Web3 technologies enable novel forms of real-time digital human streaming media by supporting both high-fidelity transmission and interactive user engagement. However, Real-Time Streaming Interactive Digital Humans (RTSIDHs) remain vulnerable to network instability, resulting in buffering, latency, visual degradation, and audio–video desynchronization that substantially impair user Quality of Experience (QoE). To effectively perceive these distortions, we present RDHQA, the first large-scale RTSIDH Quality Assessment dataset. RDHQA comprises 134 representative interaction scenarios with eight digital human avatars as high-quality references, along with 1,340 distorted samples generated by simulating five common streaming degradations. Based on extensive subjective evaluations, we further propose SAV-PF, an audio–visual quality assessment method built on the human foundation model Sapiens and informed by cognitive principles such as the primacy effect and forgetting curve. Experimental results demonstrate that SAV-PF achieves superior performance over existing objective QoE assessment approaches, providing a more accurate prediction of user experience. This work is open-sourced at https://github.com/zyj-2000/RDHQA under the CC BY-NC 4.0 licence.
Rodrigo Dutra Garcia, Gowri Ramachandran, Christian Esteve Rothenberg, Daniel Macêdo Batista · 5 authors
The transition to 6G networks is expected to support a broader range of user-centric applications. As these applications expand, Quality of Experience (QoE) has emerged as a key metric for evaluating user satisfaction. However, the use of centralized systems lacks transparency and limits users’ ability to govern their data usage. It also introduces challenges in managing QoE data while preserving privacy. At the same time, verification mechanisms are needed that allow regulators to evaluate compliance without exposing confidential business information. To address this, we propose a decentralized, privacy-preserving QoE system that integrates blockchain with fully homomorphic encryption (FHE). This design enables transparent evaluations between users and service providers by supporting computations directly on encrypted data, ensuring that all information remains protected throughout the process. Users contribute QoE metrics through a decentralized infrastructure and retain control over their data. Regulators can monitor compliance without accessing raw data, and service providers can use encrypted QoE data to perform privacy-preserving computations via smart contracts without relying on a central authority. We developed a proof-of-concept integrating FHE smart contracts compatible with Ethereum Virtual Machine (EVM) blockchains and evaluated their performance using a video streaming dataset. Our results show that encryption and FHE operations consistently occur within milliseconds when tested in a local environment. We also evaluated these operations on a public blockchain testnet. In this setting, our system adds a millisecond-scale delay while supporting privacy-preserving computations directly on encrypted user data.
The Bitcoin network comprises numerous nodes, necessitating users to invest significant network requests and time in comprehending its network topology. In this paper, we propose a Bitcoin network topology discovery algorithm that utilizes lightweight probe nodes to facilitate rapid transmission of network protocols. Building upon this, we introduce a node layer clustering algorithm based on filtering stable network nodes, enabling parallel discovery of the network topology. Additionally, we present an adaptive method for dynamically displaying the layered structure of the network topology. Experimental results demonstrate that our proposed method reduces communication overhead by approximately 72.16% when achieving a 95% similarity in network topology. Furthermore, the algorithm is applicable for discovering the network topology in other blockchain networks with similar structures.
This paper aims to analyze and improve user experience (UX) in cryptocurrency applications. The study takes an interdisciplinary approach combining UX design principles, behavioral economics, and fintech innovation. Key UX challenges are identified, including cognitive complexity, security concerns, limited integration with traditional finance, and the impact of volatility. Innovative solutions are proposed with a focus on intuitive interfaces, educational elements and hybrid financial instruments. As an example, a cryptocurrency card with a line of credit function was presented. Quantitative results show a significant improvement in usability metrics: SUS scores improved, user retention rates increased, and key transaction times decreased. The study concludes that improving UX is critical for cryptocurrency adoption and integration into the global financial system. Future research directions include longitudinal studies on the impact of UX, developing standardized metrics, and exploring cultural factors of cryptocurrency interface perception.
