Cryptocurrency airdrops power the growth and governance of the cryptocurrency ecosystem, yet attract airdrop hunters, who coordinate wallets, script interactions, and cash out quickly, distorting metrics and fairness. Prior detection strands (heuristics/clustering, light-supervised community partitioning, and graph learning) face three fundamentals: inconsistent definitions, weak explainability, and poor cross-context generalization. We distill expert knowledge into a computable, interpretable baseline: open/axial coding of expert narratives followed by two Delphi rounds to (1) formalize a consensus, operational definition with six contrasts to regular users; (2) derive 15 measurable indicators spanning operations and fund-flow, tempered by human-ness counter-evidence; and (3) report thresholds as reference distributions (medians, quartiles). The baseline supplies shared semantics and computation for labeling/evaluation, yields inspectable why-flagged rationales for audit and governance, and offers context-aware guidance across chains, campaign designs, and market phases, thereby strengthening on-chain security while informing the design of socio-technical systems perceived as fair, trustworthy, and resistant to strategic misuse.

The quest for carbon neutrality in the 21st century has led to the rise of decentralized low-carbon energy systems as a promising solution. Blockchain technology has played a pivotal role in catalyzing this transition, with various Web3 projects exploring decentralized operational models and carbon credit markets. However, there is a notable gap in harnessing blockchain’s potential to integrate electric vehicles (EVs) into low-carbon energy systems effectively. This article addresses this gap by proposing a decentralized low-carbon EV charging system that enables transactions between individual low-carbon energy producers and EV owners. Leveraging blockchain and smart contracts, the proposed system issues low-carbon tokens to certify and incentivize environmentally conscious charging behaviors, while enabling token circulation to further promote low-carbon participation. A blockchain-based double auction mechanism is designed to ensure fair and efficient energy allocation, achieving individual rationality, incentive compatibility, and social welfare maximization. By incentivizing user engagement and ensuring fair transactions, this model paves the way for sustainable EV integration within low-carbon energy systems.

User-Generated Content (UGC) refers to any form of content, such as posts and images, created by users rather than by professionals. In recent years, UGC has become an essential part of the evolving video game industry, influencing both game culture and community dynamics. The ability for users to actively contribute to the games they engage with has shifted the landscape of gaming from a one-directional entertainment experience into a collaborative, user-driven ecosystem. Therefore, this growing trend highlights the urgent need for summarizing the current UGC development in game industry. Our conference paper has systematically classified the existing UGC in games and the UGC editors separately into four types. However, the previous survey lacks the depth and precision necessary to capture the wide-ranging and increasingly complex nature of UGC. To this end, as an extension of previous work, this article presents a refined and expanded classification of UGC and UGC editors within video games, offering a more robust and comprehensive framework with representative cases that better reflects the diversity and nuances of contemporary user-generated contributions. Moreover, we provide our insights on the future of UGC, involving application of artificial intelligence, potential ethical considerations, relationship between games, users and communities, game culture, and game genre, and user creative tendencies.

This editorial introduces the first part of the Special Issue on “Advanced Technologies in the Decentralized Web.” As the Internet evolves toward more user-centric and resilient architectures, concepts like Web3 and Web 3.0 have gained significant prominence. This issue explores key advancements including decentralized machine learning, AI-blockchain integration, identity management, and data sovereignty. We provide an overview of the selected papers, highlighting their contributions to creating a more transparent and secure decentralized digital future.

International audience

Amid the rapid expansion of the Non-Fungible Token (NFT) market, X (formerly Twitter) has emerged as a crucial channel for communication between project creators and their communities. This study investigates the short-term effects of NFT project tweets on trading behaviors and price dynamics. Guided by Media Richness Theory (MRT), we con-ducted a quantitative analysis of tweets from nine leading NFT projects, categorizing them into three distinct clusters. Our findings reveal heterogeneous correlations between tweet content, NFT categories, and price fluctuations. The differing roles and functions of NFTs across categories shape both the distribution of tweets and their short-term pricing impacts. Furthermore, we employed three machine learning models using media richness as a predictive feature, achieving approximately 60 % accuracy in forecasting NFT price movements. Overall, this research highlights the predictive potential of social media for NFT price trends and its contribution to the NFT ecosystems sustainability.

This study explores the role of governance tokens in shaping player communities in blockchain games, focusing on Axie Infinity. Blockchain games introduced governance tokens to empower players with decision-making capabilities, enhancing decentralization. However, their impact on community dynamics remains insufficiently understood. This research adopts a data-driven approach, collecting AXS transaction data, constructing monthly token transfer networks, and analyzing community evolution using a detection algorithm. The results reveal an increasing trend towards decentralization over time. While larger communities generally have longer lifespans, surpassing a certain size threshold can accelerate their decline due to decentralization. Maintaining stability within community membership is essential for longevity, highlighting the roles played by experienced players and established communities in governance.

Mesh simplification of 3D models can accelerate rendering, reduce storage space, and improve performance. However, for high-poly 3D models, there are ongoing concerns about potentially compromising the Quality of Experience (QoE), the need to set simplification ratios or parameters, and the time-consuming nature of the simplification process. To address these issues, we proposed a new mesh simplification for the preprocessing step. Based on the Quadratic Error Metric (QEM) simplification algorithm, we conducted human-centered 3D model comparison experiments to determine the optimal simplification ratio for high-poly 3D models in full body shots. From experimental data, we proposed and implemented ASimp, an automatic 3D mesh simplification scheme. In evaluation experiments, ASimp demonstrated rapid preprocessing speeds while ensuring QoE and the effectiveness of its simplification products. We hope that ASimp will contribute to the optimization of 3D models and find applications in fields such as cultural heritage, archaeology, visual effects, video games, medicine, metaverse, and beyond.

Current 3D model Level of Detail (LOD) methods require multiple models with varying detail levels to reduce client computational load, transmitting different models based on the user's distance to the object. However, this process consumes excessive network bandwidth and strains the client's memory and storage. To address this, we propose BS3 (Bézier Slicing for 3Ds), a middleware-enabled method that slices 3D meshes and fits the contours using Bézier curves. Acting as an intermediate layer, the BS3 middleware handles slicing, vectorization, sampling and reconstruction, allowing .bs3 files to be streamed only once and adjusted dynamically at different sampling rates. Our experiments demonstrate the efficiency and performance analysis of BS3, which shows that it can reduce network and storage burdens while keeping the display effect. We believe that BS3 will enhance 3D multimedia in the game, exhibition, digital museum, cultural heritage, metaverse, etc.

In mobile edge computing (MEC), one optimization strategy for mobile applications is to offload heavy computing tasks to cloud and edge servers. Constructing partitioning algorithms involves modeling individual methods through static code profilers, but exploiting dynamic user-driven execution patterns is also crucial. This paper introduces PartFlow, an interactive visualization system that supports comprehensive analysis of mobile application components and aids researchers in developing partitioning and offloading algorithms using real human behavioral data. PartFlow collects application component data remotely through binary instrumentation of mobile applications. Interactive diagrams are designed to evaluate component performance and illustrate transition patterns using the collected data. Additionally, PartFlow integrates a deep learning (DL)-based approach for multi-step forecasting of component states to improve accuracy and user experience in algorithm design. A case study and user feedback demonstrate PartFlow’s effectiveness in assisting researchers and engineers in creating offloading strategies.

In the rapidly evolving Web3 world, non-fungible token (NFT) communities are reshaping the formation, distribution, and activation of social capital in ways distinct from traditional models. However, despite their growing impact on societal prosperity, a comprehensive understanding of social capital dynamics within Web3 NFT communities remains limited. This study explores the Mfers community, a key example within Web3 NFT ecosystems. By analyzing social media and blockchain data and using a Delphi method-based human-large language model (LLM) collaboration, we uncovered unique social capital patterns across six dimensions. Our findings highlight a compelling blend of decentralization, inclusion, trust, and empowerment but also raise critical questions about wealth inequality, content quality, and ethical challenges. Based on the findings, we discussed the uniqueness of social capital in Web3 NFT communities, the tension between technical and power decentralization, and the multidimensional nature of societal prosperity. We also suggested directions for future research on decentralized online communities in the CSCW field. This study provides a systematic perspective on social capital in Web3 NFT communities and introduces an innovative human-LLM collaborative analysis, offering insights into the design and governance of benign decentralized online communities.

