Machine learning aided blockchain assisted framework for wireless networks
Machine learning aided blockchain assisted framework for wireless networks
Inspired by its success in financial sectors, the blockchain technique is emerging as an enabling technology for secure distributed control and management of wireless networks. In order to fully benefit from this distributed ledger technology, its limitations, cost, complexity and empowerment also have to be critically appraised. Depending on the specific context of the problem to be solved, these limitations have been handled to some extent through a clear dichotomy in the blockchain architectures, namely by conceiving both permissioned and permissionless blockchains. Permissionless blockchain requires massive computing power to achieve consensus, while its permissioned counterpart is energy efficient but would require trusted participants. To combine these benefits by gaining trust at a high energy efficiency, a novel mechanism is proposed for automatically learning the trust level of users in a public blockchain network and granting them access to a private blockchain network. In this context, machine learning is a very powerful tool capable of automatically learning the trust level. We have proposed reinforcement learning for bridging the dichotomy of blockchains in terms of striking a trust vs complexity trade-off in an unknown environment. Benefits and limitations of various forms of blockchain techniques are analyzed, followed by their reinforcement-aided evolution. We demonstrate that the proposed reinforcement learning aided blockchain is capable of supporting high-integrity autonomous operation and decision making in wireless networks. The win-win amalgamation of these techniques has been demonstrated for striking a compelling balance between the benefits of permissioned and permissionless blockchain networks through the case-study of the proposed blockchain based unmanned aerial vehicle aided wireless networks.
262 - 268
Saeed Khan, Amjad
4535edee-72e8-4deb-972c-a392ec40f6ac
Zhang, Xinruo
48fbbacd-0518-492d-b9c0-5a40f8a16c03
Lambotharan, Sangarapillai
9839317e-0bf4-4d7c-8722-87d3ec9086de
Zheng, Gan
b11bd5ef-6c5e-44f0-a7e0-a679d94cae80
AsSadhan, Basil
9f8f1943-a9a3-4881-9159-0f92101cef17
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 September 2020
Saeed Khan, Amjad
4535edee-72e8-4deb-972c-a392ec40f6ac
Zhang, Xinruo
48fbbacd-0518-492d-b9c0-5a40f8a16c03
Lambotharan, Sangarapillai
9839317e-0bf4-4d7c-8722-87d3ec9086de
Zheng, Gan
b11bd5ef-6c5e-44f0-a7e0-a679d94cae80
AsSadhan, Basil
9f8f1943-a9a3-4881-9159-0f92101cef17
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Saeed Khan, Amjad, Zhang, Xinruo, Lambotharan, Sangarapillai, Zheng, Gan, AsSadhan, Basil and Hanzo, Lajos
(2020)
Machine learning aided blockchain assisted framework for wireless networks.
IEEE Network, 34 (5), , [9165554].
(doi:10.1109/MNET.011.1900643).
Abstract
Inspired by its success in financial sectors, the blockchain technique is emerging as an enabling technology for secure distributed control and management of wireless networks. In order to fully benefit from this distributed ledger technology, its limitations, cost, complexity and empowerment also have to be critically appraised. Depending on the specific context of the problem to be solved, these limitations have been handled to some extent through a clear dichotomy in the blockchain architectures, namely by conceiving both permissioned and permissionless blockchains. Permissionless blockchain requires massive computing power to achieve consensus, while its permissioned counterpart is energy efficient but would require trusted participants. To combine these benefits by gaining trust at a high energy efficiency, a novel mechanism is proposed for automatically learning the trust level of users in a public blockchain network and granting them access to a private blockchain network. In this context, machine learning is a very powerful tool capable of automatically learning the trust level. We have proposed reinforcement learning for bridging the dichotomy of blockchains in terms of striking a trust vs complexity trade-off in an unknown environment. Benefits and limitations of various forms of blockchain techniques are analyzed, followed by their reinforcement-aided evolution. We demonstrate that the proposed reinforcement learning aided blockchain is capable of supporting high-integrity autonomous operation and decision making in wireless networks. The win-win amalgamation of these techniques has been demonstrated for striking a compelling balance between the benefits of permissioned and permissionless blockchain networks through the case-study of the proposed blockchain based unmanned aerial vehicle aided wireless networks.
Text
IEEE_magazine_double_column
- Accepted Manuscript
More information
Accepted/In Press date: 1 July 2020
e-pub ahead of print date: 12 August 2020
Published date: 1 September 2020
Additional Information:
Funding Information:
AcknowLedgments This work was supported in part by the Engineering and Physical Sciences Research Council under Grants EP/R006385/1, EP/N007840/1 and EP/ P003990/1 (COALESCE); in part by the Royal Society’s Global Challenges Research Fund Grant; in part by the European Research Council’s Advanced Fellow Grant QuantCom; and in part by the International Scientific Partnership Program (ISPP) at King Saud University under Grant ISPP 134.
Publisher Copyright:
© 1986-2012 IEEE.
Identifiers
Local EPrints ID: 443008
URI: http://eprints.soton.ac.uk/id/eprint/443008
ISSN: 0890-8044
PURE UUID: ab94a250-092e-4091-9f7f-a517c9a204a9
Catalogue record
Date deposited: 05 Aug 2020 16:35
Last modified: 18 Mar 2024 05:13
Export record
Altmetrics
Contributors
Author:
Amjad Saeed Khan
Author:
Xinruo Zhang
Author:
Sangarapillai Lambotharan
Author:
Gan Zheng
Author:
Basil AsSadhan
Author:
Lajos Hanzo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics