Machine learning aided blockchain assisted framework for wireless networks

Amjad Saeed Khan, Xinruo Zhang, Sangarapillai Lambotharan, Gan Zheng, Basil AsSadhan, Lajos Hanzo

Research output: Contribution to journalArticlepeer-review

8 Downloads (Pure)

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.
Original languageEnglish
Article number9165554
Pages (from-to)262-268
Number of pages7
JournalIEEE Network
Volume34
Issue number5
Early online date12 Aug 2020
DOIs
Publication statusPublished - 1 Sep 2020

Bibliographical note

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Funder

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.

Keywords

  • Complexity theory
  • Learning (artificial intelligence)
  • Machine learning
  • Reliability
  • Wireless networks

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Machine learning aided blockchain assisted framework for wireless networks'. Together they form a unique fingerprint.

Cite this