Zuobin Ying, Wusong Lan, Chen Deng, Lu Liu · 5 authors
The metaverse provides us with an attractive virtual space in which the value of the virtual property has been increasingly recognized. However, the lack of effective cross-metaverse trading tools and the reputation guarantee makes it difficult to trade items among different metaverses. To this end, a decentralized reputation system for virtual items trading forum named DVIT is devised. To the best of our knowledge, DVIT is the first decentralized cross-metaverse item trading prototype inspired by the online-game trading system. We designed the corresponding transaction function and realized the autonomous governance of the community by introducing the reputation mechanism. An improved election mechanism is proposed to improve efficiency based on Delegated Proof-of-Stake (DPoS). Through token rewards associated with activity levels, users’ motivation can be stimulated. The experiments indicate that our proposed scheme could dynamically measure the trustworthiness degree of the users through the dynamic reputation value and thereby exclude malicious users from the blockchain within 20 epochs.
Amaç: Global ticari markalar son yıllarda metaverse ile ilgili hedef kitleleri ile etkileşime girmek, marka imajını kuvvetlendirmek için çeşitli projelere imza atmaktadırlar. Bu makalenin amacı biri orta segmentte hızlı, ikincisi ise lüks segmentte moda markası olan 2 giyim markasının metaverse alanına giriş yaparak yönettikleri projelerin marka imajlarına ve takipçileri ile olan etkileşimlerine katkı sağlayıp sağlamadıklarının incelenmesi ve analizlerini içermektedir. Gereç ve Yöntem: Zaman içerisinde sınırlandırılmış birkaç durumu çoklu kaynakları içeren veri toplama araçları ile derinlemesine incelendiği, durumların ve duruma bağlı temaların tanımlandığı nitel bir araştırma yaklaşımı olarak özgün vaka incelemesi yöntemi ile değerlendirilerek açıklayıcı format kullanılmıştır. Bulgular: Metaverse için gerekli altyapıyı destekleyecek enerji kaynakları ve ekipman ürünlerinin yönetimi gibi sorunların aşılması gerekmektedir. Meta veri tabanının bileşenleri olan AR/VR teknolojileri, çevrimiçi çok oyunculu oyunlar, dijital sanal ikizler ve blok zincir uygulamaları bir süredir aktif olarak kullanılmaktadır. Ayrıca son dönemde oldukça popüler olan Non Fungible Token (NFT) tabanlı olan sanat eserlerinin, hediyelik eşyaların ve arazilerin alınıp satılmasına olanak sağlayan teknolojik gelişmeler, vaat edilen evrenin bir adım daha yakın olduğunu gösteriyor. Ancak bu kurgusal evrenin tam anlamıyla gerçekleşebilmesi için 5G teknolojisinin devreye girmesi, AR/VR donanım ve içeriğinin yaygınlaşması, peer2peer teknolojilerinin kullanımının artması gerekmektedir. Metaverse tasarlandığı gibi hayata geçirildiğinde, dijital bir evrende 3D avatar oluşturarak çalışmak, seyahat etmek, alışveriş yapmak, okula gitmek, eğlenmek gibi birçok günlük aktiviteyi gerçekleştirmek mümkün olacaktır. Kullanıcıların meta veri deposunda yaptığı herhangi bir değişiklik, neredeyse herkes tarafından kalıcı olarak görülebilecek ve böylece kullanıcılara daha fazla kimlik ve deneyim sürekliliği sağlanacaktır. Bu değişimlerin sosyo-kültürel ve psikolojik etkileri de incelenecek bir konu olacaktır. Sonuç: Metaverse şu an başlangıç aşamasında olup bu platformda istikrarlı ve belli bir stratejiye dayalı projeler üreten çok az marka vardır. Bunların çoğu da popüler olan global markalardır. Metaverse evreninin daha büyük kitleleri kendisine çekme yönünde gelişmesi için bu büyük global markaların yanı sıra lokal markaların da çok katman oluşturacak şekilde var olması gerekiyor ki bu evren daha hızlı bir şekilde gelişsin.
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.
The evolution of technology and science has brought groundbreaking developments to the current visual arts, and the application of digital technology has brought significant changes to the creation and aesthetic taste of traditional art. This study, therefore, investigates the current state-of-the-art artificial intelligence (AI) technologies and applications in generating visual art while giving a brief history of the intersection of AI and art, including the milestone advancements in neural networks. Fifteen interviews were conducted with technical artists who use text-to-image AI generators to gather data. Based on the findings from the interviews, the state-of-the-art applications were reviewed and analyzed in six categories: Accessibility, Barrier to Entry, Novelty, Ethics and Morality, Control, Non-fungible tokens (NFT) and Monetization which were widely discussed along with their success and limitations. The research concludes with three main findings; (a) monetization of digital media through NFTs that has a direct impact on the advancement of art generating AI applications, (b) there is a significant change in the traditional creative process with the integration of AI applications, and AI is not just a tool but it's a creative agent that artists collaborate with (c) art generating AI applications can generate limitless possibilities within the same aesthetics as a result revolutionize the way humans create and interact with art.
