Bibliographic Details
| Title: |
Trustful data trading through monetizing IoT data using BlockChain based review system. |
| Authors: |
Abubaker, Zain, Khan, Asad Ullah, Almogren, Ahmad, Abbas, Shahid, Javaid, Atia, Radwan, Ayman, Javaid, Nadeem |
| Source: |
Concurrency & Computation: Practice & Experience; 2/28/2022, Vol. 34 Issue 5, p1-19, 19p |
| Subject Terms: |
BLOCKCHAINS, ADVANCED Encryption Standard, INTERNET of things, DATA integrity, DATA quality |
| Abstract: |
In this article, Internet of Things (IoTs) devices are used for sensing the data through which the device owners earn revenue. Interested users can purchase data from IoT device owners, according to their demands. However, users are not confident about the quality of data they are purchasing. Moreover, the users do not rely on the device owner and are not willing to initiate data trading. Currently, data trading systems have many drawbacks, as they involve a third party, security and reputation mechanisms. Therefore, in this article, IoTs and BlockChain (BC) are integrated to monetize IoT's data and provide trustful data trading. A BC based review system to monetize IoT's data trading is developed through Ethereum smart contracts. The review system encourages the owners to provide authentic data and solves the issues regarding data integrity, fake reviews and conflicts between entities. Reviews and ratings are stored in the BC database for providing a guarantee about the data quality to users. To maintain data integrity, we use an advanced encryption standard (AES)‐256 encryption technique to encrypt data. Moreover, an arbitrator entity is responsible to resolve conflicts between data owner and users. The incentive is provided to the users and arbitrators to increase user participation and honesty. Simulations are performed for the validation of our system. We examine the proposed model using three parameters: gas consumption, mining time and encryption time. [ABSTRACT FROM AUTHOR] |
|
Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science