Empowered by blockchain technology, smart contracts have attracted considerable interest from Web3 users due to their distinct advantages. Nevertheless, it is challenging to address problems caused by the dramatic expansion of the Web3 ecosystem. This article introduces the smart contract-as-a-service (SCaaS) paradigm to mitigate smart contracts’ redundant deployment via their composability and reusability. Moreover, we design trust and incentive schemes to ensure project security and developer engagement in SCaaS. Specifically, we first introduce a reputation filter by leveraging the authentic on-chain data, aiming to eliminate high-risk contracts. We then design a contract-based incentive mechanism to help the foundation attract heterogeneous developers with multidimensional private information, and maximize the foundation’s utility by inducing developers to undertake projects of differing complexities based on their ability. We further differentiate between veteran and newcome developers and examine their influences on foundational strategies. Finally, extensive experimental results demonstrate that our proposed contracts can efficiently remove high-risk smart contracts, maximize the foundation’s utility, and ensure that developers select contracts honestly and participate in the SCaaS ecosystem actively.

Blockchain technologies, particularly blockchain-based digital twins (DTs), have gained widespread interest in academia and industry. Despite this, existing literature frequently overlooks the unique characteristics of location information for consensus nodes in DTs, often merging them with conventional blockchain frameworks. This article introduces LocPoS, a novel location-based proof-of-stake mechanism tailored for industrial DTs, bridging this gap. Our proposed model strives to enhance security via broader consensus node distribution. Despite this enhanced security, potential for dishonest behavior by users for gain maximization persists. We perform an in-depth analysis of user strategies and devise a mechanism to inhibit malicious behavior, fostering truthfulness among all users. Our theoretical and simulation results demonstrate that LocPoS satisfies truthfulness and individual rationality, while reducing the proportion of malicious nodes in the consensus group by over 50% compared to PoS and DPoS, thereby significantly improving consensus integrity. The proposed mechanism’s effectiveness in ensuring truthful behavior and secure node selection is validated through both analytical proofs and extensive simulations.

In the era of Big Data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale model training demands high financial and computational resources, which are only affordable by a few technological giants and well-funded institutions. In this case, common users like mobile users, the real creators of valuable data, are often excluded from fully benefiting due to the barriers, while the current methods for accessing largescale models either limit user ownership or lack sustainability. This growing gap highlights the urgent need for a collaborative model training approach, allowing common users to train and share models. However, existing collaborative model training paradigms, especially federated learning (FL), primarily focus on data privacy and group-based model aggregation. To this end, this paper intends to address this issue by proposing a novel training paradigm named decentralized relay learning (DeRelayL), a sustainable learning system where permissionless participants can contribute to model training in a relay-like manner and share the model. In detail, this paper presents the architecture and workflow of DeRelayL, designs incentive mechanisms to ensure sustainability, and conducts theoretical analysis and numerical simulations to demonstrate its effectiveness

In the Web3 ecosystem, multimedia applications exhibit significant potential by leveraging decentralization, regarded as the core spirit of Web3. This survey aims to provide a comprehensive overview of the potential of decentralization in shaping multimedia applications in the Web3 ecosystem. Through a systematic review of the academic research conducted over the past decade on Web3 decentralization, we identify the two key distinctive decentralization characteristics (decentralized assets and decentralized participation). Subsequently, we comprehensively analyze Web3 applications from both technology and application dimensions. Building upon this, we focus on multimedia-related aspects and propose an architecture for Web3 multimedia applications. In contrast to the broader scope of Web3 applications, the unique aspects of Web3 multimedia applications reside in their core application components (non-fungible tokens and smart contract-based rules) and core application domains (art, games, and social media). Based on this architecture, we provide a precise definition of Web3 multimedia applications. Lastly, through the lens of the two identified distinctive decentralization characteristics, we investigate the advantages, development, and limitations of Web3 multimedia applications within the three core application domains, namely crypto art, blockchain games, and blockchain on social media (BOSM). Furthermore, we share our insights into several promising yet challenging directions, covering the interoperability and potential of increasingly valuable multimedia content, as well as the delicate balance between centralization and decentralization.

The progressions in blockchain technologies have propelled the advancement of the Internet of Things (IoT), particularly the Industrial IoT (IIoT) system, toward a decentralized structure. This shift is driven by the need to address the privacy and security vulnerabilities inherent in centralized organizations. In a blockchain-based IoT system, the IoT nodes are tasked with both mining and data collection activities concurrently. However, due to the limited resources of these nodes, a rational resource allocation mechanism is imperative to maximize social welfare. Insufficient allocation of resources to mining can significantly reduce the overall Hashrate of the blockchain, thereby jeopardizing its security. Conversely, prioritizing resource allocation to mining can diminish the value of collected data, resulting in a decrease in overall social welfare. To tackle these challenges, this article proposes a transaction fee mechanism that levies charges on data consumers based on the resources utilized by the nodes for data collection. The objective is to achieve a balanced allocation of resources between mining and data collection while ensuring the security of the blockchain. Specifically, we model the interactions among the system designer, data consumer, and IoT nodes as a three-stage Stackelberg game. By characterizing the equilibrium of the game, we derive optimal strategies that lead to the maximization of social welfare. The effectiveness of the proposed approach is validated through extensive experimental results.

Non-Fungible Tokens (NFTs) have emerged as a pivotal digital asset, offering authenticated ownership of unique digital content. Despite it has gained remarkable traction, yet face pressing storage and verification challenges stemming from blockchain's permanent data costs. Existing off-chain or centralized storage solutions, while being alternatives, also introduce notable security vulnerabilities. We present SemNFT, an innovative decentralized framework integrated with blockchain oracle middleware services, addressing these persistent NFT dilemmas. Our approach compresses NFT source data into compact embeddings encapsulating semantic essence. These arrays are stored on-chain, while facilitating reliable decentralized image reconstruction and ownership verification. We implemented ERC721-compliant smart contracts with supplementary functionalities, demonstrating SemNFT's seamless integrative capabilities within the ecosystem. Extensive evaluations evidence marked storage optimizations and preservation of requisite visual fidelity by comparison with existing solutions. The proposed SemNFT framework marks a significant advancement in holistically confronting rising NFT storage and verification challenges without compromising decentralization. It substantively propels the meaningful evolution of NFT infrastructure to achieve digital asset immortality.

As a bridge connecting the Web3 financial ecosystem and digital games, smart contracts empowered blockchain games have attracted significant attention from the Web3 community in recent years. By providing players ownership over assets and interoperable Non-Fungible Tokens (NFTs), blockchain games enable the reuse of in-game assets beyond the original games, thereby overturning the “walled garden” among traditional games. Nonetheless, blockchain games diminish the monopolistic edge previously held by traditional game providers, forcing them to compete with players by token distribution. Therefore, this paper explores the duopoly competition within the blockchain game market, emphasizing the role of interoperable NFTs together with NFT wear and tear. Specifically, we propose a three-stage game to formulate the interactions between game providers and players. Besides, we revealed the relationship between game providers' code disclosure strategies for NFT interoperability and token retention strategies. Finally, the experimental results demonstrate how the token distribution, players' preferences, and the NFT wear level affect the profits of game providers.

Unlike fungible tokens (e.g., cryptocurrency), a Non-Fungible Token (NFT) is unique and indivisible. As such, they can be used to authenticate ownership of digital assets (e.g., a photo) in a decentralized fashion. Given that NFTs have generated significant media attention since 2021, we perform a large-scale measurement study of the NFT ecosystem. We collect over 242M transfer logs and over 97M marketplace transactions until Aug 1st, 2023, by far the largest NFT dataset, to the best of our knowledge. We characterize the on-chain behavior of NFTs and their trading across five major marketplaces. We find that, although the NFT ecosystem is growing rapidly, it is driven by a relatively small set of dominant centralized players, with suspicious trade activities, e.g., over 23% of the monetary volume is generated by malicious wash trading and the ecosystem has experienced over 157K cases of NFT arbitrage, with a total sum of over \25M profit. Our observations motivate the need for more research efforts in the NFT security analysis.

As Web3 projects leverage airdrops to incentivize participation, airdrop hunters tactically amass wallet addresses to capitalize on token giveaways. This poses challenges to the decentralization goal. Current detection approaches tailored for cryptocurrencies overlook non-fungible tokens (NFTs) nuances. We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. ARTEMIS captures NFT airdrop hunters through: (1) a multimodal module extracting visual and textual insights from NFT metadata using Transformer models; (2) a tailored node aggregation function chaining NFT transaction sequences, retaining behavioral insights; (3) engineered features based on market manipulation theories detecting anomalous trading. Evaluated on decentralized exchange Blur's data, ARTEMIS significantly outperforms baselines in pinpointing hunters. This pioneering computational solution for an emergent Web3 phenomenon has broad applicability for blockchain anomaly detection. The data and code for the paper are accessible at the following link: \hrefhttps://doi.org/10.5281/zenodo.10676801 doi.org/10.5281/zenodo.10676801.

