Operations Research in the Blockchain Technology

Wang, Xu; Wu, Lingyun

doi:10.1007/s40305-021-00348-7

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Shi et al (2022) insist on the use of learning a near-optimal policy using history data by the agent with offline or batch reinforcement learning. Wang and Wu (2022) discuss the perspective of research directions of blockchain in the domain of operations research. Miner's computational power allocation and edge computational power allocation are the open research areas in the blockchain that require combinatorial optimization and the queueing theory.…”

Section: Related Workmentioning

confidence: 99%

Q‐Learning model for selfish miners with optional stopping theorem for honest miners

Jeyasheela Rakkini,

Geetha

2023

Int Trans Operational Res

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Bitcoin, the most popular cryptocurrency used in the blockchain, has miners join mining pools and get rewarded for the proportion of hash rate they have contributed to the mining pool. This work proposes the prediction of the relativegain of the miners by machine learning and deep learning models, the miners' selection of higher relativegain by the Q‐learning model, and an optional stopping theorem for honest miners in the presence of selfish mining attacks. Relativegain is the ratio of the number of blocks mined by selfish miners in the main canonical chain to the blocks of other miners. A Q‐learning agent with ε‐greedy value iteration, which seeks to increase the relativegain for the selfish miners, that takes into account all the other quintessential parameters, including the hash rate of miners, time warp, the height of the blockchain, the number of times the blockchain was reorganized, and the adjustment of the timestamp of the block, is implemented. Next, the ruin of the honest miners and the optional stopping theorem are analyzed so that the honest miners can quit the mining process before their complete ruin. We obtain a low mean square error of 0.0032 and a mean absolute error of 0.0464 in our deep learning model. Our Q‐learning model exhibits a linearly increasing curve, which denotes the increase in the relativegain caused by the selection of the action of performing the reorganization attack.

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Section: Related Workmentioning

confidence: 99%

Q‐Learning model for selfish miners with optional stopping theorem for honest miners

Jeyasheela Rakkini,

Geetha

2023

Int Trans Operational Res

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Analysis of blockchain system based on $$BMAP/PH^{[b]}/1$$ queueing model

Chakravarthy,

Ozkar,

Goel

2024

J Supercomput

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The use of blockchain in the insurance industry: A bibliometric analysis

Fathi Eletter

2024

Insurance Markets and Companies

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Blockchain technology has surfaced as a formidable catalyst, capable of reshaping the operational dynamics of conventional businesses. The integration of blockchain technology within the insurance industry has prompted a need for comprehensive understanding and assessment. This study aims to elucidate the relevance of blockchain in the insurance sector, identify key trends, influential research, and the overarching goals motivating the incorporation of blockchain in insurance practices. The study employed a systematic review to collect 125 research articles published between 2017 and 2023 focused on blockchain in insurance from the Web of Science database. This study used Biblioshiny. The analysis encompasses a wide range of parameters, including publication trends, influential authors, thematic clusters, and the geographic distribution of research. IEEE Access emerges as a prominent platform, hosting 11 articles dedicated to this topic. The results of keyword co-occurrence highlighted two keywords: blockchain and insurance. The results of the country collaboration map highlighted particular shortcomings, such as the concentration of research in countries like China, the USA, India, KSA, Malaysia, and Australia. A lack of research in developing and underdeveloped countries and insufficient knowledge sharing among researchers highlights a research gap. In conclusion, the analysis offers valuable insights into the evolving landscape of blockchain within the insurance industry.

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Operations Research in the Blockchain Technology

Cited by 3 publications

References 35 publications

Q‐Learning model for selfish miners with optional stopping theorem for honest miners

Q‐Learning model for selfish miners with optional stopping theorem for honest miners

Analysis of blockchain system based on $$BMAP/PH^{[b]}/1$$ queueing model

The use of blockchain in the insurance industry: A bibliometric analysis

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