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.

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.

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 work 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.

By facilitating multiple independent owners to jointly control a distributed network, blockchain can be used to solve the problem of device collaboration in complex networks (e.g. 5G, health care industries) through a distributed consensus mechanism. However, the state-of-the-art blockchain-based solutions cannot meet the demand of high transaction rate for those applications, due to the unavoidable data synchronization cost in decentralized systems. To address this issue, recent research splits blockchain nodes into multiple groups as parallel shardings to improve scalability at the cost of increased communication and storage per node. This paper proposes a fast and secure distributed blockchain protocol to reduce the traffic complexity while enhancing the transaction rates and the capability of fault-toleration. We introduce Proof-of-Behavior (PoB), a behavior-based incentive mechanism, for stimulating honest behavior and neutralizing malicious attacks. We design a blockchain protocol by integrating PoB with Raft, another classic consensus protocol with supervision, called Beh-Raft-Chain. Our approach replaces Practical Byzantine Fault Tolerance (PBFT) with Behavior-based Raft to lower the traffic complexity to O(n) and boost the capability of fault-toleration from ${n/4}$ to ${n/3}$ , where ${n}$ is the scale of blockchain. In our solution, we weigh all nodes based on their money and behaviors, and then set an adjustment parameter to increase the probability of candidate nodes being chosen beyond only a few nodes with the highest weight, in order to incentivize honest behavior in our mechanism. Our comparative experiments confirm Beh-Raft-Chain's theoretical low complexity and high fault-toleration properties.