Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework

• URL: http://arxiv.org/abs/2302.11457v1
• Date: Wed, 22 Feb 2023 15:52:37 GMT
• Title: Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework
• Authors: Ismail Lotfi, Dusit Niyato, Sumei Sun, Dong In Kim, Xuemin (Sherman) Shen
• Abstract summary: We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
• Score: 68.8725783112254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating and rendering the digital copy of the physical world in the Metaverse. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. Nevertheless, mechanisms to hire sensing IoT devices to share their data with the VSP and then deliver the constructed digital twin to the Metaverse users are vulnerable to adverse selection problem. The adverse selection problem, which is caused by information asymmetry between the system entities, becomes harder to solve when the private information of the different entities are multi-dimensional. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem. To demonstrate the effectiveness of our algorithm, we conduct extensive simulations and measure several key performance metrics of the contract for the Metaverse. Our results show that our designed iterative contract is able to incentivize the participants to interact truthfully, which maximizes the profit of the VSP with minimal individual rationality (IR) and incentive compatibility (IC) violation rates. Furthermore, the proposed learning-based iterative contract framework has limited access to the private information of the participants, which is to the best of our knowledge, the first of its kind in addressing the problem of adverse selection in incentive mechanisms.

Related papers

• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• INTELLECT: Adapting Cyber Threat Detection to Heterogeneous Computing Environments [0.055923945039144884]
  This paper introduces INTELLECT, a novel solution that integrates feature selection, model pruning, and fine-tuning techniques into a cohesive pipeline for the dynamic adaptation of pre-trained ML models and configurations for IDSs. We demonstrate the advantages of incorporating knowledge distillation techniques while fine-tuning, enabling the ML model to consistently adapt to local network patterns while preserving historical knowledge.
  arXiv  Detail & Related papers  (2024-07-17T22:34:29Z)
• Federated PCA on Grassmann Manifold for IoT Anomaly Detection [23.340237814344384]
  Traditional machine learning-based intrusion detection systems (ML-IDS) possess limitations such as the requirement for labeled data. Recent unsupervised ML-IDS approaches such as AutoEncoders and Generative Adversarial Networks (GAN) offer alternative solutions. This paper proposes a novel federated unsupervised anomaly detection framework, FedPCA, that learns common representations of distributed non-i.i.d. datasets.
  arXiv  Detail & Related papers  (2024-07-10T07:23:21Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications [14.51946231794179]
  This paper introduces a novel formulation for the problem of multiple access to the wireless spectrum. It aims to optimize the utilization-fairness trade-off, using the $alpha$-fairness metric. A Semantic-Aware Multi-Agent Double and Dueling Deep Q-Learning (SAMA-D3QL) technique is proposed.
  arXiv  Detail & Related papers  (2024-01-12T00:32:38Z)
• Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
  We investigate the privacy-preserving joint edge association and power allocation problem. The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-01-26T10:09:23Z)
• Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design [72.27143788103245]
  SemCom and edge computing are disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. Deep learning (DL)-based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties. We present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse.
  arXiv  Detail & Related papers  (2022-12-13T10:29:41Z)
• SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems [15.796325306292134]
  Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. Various personalized approaches have been proposed, but such approaches fail to handle underlying shifts in data distribution. This paper presents a dynamically optimized personal deep learning scheme based on blockchain and federated learning.
  arXiv  Detail & Related papers  (2021-03-12T02:57:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.