Dubbed “the successor to the mobile Internet,” the concept of the Metaverse has grown in popularity. While there exist lite versions of the Metaverse today, they are still far from realizing the full vision of an immersive, embodied, and interoperable Metaverse. Without addressing the issues of implementation from the communication and networking, as well as computation perspectives, the Metaverse is difficult to succeed the Internet, especially in terms of its accessibility to billions of users today. In this survey, we focus on the edge-enabled Metaverse to realize its ultimate vision. We first provide readers with a succinct tutorial of the Metaverse, an introduction to the architecture, as well as current developments. To enable ubiquitous, seamless, and embodied access to the Metaverse, we discuss the communication and networking challenges and survey cutting-edge solutions and concepts that leverage next-generation communication systems for users to immerse as and interact with embodied avatars in the Metaverse. Moreover, given the high computation costs required, e.g., to render 3D virtual worlds and run data-hungry artificial intelligence-driven avatars, we discuss the computation challenges and cloud-edge-end computation framework-driven solutions to realize the Metaverse on resource-constrained edge devices. Next, we explore how blockchain technologies can aid in the interoperable development of the Metaverse, not just in terms of empowering the economic circulation of virtual user-generated content but also to manage physical edge resources in a decentralized, transparent, and immutable manner. Finally, we discuss the future research directions towards realizing the true vision of the edge-enabled Metaverse.

A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges

Discover the very latest game-theoretic approaches for designing, modeling, and optimizing emerging wireless communication networks and systems with this unique text. Providing a unified and comprehensive treatment throughout, it explains basic concepts and theories for designing novel distributed wireless networking mechanisms, describes emerging game-theoretic tools from an engineering perspective, and provides an extensive overview of recent applications. A wealth of new tools is covered - including matching theory and games with bounded rationality - and tutorial chapters show how to use these tools to solve current and future wireless networking problems in areas such as 5G networks, network virtualization, software defined networks, cloud computing, the Internet of Things, context-aware networks, green communications, and security. This is an ideal resource for telecommunications engineers, and researchers in industry and academia who are working on the design of efficient, scalable, and robust communication protocols for future wireless networks, as well as graduate students in these fields.

Game Theory for Next Generation Wireless and Communication Networks: Modeling, Analysis, and Design

Dubbed “the successor to the mobile Internet,” the concept of the Metaverse has grown in popularity. While there exist lite versions of the Metaverse today, they are still far from realizing the full vision of an immersive, embodied, and interoperable Metaverse. Without addressing the issues of implementation from the communication and networking, as well as computation perspectives, the Metaverse is difficult to succeed the Internet, especially in terms of its accessibility to billions of users today. In this survey, we focus on the edge-enabled Metaverse to realize its ultimate vision. We first provide readers with a succinct tutorial of the Metaverse, an introduction to the architecture, as well as current developments. To enable ubiquitous, seamless, and embodied access to the Metaverse, we discuss the communication and networking challenges and survey cutting-edge solutions and concepts that leverage next-generation communication systems for users to immerse as and interact with embodied avatars in the Metaverse. Moreover, given the high computation costs required, e.g., to render 3D virtual worlds and run data-hungry artificial intelligence-driven avatars, we discuss the computation challenges and cloud-edge-end computation framework-driven solutions to realize the Metaverse on resource-constrained edge devices. Next, we explore how blockchain technologies can aid in the interoperable development of the Metaverse, not just in terms of empowering the economic circulation of virtual user-generated content but also to manage physical edge resources in a decentralized, transparent, and immutable manner. Finally, we discuss the future research directions towards realizing the true vision of the edge-enabled Metaverse. 

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IoT technology is a fundamental infrastructure for information transmission and integration in smart city implementations to achieve effective fine-grained city management, efficient operations, and improved life quality of citizens. Smartcity IoT systems usually involve high volume and a variety of information circulation. The information life-cycle also involves many parties, stakeholders, and entities such as individuals, businesses, and government agencies with their own objectives which needed to be incentivized properly. As such, recent studies have modeled smart-city IoT systems as a market, where information is treated as a commodity by market participants. In this work, we first present a general information-centric system architecture to analyze smart-city IoT systems. We then discuss features of market-oriented approaches in IoT, including market incentive, IoT service pattern, information freshness, and social impacts. System design challenges and related works are also reviewed. Finally, we optimize information trading in smart-city IoT systems, considering direct and indirect network externalities in a social domain.

Information Trading in Internet of Things for Smart Cities: A Market-Oriented Analysis

The Internet of Things (IoT) connects mobile and wireless devices, and enables the IoT service providers to deliver IoT services to the mobile users in various applications, e.g., transportation and communications. In this paper, the problem of IoT service delivery management is studied with the consideration of substitutability, complementarity, and externalities of delivering IoT services due to the diversity of different IoT components in mobile systems. The substitutable IoT services have similar functionalities to serve IoT users, and the IoT users can switch to buy service from any IoT service provider. The complementary IoT services have different functionalities to serve IoT users, and the IoT users may request a bundle of IoT services from multiple IoT service providers as their IoT services can be integrated. Externalities represent the situation in which IoT users in the same system can affect the utilities of each other due to the connections and interference among the IoT users, which leads to the presence of network effect and congestion effect. To analyze the impact of these factors on the performance of IoT systems, a multi-leader multi-follower Stackelberg game model is introduced. Therein, the IoT service providers and IoT users make their strategic decisions in terms of pricing and service requests, respectively, toward their individual objectives in a distributed manner. A closed-form equilibrium solution is derived analytically through backward induction.