Shijing Yuan, Qingshi Zhou, Jie Li, Song Guo · 7 authors
Edge computing significantly enhanced the growth of edge-assistant video streaming applications. However, challenges such as unpredictable wireless conditions, resource constraints, and task redundancy have intertwined impacts on the overall performance of edge video streaming systems (EVS). Therefore, it is essential to have an integrated framework that addresses resource management, computational offloading, and video task preprocessing. Existing optimization strategies often neglect the simultaneous management of computational offloading, resource allocation, and video task preprocessing, leading to a suboptimal system utility. Moreover, they struggle to handle high-dimensional decision variables. On the other hand, learning-based adaptive schemes fall short in integrating distributed decisions and ensuring the scalability of wireless devices. Additionally, current approaches lack adaptive incentives. To bridge these gaps, we propose a novel framework called AIRA, which is based on improved multi-agent reinforcement learning (MARL) and smart contracts. AIRA manages resources, video compression, and adaptive incentives in a distributed manner. It consists of a MARL-driven cooperative learning algorithm (CLA) and a smart contract-guided adaptive incentive mechanism. Leveraging an actor-critic structure, the CLA enables wireless devices to master strategies for resource allocation, video task compression, and offloading, utilizing historical data. Notably, the CLA incorporates an attention mechanism to select pivotal tuples from the observation-action pairings among different agents, ensuring improved scalability and computational prowess. Evaluations based on real-world trajectories demonstrate that AIRA enables adaptive incentives. Compared to state-of-the-art approaches, CLA effectively enhances the long-term system utility and scalability of EVS.
This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023. Leveraging SenticNet, we identified emotions like Fear and Anxiety, Rage and Anger, Grief and Sadness, Delight and Pleasantness, Enthusiasm and Eagerness, and Delight and Joy. Following data extraction, we segmented each month into bi-weekly intervals, replicating this process for price data obtained from Finance-Yahoo. Consequently, a comparative analysis was conducted, establishing connections between emotional trends observed across bi-weekly intervals and cryptocurrency prices, uncovering significant correlations between emotional sentiments and coin valuations.
The emergence of Distributed Ledger Technologies (DLT) in the past decade has challenged our imagination to discover new, innovative and disruptive solutions to problems in domains ranging from finance and healthcare to supply chain and Smart Cities. However, the enormous energy consumption that has been observed in some of the most successful DLT applications raises the question of their long term sustainability. This article reviews the standardization efforts of the International Telecommunications Union (ITU) to provide guidelines to regulators and policy makers for making informed decisions on the applicability and sustainability of DLT architectures from the point of view of energy consumption.
Minh-Duc Nguyen, Van Tong, Sami Souihi, Abdelhamid Mellouk
In the past, Quality of Service (QoS) was taken into account to evaluate the performance of multimedia services (e.g., video streaming, file transfer, etc.). However, it cannot reflect the user's perception, which is considered a crucial consideration by these services nowadays. Therefore, the emergence of Quality of Experience (QoE) is a potential solution. QoE can be measured via many parameters provided by Internet Service Providers (ISP), Application Service Providers (ASP), or end-users. However, privacy concerns hinder data sharing between the parties involved. To address these limitations, this paper proposes a QoE estimation mechanism that leverages Federated Learning. This mechanism aims to guarantee data privacy when no party needs to disclose their data to others. Moreover, the proposed mechanism incorporates the concept of a Decentralized Autonomous Organization (DAO) to mitigate the risk of a single point of failure in the centralized architecture of Federated Learning. It enables all participants to evaluate and select the model efficiently. The experimental results illustrate that the proposal surpasses the centralized solutions and guarantees data privacy.