Aesthetic Perception and Analysis
Generative Adversarial Networks and Image Synthesis
The immense success of Bitcoin has also highlighted the hidden potential of blockchains and distributed ledgers in general. However, most blockchains are based on the so-called Proof-of-Work principle, which requires significant resources, making them unsuitable for the growing number of Internet of Things devices, not to mention other problems such as ensuring privacy and resistance to quantum computing. This paper, therefore, analyses current approaches in the field of ledgers with applications for the Internet of Things. Based on this, it presents a new ledger architecture for cultural heritage preservation that is energy sustainable and tailored to smartphones while pushing the deployment boundaries closer to the rest of the Internet of Things world. It is also resistant to quantum computing and provides privacy with accountability. Moreover, the developed solution considers not only the core technological structure but also its broader social (business) context, i.e., links to the tourism sector. This is performed by incorporating the relevant business model, as business models are important for the successful adoption of new technologies.
Yuchuan Fu, Changle Li, F. Richard Yu, Tom H. Luan · 6 authors
The virtual world created by the development of the Internet, computers, artificial intelligence (AI), and hardware technologies have brought various degrees of digital transformation to people’s lives. With multiple demands for virtual reality increasing, the metaverse, a new type of social ecology that can connect the physical and virtual worlds, is booming. However, with the rapid growth of data volume and value, the continuous evolution of the metaverse faces the demands and challenges of privacy, security, high synchronization, and low latency. Fortunately, the ever-evolving blockchain and intelligent networking technologies can be used to satisfy the trusted construction, continuous data interaction, and computing demands of the metaverse. Therefore, it is necessary to conduct an in-depth review of the role and gains of blockchain, intelligent networking, and the combination of both in providing the immersive experiences of the metaverse. In this survey, we first discuss the development trend, characteristics, and architecture of the metaverse. Then, the existing work on blockchain, networking, and the combination of the two technologies are reviewed, including overviews, applications, and challenges. Next, applications of the metaverse are summarized, emphasizing the importance of the metaverse and the fields of development. Finally, we discuss some open issues, challenges, and future research directions.
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.
Trustworthy and real-time video surveillance aims to analyze the live camera streams in a privacy-preserving manner for the decision-making of various advanced services, such as pedestrian reidentification and traffic monitoring. In recent years, edge computing has been identified as a promising technology for trustworthy and real-time video surveillance because it keeps confidential video data locally and reduces the latency caused by massive data transmission. Generally, a single edge device can hardly afford the computation-intensive video analytics tasks. Most existing solutions incorporate cloud servers to handle the overloaded tasks. However, such an edge-cloud collaboration approach still suffers from unpredictable latency and privacy concerns because the remote cloud is centralized and distant from the cameras. In this work, we designed a blockchain-based collaborative edge intelligence (BCEI) approach for trustworthy and real-time video surveillance. In BCEI, geo-distributed edge devices form a peer-to-peer network to maintain a permissioned blockchain and share data and computation resources to perform computation-intensive video analytics tasks. The video analytics results are written on the blockchain in an immutable manner to guarantee trustworthiness. To reduce task execution time, we formulate and solve a joint stream mapping and task scheduling problem to schedule video streams and machine learning models among edge devices. A pedestrian reidentification prototype is implemented and deployed based on BCEI with the extensive performance evaluation, indicating the superiority of BCEI in latency reduction and system throughput improvement by leveraging collaboration among edge devices.
As the core of blockchain technology, the consensus algorithm directly affects the security, stability, and decentralisation of the blockchain and numerous other important characteristics. Choosing an appropriate consensus algorithm for different scenarios is currently a challenge in the implementation of blockchain applications. This paper classifies the improvement schemes of proof of stake (PoS) into three categories: PoS-based consensus algorithms, PoS- and PoW-based consensus algorithms, and PoS- and BFT-based consensus algorithms. First, the study introduces the PoS and PoS consensus algorithm variants and then summarises the core ideas, effects, advantages, and disadvantages of these algorithms. Subsequently, the performances of the improved algorithms are compared. Finally, the main improved methods are summarised, and the most common network security attacks are discussed. The study lays a foundation for the main improvement directions of PoS in the future, hoping to provide a reference for researchers to help them select and design consensus algorithms in different application scenarios while also helping the evolution of consensus algorithms and the implementation of blockchain applications.