A Game-Theoretic Analysis for Complementary and Substitutable IoT Services Delivery With Externalities

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners’ participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.

Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning

Internet of Things (IoT) technology enables various physical devices to collect, process and exchange information. Market oriented models become important for IoT systems to efficiently utilize information, as IoT network nodes operate in a highly distributed and autonomous manner. In this work, we propose a three-player game theoretic market model for IoT information trading, considering direct and indirect externalities among market participants. In the model, an IoT service provider collects and processes IoT information, and then delivers the processed information as IoT services to IoT users. Then, an IoT content vendor senses and generates raw information for the IoT service provider to collect, and receives rewards from the provider. Finally, an IoT user pays a fixed service fee to the IoT service provider to access the IoT services. To jointly derive the optimal market decisions of the three participants in the model, we employ a Stackelberg game approach. The equilibria are obtained as the closed form solutions of the game, with which the existence and uniqueness properties are proved. The analytical results show that the IoT service provider operates as an intermediary agent between the IoT content vendor and users, reducing the information trading complexity of both user and vendor sides.

Joint optimization of information trading in Internet of Things (IoT) market with externalities

In cloud computing, cloud services can be allocated to users upon requests in an on-demand basis. Heterogeneous cloud service providers may join the cloud systems to serve various types of users. Cloud services can be complementary or substitutable. For the complementary services, users may request for a bundle of the services, e.g., CPU and storage, to gain higher benefit from requesting them alone. The substitutable services have similar functionalities to serve users, e.g., different cloud database services, obtaining one of them can replace another one. Furthermore, the users of the cloud systems also influence each other because of externalities, particularly, network effect and congestion effect. From the perspective of each user, the existence of other users may introduce positive or negative impacts on the user utility, in the case of network and congestion effects, respectively. In this work, the participants in the cloud systems are treated as social enabled rational individuals. We model the complementarity, substitutability and externalities in cloud services by employing a multiple-leader multiple- follower Stackelberg game approach, including a two- stage service transaction process where service providers and users make their transaction decisions in a distributed manner. The analytical expressions of equilibria, service pricing strategies, and service allocations are derived with numerical results. We also find in the numerical results that both collusive and competitive service pricing schemes may lead to the optimized provider and user performances simultaneously.

A Game-Theoretic Analysis of Complementarity, Substitutability and Externalities in Cloud Services

For future Internet of Things (IoT) systems, data-driven and dynamic spectrum-sharing schemes can significantly improve the spectrum utilization and efficiency. However, conventional centralized architecture of such dynamic IoT spectrum-sharing systems is often considered to be nontransparent, costly, and vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-based dynamic spectrum-sharing scheme has been proposed and investigated in this work, which aims at enhancing the system by providing desirable features, such as decentralization, transparency, immutability, and auditability. By considering the privacy and transaction dynamics issues when blockchain is integrated into spectrum-sharing systems, a privacy-preserving double auction mechanism based on differential privacy is developed for incentivizing spectrum sharing, where the time-varying valuations of the spectrum resources are also taken into consideration. In the proposed auction, a winner determination problem (WDP) is formulated to decide the winning bidders and spectrum allocation. A deep reinforcement learning (DRL)-based method is then proposed for efficiently solving the WDP. The proposed auction mechanism can be integrated with smart contracts on blockchain platforms. Furthermore, the computation of the DRL-based method for solving the WDP is designed as part of the consensus mechanism in the blockchain. Theoretical analysis show that the proposed privacy-aware double auction mechanism satisfies the properties of differential privacy, individual rationality, and truthfulness. Finally, simulation results are provided to validate the performance of the spectrum-sharing approach.

Privacy-Aware Double Auction With Time-Dependent Valuation for Blockchain-Based Dynamic Spectrum Sharing in IoT Systems

In mobile and edge intelligence systems, federated learning (FL) enables local data training and learning model sharing without obtaining actual data from mobile and edge users, which are data owners. Data training processes are performed at the user side with only trained gradients passed to an aggregator, i.e., learning server. The learning server continually trains and updates corresponding learning models by collecting gradients. The updated learning models are delivered back to the mobile and edge users for improved data training results. Despite the advantages of federated learning in preserving privacy, the local data training process will consume an adequate amount of energy from the perspective of mobile and edge users. In mobile and edge intelligence systems, mobile users may not always prefer to apply federated learning to reduce energy consumption. There is a trade-off between applying federated learning to reserve data privacy and updating actual data for learning servers to train. In this work, a Markov decision process (MDP) based system model is proposed for mobile and edge users to make federated learning decisions to optimize long-term performance in terms of utility function consists of data training reward and data processing delay. A deep reinforcement learning approach is proposed to solve the MDP problem in highly dynamic systems with a large state space.

Jiangming Jin

Papers

Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning

Joint optimization of information trading in Internet of Things (IoT) market with externalities

A Game-Theoretic Analysis of Complementarity, Substitutability and Externalities in Cloud Services

Privacy-Aware Double Auction With Time-Dependent Valuation for Blockchain-Based Dynamic Spectrum Sharing in IoT Systems

Privacy is not Free: Energy-Aware Federated Learning for Mobile and Edge Intelligence