People favor nonfungible token (NFT) because of the attribute to prove digital assets’ ownership and promote interactions. Investors are keen to buy and use NFT pictures as social media avatars and participate in online communities around NFT collections. However, information manipulation in the NFT market has led to investors significant losses. Our work explored a way to correspond social media accounts with Ethereum addresses and studied the microstructure of NFT market. Taking Goblintown.wtf as an example, we analyzed the participants, mechanism, and impact of Twitter information manipulation in the market. We found five categories of investors in the NFT market under information manipulation: primary investors, amateur investors, fanatic investors, short-term rational investors, and long-term rational investors. We argue that investors will consume their limited attention more likely when joining NFT online communities. This will lead to more complicated for them to make investment decisions rationally.

Understanding whether and how online developer communities resolve conflicts that occur in discussions is critical. Few studies focused on the conflicts caused by external crises, such as geopolitical events (e.g., the 2022 Russo-Ukrainian Crisis). We comparatively studied how a decentralized autonomous organization (DAO), Aave project community, and a centralized autonomous organization (CAO), GitHub project community, managed external crises caused by conflicts. Our mixed-method analysis showed that a DAO could be better than a CAO for mitigating conflicts. And blockchain technologies (i.e., voting and cryptocurrency) played vital roles. To address the low voter turnout, we proposed adding a monetary incentive to engage more DAO members in forming common goals.

To broaden domain knowledge and enable advanced analytics, machine learning (ML) algorithms increasingly utilize comprehensive datasets across diverse sectors. However, these disparate datasets held by various stakeholders raise concerns over data heterogeneity, privacy, and security. Decentralized ML research aims to protect data privacy and integrate knowledge bases, especially knowledge graphs, to address data heterogeneity challenges. Yet, the question of how to foster trustworthy collaborations in decentralized ML ecosystems remains underexplored. This study pioneers two innovative socio-economic mechanisms designed to ensure dependable collaborations with socio-ethical integrity within a decentralized knowledge inference framework, enabling participants to share knowledge while maintaining data privacy and ethical standards. We employ an evolutionary game theory model to analyze the dynamic interactions between requestors and workers, focusing on achieving a stable equilibrium through theoretical and numerical evaluations. Furthermore, we explore how various critical factors, such as incentive schemes and the accuracy of identifying malicious workers, influence the system's equilibrium, providing insights into optimizing collaborative efforts in decentralized ML ecosystems.

The recent development of generative art, a typical category of artificial intelligence-generated content (AIGC), is essentially beneficial for social good, which can help amateurs to create artwork and improve experts’ efficiency. However, some artists are opposed to generative art technologies due to the copyright infringement and influence of the artists’ way of earning a living, which makes the artists protest against generative art technologies, causing a lose–lose situation. Adversarial attacks against generative model training are potential solutions to address this issue, while the lose–lose situation cannot be improved. To build a win–win situation, a feasible method is to incentivize the artists to actively contribute their artworks to generative model training without influencing their living or infringing copyright, such as data crowdsourcing, but traditional data crowdsourcing methods cannot well fit the generative art area. Therefore, this article builds a blockchain-based trading system for generative model training data collection and generated artwork circulation. Specifically, this article formulates a social welfare maximization problem based on the reverse auction and designs a corresponding incentive mechanism. The conducted theoretical analysis and numerical evaluation demonstrate the effectiveness of the proposed incentive mechanism toward a win–win situation for generative art model trainers and artists.

Blockchain technology has garnered significant attention in recent years due to its capacity to offer secure and transparent transactional systems. However, the technology's inherent immutability can present challenges in specific scenarios. While earlier research has concentrated on the development of redactable blockchain, existing solutions have primarily focused on the modification mechanism, often overlooking the critical element of an incentive mechanism for governance, which is paramount for ensuring the security of redactable blockchain. Some previous researches have explored the design of incentive mechanisms, but these studies exhibit certain shortcomings. To promote active participation, we have designed an incentive mechanism rooted in evolutionary game theory for stakeholders in redactable blockchain, aiming to facilitate the governance of redactable blockchain. Furthermore, we have conducted a comprehensive simulation founded on game-theoretic analysis. The results substantiate the effectiveness of our redactable blockchain incentive mechanism in achieving its intended objectives.

With the rapid advancement of free-to-play games featuring in-game payments, the loot box sales model challenges traditional fixed-price purchases by stimulating players’ psychological desires. However, concerns have arisen among players regarding the transparency of loot box drop rates as claimed by game companies. Considering players’ “partial information” and “bounded rationality” in-game, this study explores strategic interactions and evolutionary outcomes under various decision-making scenarios through a bipartite evolutionary game model, employing the replication dynamics approach. In contrast to conventional studies that examine market manipulation post hoc, evolutionary game theory enables a proactive examination of strategies to foster fairness within the loot box game market. The findings indicate that the dynamic between companies and players may ultimately converge to either a lose–lose or win–win scenario. Through simulation analysis, the impact of stakeholders’ initial strategic ratios and critical parameters on the evolutionary trajectory is examined. It is observed that companies hold a dominant position in enhancing cooperation, whereas regulatory bodies can foster industry development through heightened regulation. This assertion is substantiated with real-world examples. The goal of this research is to advocate for integrity and transparency within the gaming industry, ensuring a fair environment for players.

As mobile applications grow increasingly computation-intensive, the challenges arising from the limitations of mobile devices in terms of computing resources and battery life become more pronounced. Mobile Edge Computing (MEC) provides a promising avenue to address these challenges and enhance user experience. While existing studies have extensively explored resource allocation and task scheduling in MEC, most treat tasks as monolithic entities, overlooking the nuanced subtasks/components that often make up mobile applications. This paper endeavors to bridge the gap between the need for incentive mechanisms and the offloading of dependent computation tasks in MEC. Drawing inspiration from auction theory, we introduce a novel Multi-stage Iterative Combinatorial Double Auction (MICDA) mechanism, specifically tailored for dependent tasks in a cloud-edge-end cooperative computing scenario. Through theoretical analysis, the MICDA mechanisms demonstrate truthfulness, individual rationality, budget balance, and computational efficiency. Comprehensive experiment results further confirm its superior performance in improving application makespan and social welfare compared to other existing offloading strategies. This work validates the effective integration of dependency-aware computation offloading and auction mechanisms in overcoming economic and computational challenges in MEC systems, thereby paving the way for their potential application in broader real-world scenarios.

The metaverse, as a rapidly evolving socio-technical phenomenon, exhibits significant potential across diverse domains by leveraging Web3 (a.k.a. Web 3.0) technologies such as blockchain, smart contracts, and non-fungible tokens (NFTs). This survey aims to provide a comprehensive overview of the Web3 metaverse from a human-centered perspective. We (i) systematically review the development of the metaverse over the past 30 years, highlighting the balanced contributions from its core components: Web3, immersive convergence, and crowd intelligence communities, (ii) define the metaverse that integrates the Web3 community as the Web3 metaverse and propose an analysis framework from the community, society, and human layers to describe the features, missions, and relationships for each community and their overlapping sections, (iii) survey the state-of-the-art of the Web3 metaverse from a human-centered perspective, namely, the identity, field, and behavior aspects, and (iv) provide supplementary technical reviews. To the best of our knowledge, this work represents the first systematic, interdisciplinary survey on the Web3 metaverse. Specifically, we commence by discussing the potential for establishing decentralized identities (DID) utilizing mechanisms such as profile picture (PFP) NFTs, domain name NFTs, and soulbound tokens (SBTs). Subsequently, we examine land, utility, and equipment NFTs within the Web3 metaverse, highlighting interoperable and full on-chain solutions for existing centralization challenges. Lastly, we spotlight current research and practices about individual, intra-group, and inter-group behaviors within the Web3 metaverse, such as Creative Commons Zero license (CC0) NFTs, decentralized education, decentralized science (DeSci), and decentralized autonomous organizations (DAO). Furthermore, we share our insights into several promising directions, encompassing three key socio-technical facets of Web3 metaverse development.

