Paper Info
Title
AI-Chain: Blockchain Energized Edge Intelligence for Beyond 5G Networks
Abstract
Beyond fifth generation (B5G) networks have recently emerged as the advancement of existing communication systems. With the unprecedented proliferation of artificial intelligence (Ai), more intelligent B5G networks are projected to fuel the continuous development of Ai applications and the efficiency of communication technologies. More recently, the outgrowths of B5G have brought billions of devices throwing zillions of bytes of data to network edges. Training such huge data volumes using a centralized data center proves to be a challenging task, due to prohibitively heavy bandwidth costs, poor time efficiency, and high privacy leakages. These challenges have fueled the revolutionary shift of intelligence applications from a centralized data center to ubiquitous edges, denoted as edge intelligence. However, there is still a long way ahead before edge intelligence is able to fully mature. Governing and sharing the use of learning results efficiently, reliably, and safely are hampered by the heterogeneity and non-confidence among edges. in this article, we propose blockchain energized edge intelligence for B5G networks, called the Ai-Chain, fusing deep learning and blockchain. The Ai-Chain is a distributed and immutable record of learning results that is able to construct a new basis of sharing among edges. Additionally, we consider a novel learning-based consensus protocol in the Ai-Chain, denoted as proof of learning (PoL). in response to the huge computing power waste, PoL treats the training process as a working puzzle, rather than the meaningless hashing in a proof of work protocol. On the other hand, PoL fully unlocks the potential of sharing more advanced intelligence among edges. in order to demonstrate the effectiveness of the Ai-Chain, we employ it to solve a joint resource allocation problem in B5G networks. Experimental results prove the superior performance of the Ai-Chain to the current popular scheme.
Year
DOI
Venue
2020
10.1109/MNET.021.1900617
IEEE Network
Keywords
DocType
Volume
intelligence applications,centralized data center,ubiquitous edges,blockchain energized edge intelligence,fifth generation networks,artificial intelligence,intelligent B5G networks,AI-chain,beyond 5G networks,deep learning,blockchain,learning-based consensus protocol
Journal
34
Issue
ISSN
Citations 
6
0890-8044
9
PageRank 
References 
Authors
0.44
0
6
Name
Order
Citations
PageRank
Chao Qiu1283.14
Haipeng Yao223327.25
Xiaofei Wang368658.88
Ni Zhang4101.81
Fei Yu55116335.58
Niyato Dusit69486547.06