Simone Casale-Brunet, Marco Mattavelli, Leonardo Chiariglione
In recent years, the concept of the metaverse has evolved significantly, with the aim of defining richer immersive and interactive environments that can support various types of virtual experiences and interactions among users. This evolution has given rise to several metaverse platforms that utilize blockchain technology and non-fungible tokens (NFTs) to establish ownership of metaverse elements and attach features and information to them. This article seeks to delve into the complexity and heterogeneity of the data involved in these metaverse platforms and highlight some of the dynamics and features that make them unique. Additionally, the paper introduces a metaverse analysis tool developed by the authors, which leverages machine learning techniques to collect and analyze daily data, including blockchain transactions, platform-specific metadata, and social media trends. The experimental results of our approach are presented with a use-case scenario focused on the trading of digital parcels, commonly referred to as metaverse real estate. This scenario allows us to demonstrate the effectiveness of our tool and showcase the potential of using machine learning techniques to analyze and gain insights into the metaverse ecosystem.
The adoption of non-fungible tokens (NFTs) has revolutionized digital art transactions, providing artists with unprecedented opportunities to tokenize and monetize their generative creations, leading to increased scrutiny and demand within blockchain-oriented marketplaces. The pricing of NFT artworks, however, exhibits substantial variations within and across collections, influenced by various factors. This study aims to investigate the relationship between visual features and pricing, shedding light on the variations underlying the pricing of NFTs. First, measures of both computational aesthetics and visual complexity were applied to extract multi-faceted visual aesthetic features, encompassing aesthetic factors such as color and composition as well as complexity factors like entropy. Second, with extracted visual aesthetic features and preprocessed price data, the study proceeds to conduct correlation analysis within collections and statistical modeling across collections. Through these approaches, we reveal a moderate correlation between visual features and prices within collections, while also identifying different influential visual features across collections. The differential performance of price models highlights the distinctiveness and unique pricing characteristics of NFT collections.
In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse.
Web 3.0 is an emerging Internet paradigm based predominantly on the blockchain technology. Because Web 3.0 applications are designed to operate over trustless and permissionless networks, they can have significant advantages, such as decentralized control structures and transparency. Hence, existing web applications are being reproduced using Web 3.0 technologies. In contrast, real-time services are still implemented with the Web 2.0 architecture. In particular, implementing Web 3.0 media streaming requires modifications to the service architecture of existing media streaming systems because some technical difficulties exist. For example, as data moves from centralized data centers to distributed storage, the user's quality of experience may be severely degraded. In addition, the software components comprising the Web 3.0 stack, such as interplanetary file system, cryptocurrency wallets, and the Ethereum JavaScript API are not compatible with various combinations of OSs, media players, and browsers. Therefore, in this study, we propose an end-to-end system architecture designed for Web 3.0 real-time services, which prevents degradation of service quality. Further, we present a media NFT marketplace named Retriever (https://retriever.live) fully developed using Web 3.0 technologies. Retriever allows users to enjoy watching video content and further to directly trade their content without intermediaries by ensuring the privacy of the data and managing digital intellectual property. In particular, Retriever does not sacrifice the user experience and is compatible with multiple mobile devices.
Evaluating1 the popularity of content, developing personalized recommendation algorithms, and optimizing advertising revenue are crucial aspects of Over-the-Top (OTT) media content services. In order to achieve these goals, accurate viewership measurement of OTT content is essential. However, due to the stringent security policies of mobile platforms, it has been challenging to propose an easy and reliable method for measuring viewership. In this paper, we propose an efficient and secure viewership measurement technique for OTT content on the iOS platforms. The proposed technique utilizes audio filtering and zero-knowledge proof techniques to address the challenges. By leveraging audio filtering, the content can be quickly identified, enabling accurate viewership measurement. Additionally, the use of zero-knowledge proof ensures the protection of users' personal information, preventing indiscriminate acquisition of user data in OTT services.