Tao Xiang, Honghong Zeng, Biwen Chen, Shangwei Guo
Medical image fusion generates a fused image containing multiple features extracted from different source images, and it is of great help in clinical analysis and diagnosis. However, training a deep learning model for image fusion usually requires enormous computing power, especially for large volumes of medical data. Meanwhile, the privacy of images is also a critical issue. In this article, we propose a privacy-preserving blockchain-based medical image fusion (BMIF) framework. First, to ensure fusion performance, we design a new medical image fusion model based on convolutional neural network and Inception network and integrate the proposed model into the consensus process of blockchain. Next, to save computing power of blockchain, we design a consensus mechanism by requesting consensus nodes to train the fusion model instead of calculating useless hash values in traditional blockchain. Then, to protect data privacy, we further present an efficient homomorphic encryption to realize the training of fusion model on encrypted medical data. Finally, we conduct theoretical analysis and extensive experiments on public datasets to evaluate the feasibility and the performance of our proposed BMIF. The results exhibit that BMIF is efficient and secure, and our medical image fusion network performs better than state-of-the-art approaches.
Improvements in fabrication devices such as 3D printers are becoming possible for personal fabrication to freely fabricate any products. To clarify who is liable for the product, the fabricator should keep the fabrication history in an immutable and sustainably accessible manner. In this paper, we propose a new scheme, "Fabchain," that can record the fabrication history in such a manner. By utilizing a scheme that employs a blockchain as an audit-able communication channel, Fabchain manages print jobs for the fabricator's 3D printer over the blockchain, while maintaining a history of a print job. We implemented Fabchain on Ethereum and evaluated the performance for recording a print job. Our results demonstrate that Fabchain can complete communication of a print job sequence in less than 1 minute on the Ethereum test network. We conclude that Fabchain can manage a print job in a reasonable duration for 3D printing, while satisfying the requirements for immutability and sustainability.
With the development of advanced information and communication technology, the traditional centralized cloud architecture cannot satisfy the exploding demand for data exchange in Internet of Vehicle (IoV) systems. Moreover, the traditional centralized architecture of the vehicular network has the potential risk of a single point of failure and lacks autonomy since the system highly relies on a trusted third party (TTP) to provide identity management. Fortunately, the emergence of blockchain technology provides a potential direction to address these problems. However, there are still some problems existing in the construction of an efficient blockchain system in IoV systems, such as the dynamic network topology and limited resources. In this paper, we propose a hierarchical resource scheduling scheme for blockchain-enabled IoV systems that improves the performance of the blockchain-enabled IoV system by efficiently allocating computational resources. The superiority of the proposed method is fully demonstrated by comparing it with existing baseline methods.
Citizens need to be involved to construct digital twins by sensing the urban environments using their IoT devices and sharing the sensed data among application providers. This "cooperative sensing" is one of the key solutions to achieve sustainable smart cities/super cities and offers improvement of the Quality of Life of citizens. In this view, blockchain and distributed ledger technology are collecting much attention to build a distributed data exchanging/sharing system for IoT data as well as cryptocurrency. However, the blockchain has a scalability issue in terms of large data volume and high-frequency transaction cases. In this paper, in order to reveal the comprehensive performance of blockchain when it is applied to sensor data sharing, we build a data-sharing system using Hyperledger Fabric which is one of the well-known blockchain platforms and evaluate the system performance by using a variety of sensing data, including images and 3D objects.
A dashboard camera (Dashcam) is attached to the front or rear of a vehicle to record images and video. As it is effective in crime prevention and accident management and it can also be used as learning data for Traffic Accident Detection technology for Autonomous Vehicles the use of Dashcam is increasing. However, the video data is stored on a memory card or a cloud server, so there is a high possibility of data loss and forgery. Although it is possible to prevent forgery and falsification using distributed storage based on blockchain technology, the authenticity and privacy of the image data stored in the blockchain cannot be guaranteed. In this study, to solve this problem, we propose a multi-signature-based access control method by grouping and storing video data of multiple vehicles based on GPS (Global Positioning System) data. To ensure the privacy of the video data stored in the blockchain, only users uploading video belonging to the relevant GPS can access nearby Dashcam videos. Through these experimental results, it show that Experimental results proposed method can maintain low latency in large-scale request environment to the privacy and reach data management efficiently in a distributed file system. According to our experiments, the dashcam video data distributed storage latency was fast enough, with an average of 25 ms for uploads and less than 15 ms for downloads. The signature generation time is 10ms, and the verification time required for access control is also less than 100 ms, which is the same even if the number of nodes increases, so scalability is not affected. The blockchain transaction processing took about 2 seconds, but it does not affect the V2V network. Also, client registration time does not affect performance.
