Paper Info
Title
S-Podl: A Two-Stage Computational-Efficient Consensus Mechanism For Blockchain-Enabled Multi-Access Edge Computing
Abstract
More recently, there has been a growing interest in the blockchain technique for emerging multi-access edge computing (MEC) applications in relation to security and privacy, considering the benefits of using it in edge computing. In some blockchain-enabled MEC using the best-known consensus algorithm proof-of-work (PoW), the computational efforts increase dramatically with the number of transactions, since a large amount of computational tasks should be conducted by miners with the limited computing resources in edge devices. Hence, improving the computational performance as an important but challenging issue in the design of blockchain system for MEC applications, has attracted intensive attention within last years. To further improve the performance of PoW algorithm, we present a novel implementation mechanism in this paper. Here, motivated by proof-of-deep-learning (PoDL) method in which the deep learning algorithm is used to maintain blockchain, through the design of a two-stage model to achieving computational tasks in PoDL-based blockchain systems, a novel computational-efficient consensus mechanism, named separate-proof-of-deep-learning (S-PoDL), is accordingly proposed. Thus, an energy-efficient blockchain-enabled MEC could be developed with our proposed S-PoDL, which arranges miners to carry out a two-stage-based computation on the basis of accounting_queue technique, while presenting the achieved models as proofs in MEC network. The comparative experiments are conducted between S-PoDL and PoDL, and the experimental results verify the feasibility and efficiency of our consensus mechanism S-PoDL for some blockchain-enabled MEC applications in relation to PoW-based cryptocurrencies, while effectively reducing computing burden of edge devices. (C) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.phycom.2021.101338
PHYSICAL COMMUNICATION
Keywords
DocType
Volume
Blockchain, Consensus mechanism, Deep learning, Proof-of-work (PoW), Edge computing
Journal
46
ISSN
Citations 
PageRank 
1874-4907
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Xiong Luo19617.06
Pan Yang200.34
Wang Weiping333563.84
Yang Gao452850.36
Manman Yuan5133.55