A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing

Zhao, Ruonan; Yang, Laurence T.; Liu, Debin; Deng, Xianjun; Mo, Yijun

doi:10.1109/tits.2022.3144301

Cited by 19 publications

(3 citation statements)

References 23 publications

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“…E cient models are also proposed in [25,26,27,28] which discuss use of secure blockchains for real-time scenarios under different applications. Similar models are discussed in [29,30,31,32,33,34,35], which propose use of crowdsensing with blockchains for e cient representation of data samples. These applications suggest that a very few ontology generation models utilize blockchain, and variance-based approaches for reducing redundancies, which can be explored for improving system performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Purbey

Khandelwal

Choudhary³

2023

SIViP

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Ontology generation is a process of relationship analysis, and representation for multiple data categories using automatic or semi-automatic approaches. This process requires a domain knowledgebase that describes given input data using entity-to-entity relations. A wide variety of approaches are proposed for this purpose, and each of them processes & converts input data using multiple relationship evaluation stages. These stages include data-preprocessing, correlation analysis, entity mapping, and ontology generation. A very few of these approaches are dataset independent, and most of them do not implement security measures during ontology generation, which limits their security, scalability & deployment capabilities during real-time implementation. Thus, in this text a blockchain based secure & e cient ontology generation model for multiple data genres using augmented strati cation (BOGMAS) is described. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. It uses a variance-based method (VBM) for reduction of redundant numerical features from the dataset, while textual features are converted to numerical values via standard word2vec model, and then processed using VBM. This model uses a combination of linear support vector machine (LSVM), and extra trees (ET) strati ers for variance estimation, which makes the model highly e cient, and reduces redundant features from the output ontology. These feature sets & their variances are given to a correlation engine for relationship estimation, and ontology generation.Each ontology record is secured using a mutable proof-of-work (PoW) based blockchain model, which assists in imbibing transparency, traceability, and distributed peer-to-peer processing capabilities. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending upon incoming data. Performance of the proposed BOGMAS model is evaluated in terms of precision & recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly e cient in terms of precision, recall & accuracy performance, but has incrementally higher computational complexity & delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things (IoT) ontology generation with high security levels.

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Section: Literature Reviewmentioning

confidence: 99%

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Purbey

Khandelwal

Choudhary³

2023

SIViP

View full text Add to dashboard Cite

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“…Ref. [16] proposed a tensorbased truthful incentive mechanism to motivate vehicles to participate in completing tasks, ensuring the safety of the entire process and maximizing social welfare. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, users make decisions based on relative changes or differences between the final outcome and a reference point, rather than absolute gains or losses. Therefore, to increase participation levels and data quality on crowdsensing platforms, it is necessary to increase cost investment [15][16][17][18][19]. It is important to note that individual users identify outcomes based on reference points, not the absolute final outcome level [20].…”

Section: Introductionmentioning

confidence: 99%

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Zhao,

Zeng,

Wang

et al. 2024

Electronics

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Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

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SpectrumChain: a disruptive dynamic spectrum-sharing framework for 6G

Wang

et al. 2023

Sci. China Inf. Sci.

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A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing

Cited by 19 publications

References 23 publications

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

SpectrumChain: a disruptive dynamic spectrum-sharing framework for 6G

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