Recently, the concept of metaverse has been rapidly emerging, which highly expands the human living space. Specifically, 3D models are at the heart of building a vast metaverse space, so a massive number of 3D models are needed. Existing 3D model libraries and platforms have achieved great results. However, most of them are unscalable, insufficiently open, inefficient to collect, and at risk of service disruption and data corruption. Therefore, we propose and implement Web3DP, a crowdsourcing platform for 3D models based on Web3 (a.k.a. Web 3.0) infrastructure. By using the decentralized blockchain technology, Web3DP has the advantages of transparency, auditability, traceability, data tamper-proof, high file transfer efficiency, and service stability. Experiments are conducted to validate the performance of the proposed platform. It illustrates that Web3DP shows better file transmission capabilities with an acceptable transaction fee to facilitate 3D model collecting and managing for metaverse, games, cultural heritage, etc.
Simone Casale-Brunet, Leonardo Chiariglione, Marco Mattavelli
In recent years the concept of metaverse has evolved in the attempt of defining richer immersive and interactive environments supporting various types of virtual experiences and interactions among users. This has led to the emergence of various different metaverse platforms that utilize blockchain technology and non-fungible tokens (NFTs) to establish ownership of metaverse elements and attach features and information to it. This article will delve into the heterogeneity of the data involved in these metaverse platforms, as well as highlight some dynamics and features of them. Moreover, the paper introduces a metaverse analysis tool developed by the authors, which leverages machine learning techniques to collect and analyze daily data, including blockchain transactions, platform-specific metadata, and social media trends. Experimental results are reported are presented with a use-case scenario focused on the trading of digital parcels, commonly referred to as metaverse real estate.
The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactive and immersive socialization experience between people is one of the promises of the Metaverse. In spite of the rapid advancements in current technologies, the computation required for a smooth, seamless and immersive socialization experience in the Metaverse is overbearing, and the accumulated user experience is essential to be considered. The computation burden calls for computation offloading, where the integration of virtual and physical world scenes is offloaded to an edge server. This paper introduces a novel Quality-of-Service (QoS) model for the accumulated experience in multi-user socialization on a multichannel wireless network. This QoS model utilizes deep reinforcement learning approaches to find the near-optimal channel resource allocation. Comprehensive experiments demonstrate that the adoption of the QoS model enhances the overall socialization experience.
Reza Aria, Norm Archer, Moein Khanlari, Bharat Shah
This paper summarizes the work of many different authors, industries, and countries by introducing important and influential factors that will help in the development, successful adoption, and sustainable use of the Web3/metaverse and its applications. We introduce a few important factors derived from the current state-of-the-art literature, including four essential elements including (1) appropriate decentralization, (2) good user experience, (3) appropriate translation and synchronization to the real world, and (4) a viable economy, which are required for appropriate implementation of a metaverse and its applications. The future of Web3 is all about decentralization, and blockchain can play a significant part in the development of the Metaverse. This paper also sheds light on some of the most relevant open issues and challenges currently facing the Web3/metaverse and its applications, with the hope that this discourse will help to encourage the development of appropriate solutions.
Sheeba Backia Mary Baskaran, Tooba Faisal, Chonggang Wang, Diego López · 6 authors
Security, privacy, and trust are the key factors to unlock the full potential of future communication as beyond 5G and 6G systems enable new and more disruptive business models involved with multiple stakeholders, including mobile network operators, mobile virtual network operators, infrastructure providers, third party service providers, policy makers, end-users, and etc. The huge set of data, interaction process, and service management across these stakeholders require new technologies that can help the telecom industry to manage various data related to users, interaction process, and services in a way that is immutable, transparent, and secure with reduced OPEX to stay ahead of the market and to meet the evolving horizontal and vertical service and security requirements. This article presents an overview of the more promising key enablers, such as blockchain, permissioned distributed ledger technologies, and smart contract that can tackle the challenges of data security, privacy, and trust management in various potential beyond 5G and 6G application scenarios.
Abstract The potential of the metaverse in the field of education is an area of increasing interest, with many researchers exploring the space to increase the ease and efficacy of student education while reducing time and labor requirements to deliver effective teaching. However, there has been little work into the systematic and technological aspects of delivering education through the metaverse. To fill this gap, we propose a metaverse education system that takes good advantages of virtual reality and Web3 blockchain techologies to create a social learning environment. With this added emphasis on social aspects, learners are able to socialize and engage in collaborative efforts to improve their own knowledge. Using blockchain technology, the system can also help to ensure security and transparency while also keeping progression and grading fair for all participating students.
