Distributed Incentives for Intelligent Offloading and Resource Allocation in Digital Twin Driven Smart Industry

Peng, Kai; Huang, Hualong; Bilal, Muhammad; Xu, Xiaolong

doi:10.1109/tii.2022.3184070

Cited by 31 publications

(9 citation statements)

References 38 publications

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“…3) Game-Theoretical IoDT Governance. Apart from the solutions built on blockchain and AI technologies, gametheoretical approaches have been widely investigated in the literature for attack defense [114], service congestion governance [115], and long-term incentive design [116]. Xu et al [114] identify a novel stealthy estimation threat, where smart attackers can learn defense strategies to alter the digital twins' state estimation without being detected.…”

Section: F Provenance Governance and Accountability In Iodtmentioning

confidence: 99%

“…In IoDT, the intensive and dynamic virtual twin service demands can easily result in service congestion, which eventually deteriorates the QoS and stability of digital twin services. Peng et al [115] study a digital twin-empowered two-stage offloading mechanism in DTENs for mitigating latency-critical tasks from end devices to edge servers. In the first stage, credit-based incentives are assigned to optimize digital twins' resource allocation strategies; while in the second stage, a Stackelberg game is designed to derive the optimal offloading and privacy investment policies for digital twins.…”

Section: F Provenance Governance and Accountability In Iodtmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects

Wang

Zhou

Guo

et al. 2023

IEEE Internet Things J.

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By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as decentralized structure, information-centric routing and semantic communications entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

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Section: F Provenance Governance and Accountability In Iodtmentioning

confidence: 99%

Section: F Provenance Governance and Accountability In Iodtmentioning

confidence: 99%

A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects

Wang

Zhou

Guo

et al. 2023

IEEE Internet Things J.

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“…Consequently, it is worthwhile to investigate ways to increase the resource utilization of edge servers. Let mark(a i , v) denotes whether task a i is offloaded to the edge server v, TO v indicates the total resources of v. The resource utilization R total can be calculated by Equations ( 14) and (15).…”

Section: 32mentioning

confidence: 99%

“…Afterwards, the value of OC (𝜃,v) and A ′ 𝜃 are updated for the next step (Lines 9 and 10). At last, we obtain MMF (𝜃,v) by traversing OC (𝜃,v) (Lines [15][16][17][18][19]. The MMF (𝜃,v) can be calculated by Equations ( 19)and ( 20)…”

Section: The Resource Fairness Evaluation Based On Weighted Mmfmentioning

confidence: 99%

A fairness‐aware task offloading method in edge‐enabled IIoT with multi‐constraints using AGE‐MOEA and weighted MMF

Peng

Ling

Zhao

et al. 2023

Concurrency and Computation

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SummaryBy providing distributed and ultra‐low‐latency communication between industrial devices and resource components, the Industrial Internet of Things (IIoT) is at the forefront of a new trend. Such a distributed paradigm is viewed as a collection of autonomous computing resources utilized by multiple heterogeneous devices to achieve higher‐quality interconnection and data exchange. However, stringent requirements of exceptional service and fairness guarantees pose many formidable challenges. To this end, this study investigates the aforementioned concerns in an integrated manner and further proposes a fairness‐aware task offloading method, called FOIMAM. Specifically, the ‐norm is introduced to accommodate the Pareto plane under the non‐Euclidean geometry framework while the evaluation and elimination of low‐quality solutions are completed based on survival scores. Particularly, the fairness requirements are formulated as a multi‐constraint problem and resolved using weighted max‐min fairness. Eventually, numerical results indicate that the proposed method brings substantial improvement in both service efficiency and fairness guarantees.

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“…The expanding range of RPL applications in industrial sectors clearly shows its efficiency. However, these circumstances make RPL networks vulnerable to various potential risks [ 17 ]. Such challenging circumstances demand multiple security solutions in order to ensure durable and satisfactory communication in industrial networks [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

Attique

Wang

2022

Sensors

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The Internet of Things (IoT) is a prominent and advanced network communication technology that has familiarized the world with smart industries. The conveniently acquirable nature of IoT makes it susceptible to a diversified range of potential security threats. The literature has brought forth a plethora of solutions for ensuring secure communications in IoT-based smart industries. However, resource-constrained sectors still demand significant attention. We have proposed a fog-assisted deep learning (DL)-empowered intrusion detection system (IDS) for resource-constrained smart industries. The proposed Cuda–deep neural network gated recurrent unit (Cu-DNNGRU) framework was trained on the N-BaIoT dataset and was evaluated on judicious performance metrics, including accuracy, precision, recall, and F1-score. Additionally, the Cu-DNNGRU was empirically investigated alongside state-of-the-art classifiers, including Cu-LSTMDNN, Cu-BLSTM, and Cu-GRU. An extensive performance comparison was also undertaken among the proposed IDS and some outstanding solutions from the literature. The simulation results showed ample strength with respect to the validation of the proposed framework. The proposed Cu-DNNGRU achieved 99.39% accuracy, 99.09% precision, 98.89% recall, and an F1-score of 99.21%. In the performance comparison, the values were substantially higher than those of the benchmarked schemes, as well as competitive security solutions from the literature.

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Distributed Incentives for Intelligent Offloading and Resource Allocation in Digital Twin Driven Smart Industry

Cited by 31 publications

References 38 publications

A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects

A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects

A fairness‐aware task offloading method in edge‐enabled IIoT with multi‐constraints using AGE‐MOEA and weighted MMF

Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

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