artificially curated prompts including OSM data are also
investigated in detail, which might be instrumental for potential geospatially
aware urban Retrieval Augmented Generation (RAG) applications.

利用相对较小规模（10 亿参数）的大型语言模型和相对较小的人工数据集精细调整，为 OpenStreetMap 数据提供了语言接口，以查询城市区域的属性，并探索人工智能适应性和生成能力在这一领域的早期应用潜力。