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle these issues in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on extra equipment in the conference venues, such as the infrared camera or special microphone, which are not practical. In this article, we design and implement Meetor, a human-centered automatic video editing system for meeting recordings. The Meetor mainly contains three parts: an audio-based video synchronization algorithm, human-centered video content flaw detection algorithms, and an automatic video editing algorithm. Two main experiments are conducted from both objective and subjective aspects to evaluate the performance of the Meetor. The experimental results on a testbed illustrate that the proposed algorithms could achieve state-of-the-art (SOTA) performance in video content flaw detection. However, the conducted user study demonstrates that Meetor could generate meeting recordings with a satisfactory quality compared with professional video editors. Moreover, we also present a practical application of the Meetor in a university campus prototype, in which the Meetor is applied in the automatic editing of lecture recordings. All in all, the proposed Meetor can be utilized in practical applications to release the workload of professional video editors.

Regardless of which community, incentivizing users is a necessity for well-sustainable operations. In the blockchain-backed Web3 communities, known for their transparency and security, airdrop serves as a widespread incentive mechanism for allocating capital and power. However, it remains a controversy on how to justify airdrop to incentive and empower the decentralized governance. In this paper, we use ParaSwap as an example to propose a role taxonomy methodology through a data-driven study to understand the characteristic of community members and the effectiveness of airdrop. We find that users receive more rewards tend to take positive actions towards the community. We summarize several arbitrage patterns and confirm the current detection is not sufficient in screening out airdrop hunters. In conjunction with the results, we discuss from the aspects of interaction, financialization, and system design to conclude the challenges and possible research directions for decentralized communities.

Metaverse provides users with a novel experience through immersive multimedia technologies. Along with the rapid user growth, numerous events bursting in the metaverse necessitate an announcer to help catch and monitor ongoing events. However, systems on the market primarily serve for esports competitions and rely on human directors, making it challenging to provide 24-hour delivery in the metaverse persistent world. To fill the blank, we proposed a three-stage architecture for metaverse announcers, which is designed to identify events, position cameras, and blend between shots. Based on the architecture, we introduced a Metaverse Announcer User Experience (MAUE) model to identify the factors affecting the users' Quality of Experience (QoE) from a human-centered perspective. In addition, we implemented MetaCast, a practical self-driven metaverse announcer in a university campus metaverse prototype, to conduct user studies for MAUE model. The experimental results have effectively achieved satisfactory announcer settings that align with the preferences of most users, encompassing parameters such as video transition rate, repetition rate, importance threshold value, and image composition.

Mainstream blockchain games have drawn criticism for prioritizing economic systems over gameplay experience. Influenced by these economically-centered games, existing research on blockchain games predominantly focuses on the financial sector. We have developed BranchClash, a fully on-chain tower defense game on the Sepolia testnet of Ethereum. It introduces chain collaboration, a novel non-economically-centered game mechanism inspired by blockchain technology. BranchClash aims to expand unique game mechanics in blockchain games and explore innovative cooperative modes within the decentralized ecosystem.

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.

Mobile Edge Computing (MEC) enables computation offloading from resource-constrained mobile devices to edge servers in close vicinity, effectively promoting the user experience on emerging interactive multimedia applications such as virtual/augmented reality, mobile gaming, and mobile video editing. However, most contemporary MEC offloading research disregards the interdependencies between partitioned subtasks of application. Also, few studies focused on application topologies have neglected to design effective incentives to encourage edge servers to provide offloading services. In this paper, we propose a dependency-aware offloading algorithm based on a multi-round truthful combinatorial reverse auction (MTCRA) to address the social welfare maximization problem in the paradigm of MEC. Building on the topology of directed acyclic graphs (DAGs) modeled from applications, we discuss the complementarity and substitutability of subtasks in the context of combinatorial auction. Theoretical analysis shows that the presented auction mechanism achieves computing efficiency while maintaining desirable economic features like truthfulness, individual rationality, and budget balance. Simulation results demonstrate that the proposed algorithm achieves high social welfare regarding reduced execution time and good economic benefits for MEC servers.

Cloud gaming is a novel service provisioning technology that offloads parts of game software from terminals to powerful cloud infrastructures. However, the commercial charging model for cloud gaming is still in its infancy. In this paper, we reveal the deficiencies of existing cloud gaming pricing models and propose CryptoArcade, a token-based cloud gaming system that adopts cryptocurrency as a payment method. Using cryptocurrency, CryptoArcade provides a transparent and resource-aware pricing method, enabling a time irrelevant silent payment on the floating price to protect players' interests, which avoids the Quality of Experience (QoE) degradation caused by traditional dynamic models. While CryptoArcade can solve the problem of pricing strategies, players still face decision headaches caused by having commission overhead and pre-deposit amounts on blockchains. To better understand players' trading behaviors in this decision-making, we consider a marketplace where players trade tokens through smart contracts before gaming sessions. Considering the uncertainty of future token consumption, we use Prospect Theory (PT) in modeling and obtain the optimal solution in closed form. When comparing with the benchmark expect utility theory (EUT), we show that with the same external factors, EUT players are more likely to buy tokens than PT ones.

The term “metaverse” was first introduced in Neal Stephenson’s 1992 science fiction novel, Snow Crash. It is conceived as the successor to the contemporary Internet, wherein users, represented as avatars, can interact with others or with applications within a three-dimensional (3D) virtual space, which is ubiquitously accessible. Although the metaverse remains a digital construct, establishing a sophisticated virtual societal framework—including a stable economic system—is paramount as users acquire assets and foster communities therein[4]. The implications become profound and potentially unpredictable should any single entity gain dominance over this virtual societal infrastructure. Such anxieties have been vividly portrayed in recent cinematic offerings such as “Ready Player One” and “Free Guy.” In response, blockchain technology emerges as a promising countermeasure. Trailblazing metaverse platforms leveraging blockchain, such as Decentraland, CryptoVoxels, and Sandbox, utilize cryptocurrency and nonfungible tokens (NFTs) to define programmable assets or access privileges[11]. These tokens may facilitate a borderless and frictionless payment layer, ensuring the uniqueness, persistence, and tradability of users’ digital assets[9]. Furthermore, the rise of smart contract-driven decentralized applications (DApps)[10]—spanning decentralized finance (DeFi) to innovative social applications[5]— ushers in an era of transparent, self-regulating digital ecosystems. As a multimedia community predicated upon vast online participation, advancements in blockchain may pave the way for a fair, transparent, and sustainable metaverse[13].

Applying peer-to-peer (P2P) architecture to online video games has already attracted both academic and industrial interests, since it removes the need for expensive server maintenance. However, there are two major issues preventing the use of a P2P architecture, namely how to provide an effective distributed data storage solution, and how to tackle potential cheating behaviors. Inspired by emerging blockchain techniques, we propose a novel consensus model called Proof-of-Play (PoP) to provide a decentralized data storage system that incorporates an anti-cheating mechanism for P2P games, by rewarding players that interact with the game as intended, along with consideration of security measures to address the Nothing-at-stake Problem and the Long-range Attack. To validate our design, we utilize a game-theory model to show that under certain assumptions, the integrity of the PoP system would not be undermined due to the best interests of any user. Then, as a proof-of-concept, we developed a P2P game ( Infinity Battle ) to demonstrate how a game can be integrated with PoP in practice. Finally, experiments were conducted to study PoP in comparison with Proof-of-Work (PoW) to show its advantages in various aspects.

User-generated content (UGC) is any form of content that has been created by users rather than the developers of online platforms. The UGC has been playing a very important role in video games. For instance, Counter-Strike (CS) and Defense of the Ancients (DOTA) originated from modifications of Half-Life and Warcraft III: Reign of Chaos respectively. As a promising trend, the UGC will be increasingly developed and extended to wider scopes in virtual worlds, such as the metaverse, which is highly promising for further study in both academia and industry. However, there are few existing surveys that systematically discuss the UGC in video games. In this paper, we systematically review the representative UGC in video games and their corresponding UGC editors based on a decision tree-style classification method. Then we enumerate the propagation methods of UGC in video games. Moreover, we propose our vision of the future development of UGC in the metaverse.

