In-Class Exercise for Shadow Mapping Algorithms

Wiens, Alex; Domik, Gitta

doi:10.1109/mcg.2015.58

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Those vehicles can choose to execute rendering tasks by collaborating with other vehicles within a limited communication distance. In the metaverse, vehicles that distribute large-scale matrix computation tasks (e.g., projection and shadow mapping in graphic rendering [23]) are called MSPs. The vehicles that execute the computing tasks of metaverse services are called workers.…”

Section: A System Modelmentioning

confidence: 99%

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

Jiang

Kang

Niyato

et al. 2023

IEEE Trans. Veh. Technol.

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The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in render-

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Section: A System Modelmentioning

confidence: 99%

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

Jiang

Kang

Niyato

et al. 2023

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Those devices can choose to execute rendering tasks by collaborating with other IoT devices. In the metaverse, IoT devices that distribute the large-scale matrix computation tasks (e.g., projection and shadow mapping in graphic rendering [20]) are called MSPs. The devices that execute the computation tasks of metaverse services are called workers.…”

Section: Preliminary a System Modelmentioning

confidence: 99%

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

Jiang¹,

Kang²,

Niyato³

et al. 2021

Preprint

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The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact with each other through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing intensive tasks of real-time graphic and audio rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile and Internet of Things (IoT) device. Alternatively, such devices can adopt the collaborative computing paradigm based on Coded Distributed Computing (CDC) to support metaverse services. Therefore, this paper introduces a reliable collaborative CDC framework for metaverse. In the framework, idle resources from mobile devices, acting as CDC workers, are aggregated to handle intensive computation tasks in the metaverse. A coalition can be formed among reliable workers based on a reputation metric which is maintained in a double blockchains database. The framework also considers an incentive to attract reliable workers to participate and process computation tasks of metaverse services. Moreover, the framework is designed with a hierarchical structure composed of coalition formation and Stackelberg games in the lower and upper levels to determine stable coalitions and rewards for reliable workers, respectively. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the random worker selection scheme, the proposed coalition formation and Stackelberg game can improve the utilities of both metaverse service providers and CDC workers.

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In-Class Exercises for Shadow Volume Algorithms

Wiens

Domik

2015

IEEE Comput. Grap. Appl.

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In-Class Exercise for Shadow Mapping Algorithms

Cited by 4 publications

References 2 publications

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

In-Class Exercises for Shadow Volume Algorithms

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