arXiv subjects

Yinqiu Liu

Publications and source records attributed to Yinqiu Liu.

62 records · Page 4Linked to original sources

arXiv2024

ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms using a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment (OS^{2}A) framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). Deploying OS^{2}A on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator.Extensive experiments demonstrate that ProSecutor achieves 12.5x throughput and saves 67.5\% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated.

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arXiv2024

Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking

Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely defined reward functions, which formulate the optimization objective and indicate the positive/negative progress towards the optimal. With the ever-increasing environmental complexity and human participation in Next-Generation Networking (NGN), defining appropriate reward functions become challenging. In this article, we explore the applications of Inverse Reinforcement Learning (IRL) in NGN. Particularly, if DRL aims to find optimal solutions to the problem, IRL finds a problem from the optimal solutions, where the optimal solutions are collected from experts, and the problem is defined by reward inference. Specifically, we first formally introduce the IRL technique, including its fundamentals, workflow, and difference from DRL. Afterward, we present the motivations of IRL applications in NGN and survey existing studies. Furthermore, to demonstrate the process of applying IRL in NGN, we perform a case study about human-centric prompt engineering in Generative AI-enabled networks. We demonstrate the effectiveness of using both DRL and IRL techniques and prove the superiority of IRL.

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arXiv2024

Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study

Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains. Then, we discuss emerging solutions that demonstrate the effectiveness of GAI in addressing various challenges of blockchain, such as detecting unknown blockchain attacks and smart contract vulnerabilities, designing key secret sharing schemes, and enhancing privacy. Moreover, we present a case study to demonstrate that GAI, specifically the generative diffusion model, can be employed to optimize blockchain network performance metrics. Experimental results clearly show that, compared to a baseline traditional AI approach, the proposed generative diffusion model approach can converge faster, achieve higher rewards, and significantly improve the throughput and latency of the blockchain network. Additionally, we highlight future research directions for GAI in blockchain applications, including personalized GAI-enabled blockchains, GAI-blockchain synergy, and privacy and security considerations within blockchain ecosystems.

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arXiv2024

Interactive AI with Retrieval-Augmented Generation for Next Generation Networking

With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this article, we explore an integration and enhancement of IAI in networking. We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI. We then explore the integration of IAI into the next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks.

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arXiv2023

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.

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arXiv2023

Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered framework for wireless network management is proposed, in which we elaborate the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, i.e., diffusion model, to generate effective contracts for incentivizing the mobile AI-Generated Content (AIGC) services. Last but not least, we discuss important open directions for the further research.

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arXiv2023

Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks

The rapid development of Artificial IntelligenceGenerated Content (AIGC) has brought daunting challenges regarding service latency, security, and trustworthiness. Recently, researchers presented the edge AIGC paradigm, effectively optimize the service latency by distributing AIGC services to edge devices. However, AIGC products are still unprotected and vulnerable to tampering and plagiarization. Moreover, as a kind of online non-fungible digital property, the free circulation of AIGC products is hindered by the lack of trustworthiness in open networks. In this article, for the first time, we present a blockchain-empowered framework to manage the lifecycle of edge AIGC products. Specifically, leveraging fraud proof, we first propose a protocol to protect the ownership and copyright of AIGC, called Proof-of-AIGC. Then, we design an incentive mechanism to guarantee the legitimate and timely executions of the funds-AIGC ownership exchanges among anonymous users. Furthermore, we build a multi-weight subjective logic-based reputation scheme, with which AIGC producers can determine which edge service provider is trustworthy and reliable to handle their services. Through numerical results, the superiority of the proposed approach is demonstrated. Last but not least, we discuss important open directions for further research.

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arXiv2020

Effective Scaling of Blockchain Beyond Consensus Innovations and Moore's Law

As an emerging technology, blockchain has achieved great success in numerous application scenarios, from intelligent healthcare to smart cities. However, a long-standing bottleneck hindering its further development is the massive resource consumption attributed to the distributed storage and computation methods. This makes blockchain suffer from insufficient performance and poor scalability. Here, we analyze the recent blockchain techniques and demonstrate that the potential of widely-adopted consensus-based scaling is seriously limited, especially in the current era when Moore's law-based hardware scaling is about to end. We achieve this by developing an open-source benchmarking tool, called Prism, for investigating the key factors causing low resource efficiency and then discuss various topology and hardware innovations which could help to scale up blockchain. To the best of our knowledge, this is the first in-depth study that explores the next-generation scaling strategies by conducting large-scale and comprehensive benchmarking.

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