Rethinking Quality of Experience for Metaverse Services: A Consumer-based Economics Perspective

The Metaverse is considered to be one prototype of the next-generation Internet, which contains people's expectations for the future world. However, the academic discussion of the Metaverse still mainly focused on the system technical design, and few research studied Metaverse challenges from the perspective of consumers, i.e., Metaverse users. One difficulty is that the analysis from the consumer's perspective requires interdisciplinary theoretical framework and quantifiable Quality of Experience (QoE) measurements. In this article, pioneering from consumers' point of view, we explore an interaction between Metaverse system design and consumer behaviors. Specifically, we rethink the QoE and propose an interdisciplinary framework that encompasses both the Metaverse service providers (MSPs) and consumer considerations. From the macro perspective, we introduce a joint optimization scheme that simultaneously considers the Metaverse system design, consumers' utility, and profitability of the MSPs. From the micro perspective, we advocate the Willingness-to-Pay (WTP) as an easy-to-implement QoE measurement for future Metaverse system studies. To illustrate the usability of the proposed integrated framework, a use case of Metaverse, i.e., virtual traveling, is presented. We show that our framework can benefit the MSPs in offering competitive and economical service design to consumers while maximizing the profit

Motivation for users’ knowledge sharing behavior in virtual brand communities : A psychological ownership perspective

The project is supported by National Social Science Foundation of China (21BGL132), National Natural Science Foundation of China (NSFC) (71802097), Jinan University Management School Funding Program (GY21013), Institute for Enterprise Development, Jinan University, Guangdong Province (2021MYZD04, HS2102 and 2020CP03), Philosophy and Social Sciences Planning Program of Guangzhou (2021GZYB05) and Research Institute on Brand Innovation and Development of Guangzhou (2021CS05). The first two authors contributed equally to this work.Peer reviewe

Everyone Deserves A Reward: Learning Customized Human Preferences

Reward models (RMs) are crucial in aligning large language models (LLMs) with human preferences for improving interaction quality. However, the real world is pluralistic, which leads to diversified human preferences based on different religions, politics, cultures, etc. Moreover, each individual can have their own unique preferences on various topics. Neglecting the diversity of human preferences, current LLM training processes only use a general reward model, which is below satisfaction for customized or personalized application scenarios. To explore customized preference learning, we collect a domain-specific preference (DSP) dataset, which collects preferred responses to each given query from four practical domains. Besides, from the perspective of data efficiency, we proposed a three-stage customized RM learning scheme, whose effectiveness is empirically verified on both general preference datasets and our DSP set. Furthermore, we test multiple training and data strategies on the three learning stages, and have found several ways to better preserve the general preferring ability while training the customized RMs, especially general preference enrichment and customized preference imitation learning. The DSP dataset and code are available at https://github.com/Linear95/DSP

Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076\% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.Comment: 6 pages,7figure

Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective

As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications

Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverses are brand-new concepts from the metaverse's distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework's SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions

From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Finally, we suggest several open directions worth investigating for the future popularity of GIoT

Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study

As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.Comment: 9 pages, 6 figur

Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) offers a promising solution to this challenge. However, the training and deployment of large AI models necessitate significant resources. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks, ensuring ubiquitous access to AIGC services for Metaverse users. Nonetheless, a key aspect of providing personalized user experiences requires the careful selection of AIGC service providers (ASPs) capable of effectively executing user tasks. This selection process is complicated by environmental uncertainty and variability, a challenge not yet addressed well in existing literature. Therefore, we first propose a diffusion model-based AI-generated optimal decision (AGOD) algorithm, which can generate the optimal ASP selection decisions. We then apply AGOD to deep reinforcement learning (DRL), resulting in the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, which achieves efficient and effective ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it a promising approach for the future research on AIGC-driven services in Metaverse. The implementation of our proposed method is available at: https://github.com/Lizonghang/AGOD