DLchain: Blockchain with Deep Learning as Proof-of-Useful-Work

Chenli, Changhao; Li, Boyang; Jung, Taeho

doi:10.1007/978-3-030-59595-1_4

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…The result is that the models developed during the competition of miners have higher performance. Chenli, Changhao, et al 11 suggested DL-chain by exploiting the power of pow for Deep Learning by using the Raft algorithm to choose a publisher that broadcasts the training task for a model with a large dataset. The miners begin to carry out the task, and when the required accuracy is reached, such as 95%, the miner stores the trained model on the cloud and puts its link in the block header.…”

Section: Review Of Literaturementioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Preprint

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The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we propose a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work (PoUW) mechanism, in which miners use computing resources to optimize the travelling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when travelling between different locations. This strategy aims to take advantage of the miners’ efforts to minimize the distance travelled by salesmen by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used technique works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025.

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Section: Review Of Literaturementioning

confidence: 99%

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Provably Secure Blockchain Protocols from Distributed Proof-of-Deep-Learning

Larangeira

Tanaka

2023

Lecture Notes in Computer Science

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Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Sabry,

Shabana,

Handosa

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The blockchain network uses a Proof-of-Work (PoW) mechanism to validate transactions and keep the blockchain growth safe against tampering, but it is hugely energy-consuming with no benefit to the peer-to-peer network participants. In this paper, we proposed a blockchain network for distributing products to different locations based on the use of the Proof of Useful Work mechanism, in which miners use computing resources to optimize the traveling salesman problem (TSP) as an alternative to solving mathematical problems that represent the basis of the traditional PoW mechanism to get a new block. According to this proposed blockchain, it not only receives and securely stores the distribution locations but also improves the paths for salesmen when traveling between different locations during the transportation process. This strategy aims to take advantage of the miners’ efforts to minimize the traveled distance by applying the clustering technique and computing the shortest path by Guided Local Search (GLS) for each cluster at the same time. According to the tested results on TSP-LIB instances, the used strategy works efficiently with an average of 0.08 compared to the rest of the meta-heuristics, and the proposed architecture reduced total distances with an average of 0.025%. In addition, the block generation time in the blockchain decreased by 11.11% compared to other works.

show abstract

DLchain: Blockchain with Deep Learning as Proof-of-Useful-Work

Cited by 6 publications

References 13 publications

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

Provably Secure Blockchain Protocols from Distributed Proof-of-Deep-Learning

Adapting blockchain’s proof-of-work mechanism for multiple traveling salesmen problem optimization

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