Toward Secure Data Sharing for the IoT Devices With Limited Resources: A Smart Contract-Based Quality-Driven Incentive Mechanism

Zhang, Chi; Shen, Tao; Bai, Fenhua

doi:10.1109/jiot.2022.3142786

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…Zhang et al [36] presented a smart contract-based qualitydriven incentive system for secure data exchange across IoT devices. Blockchain verifies data integrity, while smart contracts apply the incentive system.…”

Section: B Incentive Distribution Mechanismsmentioning

confidence: 99%

Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing

Sohail,

Anjum,

Saeed

et al. 2024

IEEE Access

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Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud computing and fine-grained access control to overcome these challenges. However, traditional centralized computing systems involve single points of failure. To mitigate these challenges, we have proposed a secure and incentive-based data-sharing framework for IIoT systems using blockchain technology. We leverage blockchain due to its ability to provide secure and tamper-resistant data storage and sharing as participants store their data on a distributed ledger (DL), preventing unauthorized access. A security protocol is designed that utilizes the properties of elliptic curve cryptography (ECC). Moreover, Shapley value is employed to calculate revenue and distribute it fairly. To perform the formal security evaluation, we have conducted extensive simulations using the Automated Validation of Internet Security Protocols and Applications (AVISPA) and Scyther protocol simulation tools, which demonstrated that our protocol is robust against various adversarial attacks. The experimental results show that the proposed incentive distribution framework demonstrated fairness in the distribution of revenue among participants.

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Section: B Incentive Distribution Mechanismsmentioning

confidence: 99%

Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing

Sohail,

Anjum,

Saeed

et al. 2024

IEEE Access

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“…In [21], a framework for data trading over the internet of artificially intelligent things was established, where the security and pricing issues were investigated, and a data trading strategy was proposed to jointly maintain the model performance and the budget consumption. Zhang et al [22] proposed a smart contract-based qualitydriven incentive mechanism for secure data sharing among IoT devices with limited resources, where a two-layer Stackelberg game of nested coalitional scheme was designed to realize the maximum overall social welfare. By designing a two-phase sampling-based algorithm and two types of pricing strategies for different data trading circumstances, Cai et al [23] studied the range counting trading for IoT data with the objective of preserving the privacy of data contributors.…”

Section: Related Workmentioning

confidence: 99%

“…By applying the allocation rules (13a)-(13b) as well as the payment rule (18) and the reimbursement rule (22), the RSUs and EVs can calculate their optimal bids and the broker can achieve the market equilibrium for social welfare maximization in one round, if they know the complete market Algorithm 1 Iterative Double-Sided Auction Mechanism Initialization:…”

Section: Implementation Of Ida-based Data-energy Transaction Ecosystemmentioning

confidence: 99%

IDADET: Iterative Double-Sided Auction-Based Data-Energy Transaction Ecosystem in Internet of Vehicles

Liu

et al. 2023

IEEE Internet Things J.

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In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the selfinterested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker's coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing "currency", such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical

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“…Contract theory is a powerful tool for incentive mechanism design under information asymmetry [25], which has been applied in wireless communication areas [26], [27]. Some works have studied contract-based incentive mechanism design for incentivizing data sharing [28]- [30], but they ignore the data freshness. Therefore, we focus on designing a contract-based incentive mechanism with optimal data freshness.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

Kang

Nie

et al. 2022

2022 IEEE International Conference on Blockchain (Blockchain)

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Given the revolutionary role of metaverses, healthcare metaverses are emerging as a transformative force, creating intelligent healthcare systems that offer immersive and personalized services. The healthcare metaverses allow for effective decision-making and data analytics for users. However, there still exist critical challenges in building healthcare metaverses, such as the risk of sensitive data leakage and issues with sensing data security and freshness, as well as concerns around incentivizing data sharing. In this paper, we first design a usercentric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. To further improve the privacy protection of healthcare metaverses, a crosschain empowered FL framework is utilized to enhance sensing data security. This framework utilizes a hierarchical crosschain architecture with a main chain and multiple subchains to perform decentralized, privacy-preserving, and secure data training in both virtual and physical spaces. Moreover, we utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing in a usercentric manner. This model exploits PT to better capture the subjective utility of the service provider. Finally, our numerical results demonstrate the effectiveness of the proposed schemes for healthcare metaverses.

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Toward Secure Data Sharing for the IoT Devices With Limited Resources: A Smart Contract-Based Quality-Driven Incentive Mechanism

Cited by 14 publications

References 47 publications

Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing

Optimizing Industrial IoT Data Security Through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing

IDADET: Iterative Double-Sided Auction-Based Data-Energy Transaction Ecosystem in Internet of Vehicles

Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

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