Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this article, we present Web3Agent , an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.

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.

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.

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.

With the continued growth of the online video platform market, finding an effective business model has become one of the main issues for video providers. This paper proposes an advertising incentive model to maximize video providers' revenue and investigate users' motivation to obtain premium services based on value differentiation and snob effects. Considering the impact of ad loss on advertisers, we establish the existence of an equilibrium in our proposed hybrid revenue model using a two-stage Stackelberg model. We find that more aggressive advertising incentives lead to an increase in the proportion of free users, and the threshold for free users to become paid users is closely related to the disutility of ads and the strength of advertising incentives. Experimental results show that video providers can use personalized recommendations to enhance user utility. When the advertising incentives are large enough, users should choose the free mode. This provides theoretical support for optimizing the revenue model of online video platforms.

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.

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 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.

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.