Blockchain games introduce unique gameplay and incentive mechanisms by allowing players to be rewarded with in-game assets or tokens through financial activities. However, most blockchain games are not comparable to traditional games in terms of lifespan and player engagement. In this paper, we try to see the big picture in a small way to explore and determine the impact of gameplay and financial factors on player behavior in blockchain games. Taking Aavegotchi as an example, we collect one year of operation data to build player profiles. We perform an in-depth analysis of player behavior from the macroscopic data and apply an unsupervised clustering method to distinguish the attraction of the gameplay and incentives. Our results reveal that the whole game is held up by a small number of players with high-frequent interaction or vast amounts of funds invested. Financial incentives are indispensable for blockchain games for they provide attraction and optional ways for players to engage with the game. However, financial services are tightly linked to the free market. The game will face an irreversible loss of players when the market experiences depression. For blockchain games, well-designed gameplay should be the fundamental basis for the long-lasting retention of players.

Incentive mechanism design in crowdsourcing is a trending topic. Advanced research attempts to tackle this issue from a game-theory perspective, modeling workers’ and requestor’s material utility functions. Besides material benefits, studies have shown that the intrinsic rewards (psychological factors) were also part of the workers’ non-negligible motivation. However, previous works only mention this discovery textually, rather than quantifying their models’ psychological factors. To fill this blank, we utilize the psychological game theory to analyze the crowdsourcing process. With mathematical ways, we first show that the requestor could reduce the cost of compensation via psychology methods, substituting partial monetary rewards with psychological payoffs. Furthermore, when workers are reciprocal and risk-neutral about expected earnings, we prove that the related incentive plan is requestor’s optimal choice. In particular, we find the workers’ psychological payoffs and requestor’s cost in equilibrium. Finally, we conduct a simulation to illustrate our findings intuitively. Therefore, our unique psychological crowdsourcing model provides a promising detour for incentive mechanism design in crowdsourcing scenarios.

In a blockchain-driven metaverse, user-generated content (UGC) is the core power for building the metaverse, so an easy-to-use UGC editor is imperative. Specifically, using artificial intelligence (AI) to simplify the UGC creation procedure is promising, e.g., generating images from sketches using generative adversarial networks (GANs). However, the simplicity of these UGC creation methods would lead to weak distinctions between the generated UGC, since the users' created drafts may be very similar. In this paper, we propose Crypto-dropout, a specially designed dropout used in the generative neural networks, which could cause pseudo-random disturbance based on the hash value of user information to generate unique results. With a pilot study, the experimental results demonstrate that the participants have different preferences for the generated images when setting Crypto-dropout in the different layers. Accordingly, we implement a practical profile pictures (PFPs) creation prototype. The proposed Crypto-dropout can provide a novel and general insight for creating unique UGC using generative neural networks.

The new computation technologies, such as big data analytics, modern machine learning technology, artificial intelligence (AI), blockchain, and security processing, have the great potential to be embedded into network to enable it to be intelligent and trustworthy. On the other hand, Information-Centric Networking (ICN), software-defined network (SDN), network function virtualization (NFV), network slicing, and data center network have emerged as the novel networking paradigms for fast and efficient delivering and retrieving data. Against this backdrop, there is a strong trend to move the computations from the cloud to not only the edges but also the resource-sufficient networking nodes, which triggers the convergence between the emerging networking concepts and the new computation technologies.

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle this issue in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on additional conditions of conference venues, like infrared camera or special microphone, which are not practical. Moreover, current video quality assessment works mainly focus on the quality loss after compression or encoding rather than the human-centered video content flaws. In this paper, we design and implement FLAD, a human-centered video content flaw detection system for meeting recordings, which could build a bridge between subjective sense and objective measures from a human-centered perspective. The experimental results illustrate the proposed algorithms could achieve the state-of-the-art video content flaw detection performance for meeting recordings.

In current blockchain systems, the transaction fee is often not enough to cover the storage cost, jeopardizing blockchain sustainability in the long run. Such a storage sustainability issue is partially due to miners’ heterogeneous storage costs and users’ low-intensity fee competition. Motivated by these two observations, we propose a Fee and Transaction Expiration Time (FTET) mechanism to alleviate this issue. Specifically, we model the blockchain operation as a three-stage game. In Stage I, the system designer proposes the storage sustainability mechanism. In Stage II, each user decides whether to propose transactions and the corresponding transaction fees. In Stage III, each miner decides which transactions to include in the block. Although the analysis of the heterogeneous miner interaction is technically challenging, we fully solve it in closed-form motivated by how miners select transactions in practice. The equilibrium analysis reveals that high-storage-cost miners admit transactions with fees above a time-increasing threshold. Under the optimal FTET mechanism, the blockchain system can achieve the storage sustainability without any social welfare loss, comparing with the maximum achievable social welfare without the storage sustainability constraint. Moreover, the optimal FTET mechanism achieves a higher social welfare than the fee mechanism in current practice by selectively rejecting some transactions suffering high delays. Finally, we implement a blockchain prototype to compare the performance of the optimal FTET mechanism with the mining round time adjustment (MRTA) mechanism. The optimal FTET mechanism achieves higher social welfare (94.5% on average) and better storage sustainability. We find that more pending transactions may lead to lower transaction fees.

Cloud gaming is a novel service provisioning paradigm, which hosts video games in the cloud and transmits interactive game streams to game players via the Internet. In such cloud gaming scenarios, the cloud is required to consume tremendous resources for video rendering and streaming, especially when the number of concurrent players reaches a certain level. On the other hand, different game players may have distinct requirements on Quality-of-Experience, such as high video quality, low delay, etc. Under this circumstance, how to satisfy players of different interests by efficiently leveraging cloud resources becomes a major challenge to existing cloud gaming services. In order to meet the overall requirements of players in a cost-effective manner, this article applies game theory to cloud gaming scenarios. It proposes a distributed algorithm to optimize virtual machine (VM) placement in mobile cloud gaming through resource competition. Further, by constructing a potential function, we prove that the resource competition game is a potential game, and the proposed algorithm scales well as the player population increases. We prove theoretically and verify experimentally that, with the proposed distributed VM placement algorithm, players can achieve a mutually satisfying state within a finite number of iterations.

Miners in a blockchain system are suffering from ever-increasing storage costs, which in general have not been properly compensated by the users’ transaction fees. This reduces the incentives for the miners’ participation and may jeopardize the blockchain security. To mitigate this blockchain insufficient fee issue, we propose a Fee and Waiting Tax (FWT) mechanism, which explicitly considers the two types of negative externalities in the system. Specifically, we model the interactions between the protocol designer, users, and miners as a three-stage Stackelberg game. By characterizing the equilibrium of the game, we find that miners neglecting the negative externality in transaction selection cause they are willing to accept insufficient-fee transactions. This leads to the insufficient storage fee issue in the existing protocol (i.e., deployed in Bitcoin and Ethereum). Moreover, our proposed optimal FWT mechanism can motivate users to pay sufficient transaction fees to cover the storage costs and achieve the unconstrained social optimum. Numerical results show that the optimal FWT mechanism guarantees sufficient transaction fees and achieves an average social welfare improvement of 51.43% or more over the existing protocol. Furthermore, the optimal FWT mechanism reduces the average waiting time of low-fee transactions and all transactions by 68.49% and 61.56%, respectively.

Deep Learning (DL) is an essential technology for modern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework’s training performance. Furthermore, we summarize eight privacy-sensitive information classes on which existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.

Computation partitioning is an important technique to improve the application performance by selectively offloading some computations from the mobile devices to the nearby edge cloud. In a dynamic environment in which the network bandwidth to the edge cloud may change frequently, the partitioning of the computation needs to be updated accordingly. The frequent updating of partitioning leads to high state migration cost between the mobile side and edge cloud. However, existing works don’t take the state migration overhead into consideration. Consequently, the partitioning decisions may cause significant network congestion and increase overall completion time tremendously. In this article, with considering the state migration overhead, we propose a set of novel algorithms to update the partitioning based on the changing network bandwidth. To the best of our knowledge, this is the first work on computation partitioning for stateful data stream applications in dynamic environments. The algorithms aim to alleviate the network congestion and minimize the make-span through selectively migrating state in dynamic edge cloud environments. Extensive simulations show our solution not only could selectively migrate state but also outperforms other classical benchmark algorithms in terms of make-span. The proposed model and algorithms will enrich the scheduling theory forstatefultasks, which has not been explored before.