Due to aggressive urbanization (with population size), waste increases exponentially, resulting in environmental damage. Even though it looks challenging, such an issue can be controlled if we can reuse them. To handle this, in our work, we design a machine learning and blockchain-oriented system that identifies thewaste objects/products and recommends to the user multiple ’Do-It-Yourself’ (DIY) ideas to reuse or recycle. Blockchain records every transaction in the shared ledger to enable transaction verifiability and supports better decision-making. In this study, a Deep Neural Network (DNN) trained on about 11700 images is developed using ResNet50 architecture for object recognition (training accuracy of 94%).We deploy several smart contracts in the Hyperledger Fabric (HF) blockchain platform to validate recommended DIY ideas by blockchain network members. HF is a decentralized ledger technology platform that executes the deployed smart contracts in a secured Docker container to initialize and manage the ledger state. The complete model is delivered on a web platform using Flask, where our recommendation system works on a web scraping script written using Python. Fetching DIY ideas using web-scraping takes nearly 1 second on a desktop machine with an Intel Core-i7 processor with 8 cores, 16 GB RAM, installed with Ubuntu 18.04 64-bit operating system, and Python 3.6 package. Further, we evaluate blockchain-based smart contracts’ latencies and throughput performances using the hyperledger caliper benchmark. To the best of our knowledge, this is the first work that integrates blockchain technology and deep learning for the DIY recommender system.
The emergence of technological innovations in disciplines such as Artificial Intelligence (AI) and the Internet of Things (IOT) has introduced opportunities to enhance the traditional user experience. The potential of these opportunities has yet to be fully explored particularly within the cultural heritage sector where immersive technologies can benefit both the user and the heritage site. A potentially unforgettable user experience can be offered in an immersive environment and these technologies can also be used to rebuild locations under threat to ensure they can be accessed in the future (Garlandini, 2021). The intention of this paper is to critically analyse the emergence of Industry 5.0 and the implementation of personalisation in a machine learning system. It is important to thoroughly evaluate the aspects of AI technologies and the IOT that can be used to support personalisation. An element of blockchain technology known as Non-Fungible Tokens (NFTs) which was developed in the recent past may have the potential to address ongoing challenges in digital curation and this research paper will provide an evaluation of this technology. It is crucial for all the technologies mentioned to work together to produce a system that aligns with the traditional human-centred philosophy and is inclusive to all participants.
In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.
Lik‐Hang Lee, Tristan Braud, Pengyuan Zhou, Lin Wang · 9 authors
Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We have created various computer-mediated virtual environments, including social networks, video conferencing, virtual 3D worlds (e.g., VR Chat), augmented reality applications (e.g., Pokémon Go), and Non-Fungible Token Games (e.g., Upland). Such virtual environments, albeit non-perpetual and unconnected, have brought us various degrees of digital transformation. The term “metaverse” has been coined to facilitate further digital transformation in every aspect of our physical lives. At the core of the metaverse stands the vision of an immersive Internet as a gigantic, unified, persistent, and shared realm. While the metaverse may seem futuristic, catalyzed by emerging technologies such as Extended Reality, 5G, and Artificial Intelligence, the digital “big bang” of our cyberspace is not far away. This survey presents the first effort to offer a comprehensive framework that examines the latest metaverse development under the dimensions of state-of-the-art technologies and metaverse ecosystems and illustrates the possibility of the digital “big bang”. It is essential to highlight that the metaverse encompasses diverse technologies and ecosystems, calling it an interdisciplinary and emerging field. Its primary objective is to provide users with satisfactory and interactive experiences. First, technologies are the enablers that drive the transition from the current Internet to the metaverse. We thus examine eight enabling technologies rigorously – Extended Reality, User Interactivity (Human-Computer Interaction), Artificial Intelligence, Blockchain, Computer Vision, IoT and Robotics, Edge and Cloud computing, and Future Mobile Networks. In terms of applications, the metaverse ecosystem allows human users to live and play within a self-sustaining, persistent, and shared realm. Therefore, we discuss six user-centric factors – Avatar, Content Creation, Virtual Economy, Social Acceptability, Security and Privacy, and Trust and Accountability. Finally, we propose a concrete research agenda for developing the metaverse.
Tianle Mai, Haipeng Yao, Ni Zhang, Lexi Xu · 6 authors
The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production to medical care. Dramatic advances in IoT technology not only brought enormous economic opportunities but also challenges (e.g., privacy and security vulnerabilities). Recently, with the appearance of blockchain technology, the integration of IoT and blockchain (BCoT) is considered a promising solution to address these issues. Blockchain provides a secure and scalable data management framework for IoT devices. However, the huge computation and energy cost of the consensus process in blockchain prevents it from being directly applied as a generic platform. To overcome this challenge, in this article, we propose a cloud mining pool-aided BCoT architecture, where the IoT devices can rent the computing resources from the cloud mining pools to offload the mining process. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices with different pooling strategies. We propose a centralized evolutionary game-based pool selection algorithm for the sake of maximizing the system utility. Considering the non-cooperative relationship among multiple miners, we also propose a lightweight distributed reinforcement learning algorithm, named the ‘WoLF-PHC’ algorithm.