IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Blockchain-Empowered Big Data Sharing for Internet of Things

Blockchain-Empowered Big Data Sharing for Internet of Things
View Sample PDF
Author(s): Ting Cai (Sun Yat-sen University, China), Yuxin Wu (Guangdong Baiyun University, China), Hui Lin (Sun Yat-sen University, China)and Yu Cai (Chongqing University of Posts and Telecom, China)
Copyright: 2023
Pages: 13
Source title: Research Anthology on Convergence of Blockchain, Internet of Things, and Security
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7132-6.ch017

Purchase

View Blockchain-Empowered Big Data Sharing for Internet of Things on the publisher's website for pricing and purchasing information.

Abstract

A recent study predicts that by 2025, up to 75 billion internet of things (IoT) devices will be connected to the internet, in which data sharing is increasingly needed by massive IoT applications as a major driver of the IoT market. However, how to meet the interests of all participants in complex multi-party interactive data sharing while providing secure data control and management is the main challenge in building an IoT data sharing ecosystem. In this article, the authors propose a blockchain-empowered data sharing architecture that supports secure data monitoring and manageability in complex multi-party interactions of IoT systems. First, to build trust among different data sharing parties, the authors apply blockchain technologies to IoT data sharing. In particular, on-chain/off-chain collaboration and sharding consensus process are used to improve the efficiency and scalability of the large-scale blockchain-empowered data sharing systems. In order to encourage IoT parties to actively participate in the construction of shared ecology, the authors use an iterative double auction mechanism in the proposed architecture to maximize the social welfare of all parties as a case-study. Finally, simulation results show that the proposed incentive algorithm can optimize data allocations for each party and maximize the social welfare while protecting the privacy of all parties.

Related Content

Chaymaâ Boutahiri, Ayoub Nouaiti, Aziz Bouazi, Abdallah Marhraoui Hsaini. © 2024. 14 pages.
Imane Cheikh, Khaoula Oulidi Omali, Mohammed Nabil Kabbaj, Mohammed Benbrahim. © 2024. 30 pages.
Tahiri Omar, Herrou Brahim, Sekkat Souhail, Khadiri Hassan. © 2024. 19 pages.
Sekkat Souhail, Ibtissam El Hassani, Anass Cherrafi. © 2024. 14 pages.
Meryeme Bououchma, Brahim Herrou. © 2024. 14 pages.
Touria Jdid, Idriss Chana, Aziz Bouazi, Mohammed Nabil Kabbaj, Mohammed Benbrahim. © 2024. 16 pages.
Houda Bentarki, Abdelkader Makhoute, Tőkési Karoly. © 2024. 10 pages.
Body Bottom