Particle Swarm Optimization (PSO) has been widely used in various optimization tasks (e.g., neural architecture search and autonomous vehicle navigation), because it can solve non-convex optimization problems with simplicity and efficacy. However, the PSO algorithm is often time-consuming to use, especially for high-dimensional problems, which hinders its applicability in time-critical applications. In this paper, we propose novel techniques to accelerate the PSO algorithm with GPUs. To mitigate the efficiency bottleneck, we formally model the PSO optimization as a process of element-wise operations on matrices. Based on the modeling, we develop an efficient GPU algorithm to perform the element-wise operations in massively parallel using the tensor cores and shared memory. Moreover, we propose a series of novel techniques to improve our proposed algorithm, including (i) GPU resource-aware thread creation to prevent creating too many threads when the number of particles/dimensions is large; (ii) designing parallel techniques to initialize swarm particles with fast random number generation; (iii) exploiting GPU memory caching to manage swarm information instead of allocating new memory and (iv) developing a schema to support customized swarm evaluation functions. We conduct extensive experiments on four optimization applications to study the efficiency of our algorithm called “FastPSO”. Experimental results show that FastPSO consistently outperforms the existing CPU-based PSO libraries by two orders of magnitude, and transcends the existing GPU-based implementation by 5 to 7 times, while achieving better or competitive optimization results.

In recent years, the metaverse has attracted enormous attention from around the world with the development of related technologies. The expected metaverse should be a realistic society with more direct and physical interactions, while the concepts of race, gender, and even physical disability would be weakened, which would be highly beneficial for society. However, the development of metaverse is still in its infancy, with great potential for improvement. Regarding metaverse's huge potential, industry has already come forward with advance preparation, accompanied by feverish investment, but there are few discussions about metaverse in academia to scientifically guide its development. In this paper, we highlight the representative applications for social good. Then we propose a three-layer metaverse architecture from a macro perspective, containing infrastructure, interaction, and ecosystem. Moreover, we journey toward both a historical and novel metaverse with a detailed timeline and table of specific attributes. Lastly, we illustrate our implemented blockchain-driven metaverse prototype of a university campus and discuss the prototype design and insights.

Heraclitus's Forest is an interactive artwork that utilizes birch trees as a metaphor for the life stories recorded in an oral history database. We design a day/night cycle system to present the forest experience along the time elapse, multiple interaction modes to engage audiences' participation in history exploration, and evolving forest to arouse people's reflection on the feature of history, which is constantly being constructed but can never be returned to.

By training a machine learning algorithm across multiple decentralized edge nodes, federated learning (FL) ensures the privacy of the data generated by the massive Internet-of-Things (IoT) devices. To economically encourage the participation of heterogeneous edge nodes, a transparent and decentralized trading platform is needed to establish a fair market among distinct edge companies. In this article, we propose a hybrid blockchain-based resource trading system that combines the advantages of both public and consortium blockchains. We design and implement a smart contract to facilitate an automatic, autonomous, and auditable rational reverse auction mechanism among edge nodes. Moreover, we leverage the payment channel technique to enable credible, fast, low-cost, and high-frequency payment transactions between requesters and edge nodes. Simulation results show that the proposed reverse auction mechanism can achieve the properties, including budget feasibility, truthfulness, and computational efficiency.

Miners in a blockchain system are suffering from the ever-increasing storage costs, which in general have not been properly compensated by the users' transaction fees. In the long run, this may lead to less participation of miners and jeopardize the blockchain security. In this paper, we study the economics of blockchain storage and identify the incentive issues related to this storage cost problem. More specifically, we model the interactions among users (who generate transactions) and miners in two stages, where the users set the transaction fees in Stage 1, and the miners select which transactions to include in Stage 2. Through characterizing the Nash equilibrium of the two-stage game, we find that the transaction fees indeed cannot cover the storage costs under the current practice in general, due to the negative externality and the unfair delay-based pricing. We also identify that a longer block interval can alleviate the concern by raising the transactions fees at the expense of larger delay.

Videos have become the new preference comparing with images in recent years. However, during the recording of videos, the cameras are inevitably occluded by some objects or persons that pass through the cameras, which would highly increase the workload of video editors for searching out such occlusions. In this paper, for releasing the burden of video editors, a frame-level video occlusion detection method is proposed, which is a fundamental component of automatic video editing. The proposed method enhances the extraction of spatial-temporal information based on C3D yet only using around half amount of parameters, with an occlusion correction algorithm for correcting the prediction results. In addition, a novel loss function is proposed to better extract the characterization of occlusion and improve the detection performance. For performance evaluation, this paper builds a new large scale dataset, containing 1,000 video segments from seven different real-world scenarios, which could be available at: https://junhua-liao.github.io/Occlusion-Detection/. All occlusions in video segments are annotated frame by frame with bounding-boxes so that the dataset could be utilized in both frame-level occlusion detection and precise occlusion location. The experimental results illustrate that the proposed method could achieve good performance on video occlusion detection compared with the state-of-the-art approaches. To the best of our knowledge, this is the first study which focuses on occlusion detection for automatic video editing.

The blockchain technology provides a data authentication and permanent storage solution to the data volatility issue in peer-to-peer games. In this work, we present the Infinity Battle, a serverless turn-based strategy game supported by a novel Proof-of-Play consensus model. Comprising three major phases: matchmaking, gaming session and global synchronization, the proposed demo game generates a blockchain through distributed storage and processing.

Traditional centralized token exchange (CEX) has been suffering from hacking due to the centralized management of users’ tokens. In contrast, decentralized token exchange (DEX) maintains users’ assets by smart contracts in a decentralized manner, but introduces additional overhead in terms of gas fee and transaction confirmation latency. Hybrid decentralized token exchange (HEX) has been proposed to combine the benefits of CEX and DEX. However, existing HEX is criticized for two issues. First, trading transactions are time-consuming and expensive for frequent token traders. Second, excessive simultaneous transactions might cause the pending transaction congestion in the Ethereum network. In this paper, we propose a payment channel based HEX, which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate the pending transaction congestion. Besides, we propose the very first gas-price vs. transaction-confirmation-latency function to guide Ethereum transaction issuers to choose an optimal gas price that minimizes the overall cost. Extensive simulations are conducted to compare the cost in the proposed HEX with that in the conventional HEX. The results demonstrate the effectiveness of our proposed mechanism in terms of reducing gas fees and transaction confirmation latency for frequent traders as well as the pending transaction congestion in Ethereum.

With the support of the blockchain systems, the cryptocurrency has changed the world of virtual assets. Digital games, especially those with massive multi-player scenarios, will be significantly impacted by this novel technology. However, there are insufficient academic studies on this topic. In this work, we filled the blank by surveying the state-of-the-art blockchain games. We discuss the blockchain integration for games and then categorize existing blockchain games from the aspects of their genres and technical platforms. Moreover, by analyzing the industrial trend with a statistical approach, we envision the future of blockchain games from technological and commercial perspectives.

Blockchain has become novel solutions for many traditional issues in computer science and finance. Recently, with the release of Libra blockchain from Facebook, the decentralized finance concepts have another huge development. However, there are already many different blockchain systems existing now and the development of blockchain systems have become more and more complicated since each kind of blockchain development environment will consume time to be built. To make the programming on different blockchain systems more easily, we propose a novel cloud-based solution, namely ChainIDE, for the development of blockchain-based smart contracts on multiple kinds of blockchain systems. With chainIDE, cross-chain developing of smart contracts on different blockchain systems can be easily done without any time consumed by building the environment. Based on the operation statistics in this paper, we served more than 310,000 compilations in the past 30 days which makes us the most popular cloud-based cross-chain development platform in the world.

Rhythm Dungeon is a rhythm game which leverages the blockchain as a shared open database. During the gaming session, the player explores a roguelike dungeon by inputting specific sequences in time to music rhythm. By integrating smart contract to the game program, the enemies through the venture are generated from other games which share the identical blockchain. On the other hand, the player may upload their characters at the end of their journey, so that their own character may appear in other games and make an influence. Rhythm Dungeon is designed and implemented to show the potential of decentralized gaming experience, which utilizes the blockchain to provide asynchronous interactions among massive players.

Cloud gaming is a novel service provisioning paradigm, which hosts video games in the cloud and transmits interactive game streaming to players via the Internet. In this model, the cloud is required to consume tremendous resources for video rendering and streaming, especially when the number of concurrent players reaches a certain level. On the other hand, different players may have distinct requirements on Quality-of-Experience, such as high video quality, low delay, etc. Under this circumstance, to ensure an overall satisfaction for all players with finite cloud resources becomes a major challenge to existing cloud services. This paper employs game theory to the cloud gaming scenario and proposes a model to meet players' overall requirements with low cost. This game is proved to be a potential game with determining a devised potential function. Our experiment has shown that, with our algorithm, players can achieve a mutually satisfactory steady state, and the system will reduce the overhead up to 50% within the time complexity of O(Mlog M), where M is the number of physical servers.

