Trustworthy and Efficient Crowdsensed Data Trading on Sharding Blockchain

Wang, En; Cai, Jiatong; Yang, Yintang; Liu, Wenbin; Wang, Hengzhi; Yang, Bo; Wu, Jie

doi:10.1109/jsac.2022.3213331

Cited by 14 publications

(4 citation statements)

References 36 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

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“…Also, the Miners can verify the payment transactions to the Workers to avoid potentially unfair treatment [30], relieving Workers from the fear of receiving unfair rewards if the sensing data meet the requirements set. In these systems, usually only the Requestor makes a deposit to determine the rewards; however, recent proposals involve Workers also making a deposit to participate in tasks, aiming to prevent the system from many attacks such as denial-ofservice (DoS), Sybil, "free-riding" and "false-reporting" attacks [138] as well as from malicious users who quit their tasks [136]. The deposit is returned to Workers who are not selected for specific tasks.…”

Section: Blockchain-enabled Hcs-based Ng-iot Systemsmentioning

confidence: 99%

“…In [134], a blockchain-based crowdsensing trust management mechanism is proposed to evaluate the credibility of reports and the trust of participants, while preventing the tampering of trust values by malicious users. In [136], the authors propose a decentralized blockchain-based crowdsensing framework to achieve trustworthy data trading and fair rewards according to the data quality of the Workers. In [129], the authors present a reputation management scheme enhanced by blockchain to identify malicious users, preserve users' privacy and eliminate single point of failure in crowdsensing technology.…”

Section: Lately Blockchain Technology Has Been Integrated With Reputa...mentioning

confidence: 99%

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

Banti,

Louta,

Baziana

2023

IEEE Open J. Commun. Soc.

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Human-Centric Sensing (HCS), a novel approach in the evolution of the Next Generation Internet of Things (NG-IoT), exploits the ubiquity of diverse smart devices, including smartphones or wearable devices, in conjunction with their enhanced sensing capabilities to collect information, leveraging human intelligence for the common benefit of the crowd. The main feature of HCS is the involvement of mobile users in data collection, processing, analysis and sharing. Thus, the main challenge in HCS systems is to ensure users' participation and trustworthiness as well as data quality. The aim of this work is, as a first step, to identify and discuss the factors that affect data quality in HCS-based NG-IoT systems, as well as elaborate on their interrelation. Furthermore, potential solutions that could be adopted to ensure the highest possible degree of data quality are highlighted, in conjunction with critical aspects that should be considered, proposing a novel classification with three major categories: task assignment, reputation mechanisms and blockchain technology. Finally, a trust-aware task assignment model is proposed to effectively address the data quality challenge in HCS-based IoT systems, reflecting users' trustworthiness, willingness, experience, and ability to collect and share high-quality data contributions. The proposed trust-aware task assignment model exploits a reputation mechanism and is designed using blockchain and smart contract technologies to enable the decentralized provision of trustworthy services among entities and preserve users' privacy, harnessing the decentralization, transparency and immutability offered by blockchain. Trust-based task assignment offers an effective solution for trustworthy users' selection while ensuring high-quality contributions and users' privacy.

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“…Off-chain layer is designed for handling real-time shared transactions. [16] proposed the membership-based hierarchical sharding system. [17] designed a layered and fragmented consensus based on the collaboration between multiple partitions.…”

Section: Related Workmentioning

confidence: 99%

A credible and adjustable load resource trading system based on blockchain networks

Jiang¹,

Liu

Zhang³

et al. 2023

Front. Phys.

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In recent years, the residents’ demands for power supply is increasing. The load resource trading system responds to the demands through intelligent scheduling, which can effectively relieve the severe power load pressure. The load resource trading system is a type of nonlinear system because the trading price is adjustable with user’s credit, instead of being linear to the power trading volume. The adjustable load resource trading is faced with the challenges of large demands, strong user autonomy, and secure and tamper-proof transaction data. The blockchain technology has been widely concerned by industrial and academic domains due to its decentralization, strong encryption of account information, and traceability of transaction behaviour. In this paper, we propose a credible and adjustable load resource trading framework based on blockchain, which uses blockchain to achieve credible grid load resource trading. Firstly, we propose a two-layer blockchain architecture based on the alliance chain. The main chain maintains all the data of the system, and the station-area nodes constitute the alliance chain. We design a distributed trading processing mechanism based on hybrid consensus and sharding technologies, which promotes the speed of cross-station transaction consensus. Next, we propose a two-level bidding model, which determines the trading price of load resources based on the maximum benefit of users and the lowest cost of grid companies. The results of extensive experiments shows that our proposed framework can achieve the promising result.

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Trustworthy and Efficient Crowdsensed Data Trading on Sharding Blockchain

Cited by 14 publications

References 36 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

Data Quality in Human-Centric Sensing-Based Next-Generation IoT Systems: A Comprehensive Survey of Models, Issues, and Challenges

A credible and adjustable load resource trading system based on blockchain networks

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