Compact block, which replaces transactions in the block with their hashes, is an effective means to speed up block propagation in the Bitcoin network. The compact block mechanism in Bitcoin counts on the fact that many nodes may already have the transactions (or most of the transactions) in the block, therefore sending the complete block containing the full transactions is unnecessary. This fact, however, does not hold in the Ethereum network. Adopting compact block directly in Ethereum may degrade the block propagation speed significantly because the probability of a node not having a transaction in the sending block is relatively high in Ethereum and requesting the missing transactions after receiving the compact block takes much additional time. To investigate the factors that prevent compact block in Ethereum, we set up probe nodes to collect data from Ethereum MainNet and performed data analysis. Our analysis results indicate that the missing transactions could be attributed to factors such as small transaction pools, network latency, and miners' selfish behaviors. Moreover, simply enlarging the transaction pool and using the prediction algorithm proposed for Bitcoin to predict the missing transactions and prefetch them do not work for Ethereum. This article proposes hybrid-compact block (HCB), an efficient compact block propagation scheme for Ethereum and other similar blockchains. First, we develop a Secondary Pool to store the low-fee transactions, which are removed from the primary transaction pool, to conserve storage space. As simple auxiliary storage, the Secondary Pool does not affect the normal block processing of the primary pool in Ethereum. Second, we design a machine learning-based transaction prediction module to precisely predict the missing transactions caused by network latency and selfish behaviors. We implemented our HCB scheme and other compact-block-like schemes (as benchmarks) and deployed a number of worldwide nodes over Ethereum MainNet to experimentally investigate them. Experimental results show that HCB performs best among the existing compact-block-like schemes and can reduce propagation time by more than half with respect to the current block propagation scheme in Ethereum.
Blockchain and edge computing have been widely applied in video streaming systems. However, previous works lack a joint consideration of video redundancy and full utilization of edge resources (bandwidth resources, CPU frequency), resulting in suboptimal performance of video streaming systems. In this paper, we propose a computing offloading framework for blockchain-enabled video streaming systems to fully exploit edge resources and reduce energy consumption. Specifically, we formulate computing offloading, resource allocation, and adaptive compression as a joint optimization problem. We transform and decompose the original non-convex problem and propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the decomposed problem in a distributed manner. Simulation results demonstrate that our scheme can effectively reduce energy consumption and fully utilize the bandwidth and computational resources.
Shijing Yuan, Jie Li, Hongyang Chen, Zhu Han · 6 authors
Edge computing has been introduced as a promising technology for real-time video streaming systems. However, due to the lack of automatic incentives and the limitation of resources, traditional edge computing performs poorly in nowadays scenarios. To handle these two challenges, we propose a framework ofJointIncentive design andResourceAllocation (JIRA) for edge-based real-time video streaming systems. Technically, to ensure the trust and automatic distribution of incentives, we develop a novel smart contract based incentive mechanism and implement a prototype. Meanwhile, we propose an efficient online algorithm, i.e., JIRA, which dynamically adjusts compression ratio, offloading decision, and resource allocation to achieve performance optimization for video streaming under long-term latency and resource constraints. Specifically, JIRA is based on Lyapunov optimization, which decomposes the challenging long-term decision problem into a series of real-time optimization problems. Then we propose a multi-cut Generalized Benders Decomposition based algorithm (MGA) to tackle the non-convexity of the decomposed problem. Through rigorous theoretical analysis, we prove the performance bound of JIRA. Extensive simulations demonstrate that the proposed schemes can achieve an efficient trade-off between accuracy performance and energy consumption.
Since Facebook officially changed its name to Metaverse in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users' digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments towards the use of blockchain in the metaverse in the future.