As the game industry matures, processing complex game logics in a timely manner is no longer an insurmountable problem. However, current cloud-based mobile gaming solutions are limited by their relatively high requirements on Internet resources. Also, they typically do not consider the geographical locations of nearby mobile users and thus ignore the potential cooperation among them. Therefore, inspired by existing cloud computing techniques, we propose an ad hoc mobile-cloudlet-cloud based approach to implement cooperative gaming architecture. In this paper, two modules of the architecture are introduced: (1) progressive game resources download, by which mobile users can adaptively download gaming resources from cloud servers or nearby mobile users, (2) ad-hoc mobile based cooperative task allocation, by which gaming components can be executed dynamically on local devices, nearby devices, stationary cloudlet(s), or cloud servers. The mechanisms of both modules are formulated as optimization problems and algorithms are proposed to solve them. Simulations results based on real mobility traces show that our system's performance depends highly on the ad-hoc network environment. Our scheme has lower system resource usage while utilizing resources of nearby devices, compared to the cloud-based gaming architecture; and performs better with short on-device task duration compared to code-offloading based architecture.

Decomposed cloud gaming is a novel paradigm to deliver gaming as a service to mobile terminals. In such a system, video games are decomposed into software components that are cognitive to players' behavior and execution environments in both cloud and mobile devices. However, in order to balance the workload distribution between cloud and mobile terminals in a real system, there are still a number of questions to be answered, which requires empirical studies with detailed measurement and analysis. In this work, we identify these questions and answer them by measuring the execution and networking performance data from a tank game prototype in our test-bed. Based on our observations on the affecting factors on the system performance, we design and implement the cognitive capacity for the system to dynamically adapt its service to different terminals and networks.

With the popularization of mobile smart devices (e.g., smartphones, tablets), the number of mobile game players increases significantly. This makes mobile gaming industry rise and take up a large piece of global game market. Different from the traditional players playing games on their personal computers with wired local area network or Wi-Fi access, mobile game players nowadays play online games on their smart devices that communicate to cloud servers via wireless cellular networks. Therefore, the monetary cost for game content downloading and updating via cellular networks may increase the burden of gamers. Meanwhile, an unpredictable latency may be introduced by communication links of wireless cellular networks. This may negatively affect player's gaming experience. In fact, we can predict the next movement of the player in the game and preload the maps that have high probability to go. Meanwhile, if any of these maps have been downloaded and cached by other players nearby, with the Device-to-Device (D2D) communication networks, people may preload these maps freely. In this paper, we focus on the problem that how to select maps to preload from either game server or neighborhood with the consideration of limited storage space on smart devices. We first formulate an optimization problem. Since the formulated problem is NP-hard, we decouple the problem into two subproblems and solve them iteratively to get a suboptimal solution. Simulation results show that our proposed scheme can significantly increase the utility received by mobile players.

With the flourishing of smart mobile devices (e.g., smartphones, tablets), development of mobile cloud computing has received more and more attentions from both industry and academia. Compared with the traditional way of executing large- scale computational tasks on powerful desktop computers and the cloud, mobile cloud computing is featured by the ubiquitous availability, flexibility, and low-cost. However, this feature also brings challenges when we build the satisfactory mobile computing system. First, the computational power on a mobile device is not comparable with that on a personal computer such that many computation-intensive tasks cannot be independently handled by mobile devices. Second, offloading computational tasks to the cloud introduces additional monetary costs (e.g. wireless communication cost, computational service cost), which may be pricy for users. In this paper, we propose a novel connectivity- aware task scheduling paradigm to enable mobile device users to accomplish computation-intensive tasks cooperatively in the device-to-device (D2D) network by incorporating the "fog" - aggregate of computational powers in the ad-hoc. A supernode at the base station is responsible for scheduling cooperation tasks based on user mobility. To further enhance the quality of experience (QoE) for the users, we propose a lightweight heuristic algorithm to perform task scheduling to ensure low cooperative task execution time. Simulation results show that our cooperative paradigm efficiently reduces the average task execution time for mobile device users in the D2D network.

With the high popularizing rate of smart devices, mobile gaming is an emerging arena in game industry with the purpose of providing ubiquitous game services to mobile players. Different from the traditional player who play games on their personal computers with wired local area network or WiFi access, more and more mobile players nowadays prefer to play online games on their smart devices that communicate to cloud servers via wireless cellular networks. Therefore, in the mobile cloud gaming context, the monetary cost of downloading or updating map files in games via cellular networks is a new issue that may effect players' experience. On the other hand, an unpredictable latency may be introduced by the wireless links in the cellular network. In fact, a mobile player can preload the maps that he has high probability to go. Moreover, mobile players nearby can also form a device-to-device (D2D) communication network to share their cached maps. In this paper, we consider the problem that with the limited storage space available on each player's device, how to select the maps on either neighboring devices or cloud server to preload so that the utility of the player can be maximized. We first formula an optimization problem and then present our solution. Simulation results show that our proposed map sharing and preloading scheme can significantly increase the utility received by mobile players.

In this article, we have classified cloud gaming platforms into three types based on how games are integrated with platforms. We have also reviewed the history of cloud gaming services, and noted that it is a key moment for cloud gaming services to increase their penetration rates. Last, built upon our extensive research experience in cloud gaming, we share several of our visions into future cloud gaming technologies, business models, and social impacts, in the format of forecasts. While our forecasts may not be an exhausted list, we firmly believe this article will stimulate more discussions among cloud gaming researchers and practitioners, resulting in a sustainable cloud gaming ecosystem.

Game as a Service (GaaS) has rapidly emerged to the industry of cloud gaming. The power of GaaS lies on having one source of code with multiple users. Several systems were proposed to model GaaS. However, none has built a scalable and reliable model for such a service. The importance of having such a model lies on having an Internet-scale platform able to provide flexibility of different types of games genre and lower the barrier of end systems (i.e. mobile clients) while taking into consideration the probability of excessive loads and failures. In this paper, we implement a Distributed Cognitive Resource Allocation (DCRA) model to run mobile games on a large-scale distributed system. On the contrary of the existing centralized models, DCRA scales with the increase of mobile clients to handle high concurrent loads of clients' requests while providing a stable level of gaming experience. The results show that DCRA is able to scale well by providing almost fixed throughput and delay while increasing the clients requests load. Also, the system preserve its key features while simulating failures.

Cloud service selection and financial expense are two main concerns of users considering adoption of cloud computing services. In this paper, we propose a novel cloud federation that is cognitive to the dynamic prices. The cloud federation system first determines on which cloud services should the user applications be deployed. Then, when a cloud provider is charging too high for a VM, the proposed system automatically migrates user tasks to a cloud system that is charging at a lower rate. We discuss the architectural framework and platform design, provide a mathematic formulation and investigate a total service fee minimization approach with privacy constraints. Preliminary simulation results demonstrate the proposed system can lower the cost of cloud services by exploiting the advantages of different price policies provided by multiple cloud providers.

Recent studies have investigated a novel software paradigm that enables program decomposition and dynamic partitioning for cloud gaming system. Among the challenges for the proposed software architecture, to design and implement the decomposed software, which follows the platform's specification, is most critical in practice. In this work, we investigate a fundamental application in gaming scenario, the 3D skeletal game engine, which is often used to animate avatars. A graphics demo program is developed to demonstrate the feasibility of proposed system, while experiments have been conducted to show the efficiency of cognitive resource allocation. We also made contributions to seeking the appropriate design pattern in developing programs on our platform. In addition, we provide future visions on alternative implementations of some specific parts, which dedicates on improving the platform's compatibility.

Contrary to conventional gaming-on-demand services that stream gaming video from cloud to players' terminals, a decomposed cloud gaming platform supports flexible migrations of gaming components between the cloud server and the players' terminals. In this paper, we present the design and implementation of the proposed decomposed gaming system. The cognitive resource optimization of the system under distinct targets, including the minimization of cloud, network, and terminal resources and response delay, subject to quality of service (QoS) assurance, is formulated as a graph partitioning problem that is solved by exhaustive searches. Simulations and experimental results demonstrate the feasibility of cognitive resource management in a cloud gaming system to efficiently adapt to variations in the service environments, such as increasing the number of supported devices and reducing the network bandwidth consumption of user terminals, while satisfying different QoS requirements for gaming sessions. We also suggest two heuristic algorithms based on local greedy and genetic algorithm approaches, which can potentially provide scalable but suboptimal solutions in large-scale implementations.

