Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MNET.011.1900643 | IEEE Network |
Keywords | DocType | Volume |
Wireless networks,Learning (artificial intelligence),Machine learning,Complexity theory,Reliability | Journal | 34 |
Issue | ISSN | Citations |
5 | 0890-8044 | 0 |
PageRank | References | Authors |
0.34 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amjad Saeed Khan | 1 | 0 | 0.34 |
| Xinruo Zhang | 2 | 18 | 6.28 |
| Sangarapillai Lambotharan | 3 | 687 | 69.79 |
| Gan Zheng | 4 | 2199 | 115.78 |
| Basil AsSadhan | 5 | 0 | 0.34 |
| Lajos Hanzo | 6 | 10889 | 849.85 |