Cloud gaming systems host the game in the cloud, while Gameplays and views are streamed to the players' terminals in the form of encoded video frames. To address the high-bandwidth issue of real-time gaming video transmission, we have proposed a cloudlet-assisted multiplayer cloud gaming system to encourage cooperative video sharing, which exploits the similarities of video frames among multiple players in the same crowd playing the same game via a secondary ad hoc network. In this paper, we provide a detailed modeling of the proposed system, including the correlation between video frames, mobility of terminal devices, and diversity of network quality of service for distinct players. With necessary mathematical formulations, we study the players' behaviors regarding the cooperative sharing patterns to optimize the system performance in terms of the quality of users' experience. Also, heuristic algorithms are proposed to reduce the computational complexity. Empirical study and trace-driven simulation results illustrate the impact of mobility on the system performance and show that the proposed solution is able to provide better quality of experience compared with the existing platform.

As the game industry matures, processing complex game logics in a timely manner is no longer an insurmountable problem. Many researchers are now trying to find ways to optimize the gaming system regarding the network usage, local resource utilization, and energy consumption. However, current cloud-based mobile gaming solutions are limited by their relatively high requirements on Internet resources. Also, they typically do not consider the geographical locations of nearby mobile users and thus ignore the potential cooperation among users. Therefore, inspired by existing cloud computing techniques and the concept of ad-hoc cloudlet computing, in this paper, we propose an ad-hoc cloudlet based gaming architecture. Two modules of the architecture are introduced: 1) progressive game resources download, by which mobile users can adaptively download gaming resources from cloud servers or nearby mobile users according to the gaming progress, and 2) ad-hoc cloudlet-based cooperative task allocation, by which gaming components can be executed dynamically over local devices, nearby devices, or cloud servers. We also formulate the mechanism for both modules as an optimization problem and propose several algorithms for both modules, which are later used for evaluation purposes. We carry out simulations based on real mobility traces, and the results show that our system's performance depends highly on the ad-hoc network environment (the more concurrent and balanced connections within the ad-hoc network, the lower the energy costs). Also, regardless of the network environment, our system has lower energy costs while utilizing resources of nearby devices, compared to the cloud-based gaming architecture.

In contrary to conventional gaming-on-demand solution, cognitive cloud gaming platform facilitates gaming component migration from the cloud server to the players' terminal, a novel flexible solution to provide Gaming as a Service. In this work, we model the component-based game and investigate the capacity of intelligent resource management for different optimization targets, including cloud resource minimization and throughput-oriented optimization. Experimental results show that, with the cognitive resource management, cloud system is adaptive to various service requirements, such as increasing the quantity of supported devices and reducing the network throughput of user terminals, while satisfying players' quality of experience.

Cloud gaming systems host the game in the cloud, and stream players' gaming videos to the terminals in the form of encode video frames. To address the high bandwidth issue of real-time gaming video transmission, a cloudlet-assisted multiplayer cloud gaming system was proposed to encourage cooperative video sharing via a secondary ad-hoc network, on the purpose of exploiting the similarities of video frames among multiple players in a same game. However, the video cooperative sharing among players also introduces fairness problems. In this paper, we complete the ad hoc cloudlet-assisted cloud gaming system by further considering the mobility of terminal devices and the diversity of network quality for distinct players. With mathematical formulation, we study the players' behavior in cooperative sharing patterns and propose a reputation-based multiplayer fairness scheme in terms of frame encoding. Experimental results illustrate the impact of mobility on the system performance and evaluate that the proposed solution provides better fairness gaming ecosystem compared to the existing platform.

Mobile cloud gaming provides a whole new service model for the video game industry to overcome the intrinsic restrictions of mobile devices and piracy issues. However, the diversity of end-user devices and frequent changes in network quality of service and cloud responses result in unstable Quality of Experience (QoE) for game players. A cognitive cloud gaming platform, which could overcome the above problem by learning about the game player's environment and adapting the cloud gaming service accordingly, does not currently exist. To fill this void, we design and implement a component-based gaming platform that supports click-and-play, intelligent resource allocation and partial offline execution, to provide cognitive capabilities across the cloud gaming system. Extensive experiments have been performed to show that intelligent partitioning leads to better system performance, such as overall latency.

In this paper, we propose a new model of community cloud (ComC) interaction that is based on the demand of tenants and propose an intrusion detection mechanism for the proposed model. ComC is a solution that is more secure than the public cloud, and less costly than the private cloud. We argue that our proposed model of the ComC will be more beneficial to consumers as well as providers. Our evaluation shows the efficiency of the proposed model from cost and operation point of views. In addition, our analysis shows that our proposed IDS can make the ComC a safe environment and can guarantee the security and privacy of the customers.

Mobile cloud-based video gaming (MCVG) is an emerging trend in moving the online entertainment industry into the cloud era. In MCVG, the game engines are hosted in the cloud, and the rendered gaming videos are transmitted over wireless networks to the mobile devices. In reverse, game players' interactions on screens are sent to the cloud server over the same networks. How to compress and transmit the real-time gaming video, so that during the gaming session, the expected server transmission rate over the bandwidth-limited wireless network is minimized while satisfying the quality of experience demanded by the players, is a great technical challenge that is addressed in this paper in a multi-player gaming context. We exploit the correlations between the gaming videos for distinct players in the same gaming scene to propose a cloud gaming system with cooperative video sharing, in which the cloud game server is able to efficiently encode and transmit multiple video streams to a group of players, while those players are able to decode their video in a cooperative manner by sharing contents via a secondary network such as ad hoc wireless local area network. Experimental results show that the expected server transmission rate can be significantly reduced compared to the conventional video encoding schemes for cloud games.

Light field is a large set of spatially correlated images of the same static scene captured using a 2D array of closely spaced cameras. Interactive light field streaming is the application where a client continuously requests successive light field images along a view trajectory of his choosing, and in response the server transmits appropriate data for the client to correctly reconstruct desired images. The technical challenge is how to encode captured light field images into a reasonably sized frame structure a priori (without knowing eventual clients' view trajectories), so that at stream time, expected server transmission rate can be minimized, while satisfying client's view-switch requests. In this paper, using I-frames, redundant P-frames and distributed source coding (DSC) frames as building blocks, we design coding structures to optimally trade off storage size of the frame structure with expected server transmission rate. The key novelty is to facilitate the use of “landmarks” in the structure-popular reference frames cached in the decoder buffer-so that the probability of having at least one useful predictor frame available in the buffer for disparity compensation is greatly increased. We first derive recursive equations to find the optimal caching strategy for a given coding structure. We then formulate the structure design problem as a Lagrangian minimization, and propose fast heuristics to find near-optimal solutions. Experimental results show that the expected server streaming rate can be reduced by up to 93.6% compared to an I-frame-only structure, at twice the storage required.

Light field is a large set of spatially correlated images of the same static scene captured using a 2D array of closely spaced cameras. Interactive light field streaming is the application where a client continuously requests successive light field images along a view trajectory of her choosing, and in response the server transmits appropriate data for the client to correctly reconstruct desired images. The technical challenge is how to encode captured light field images into a reasonably sized frame structure a priori (without knowing eventual clients' view trajectories), so that during streaming session, expected server transmission rate can be minimized, while satisfying client's view requests. In this paper, we design efficient frame structures, using I-frames, redundant P-frames and distributed source coding (DSC) frames as building blocks, to optimally trade off storage size of the frame structure with expected server transmission rate. The key novelty is to optimize structures in such a way that decoded images in caches of neighboring cooperative peers, connected together via a secondary network such as ad hoc WLAN for content sharing, can be reused to further decrease the server-to-client transmission rate. We formulate the structure design problem as a Lagrangian minimization, and propose fast heuristics to find near-optimal solutions. Experimental results show that the expected server streaming rate can be reduced by up to 83% compared to an I-frame-only structure, at less than twice the storage required.

Nowadays, online gaming is one of the emerging industry on the Internet. Massively Multiplayer Online Games (MMORPG) is one of the most important type of online games. Research on MMORPGs always pay attention to the network situations, such as flow imbalance, system optimization and traffic identification. The results help the game designers and network protocol engineers to improve user game experience. In this paper, we perform traffic analysis and modeling in three distinct game scenarios over two different wireless network connections in World of Warcraft(WoW), which is one of the most popular MMORPGs among the world. In addition, we contribute a random traffic generator base on ns-2 which could be a open development platform for the MMORPGs.