Optimal Revenue Analysis of the Stubborn Mining Based on Markov Decision Process

Zhang, Yiting; Liu, Ming; Guo, Jianan; Wang, Zhaojie; Wang, Yilei; Liang, Tiancai; Singh, Sunil K.

doi:10.1007/978-3-031-20099-1_25

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[18] utilizes a game theoretic approach to study Nash equilibrium in blockchain mining when miners can hide their newly mined nodes. [19] formulates various selfish mining strategy in blockchain as a Markov decision process and solves it to obtain a lower bound on their performance. [20] analyzes the optimal mining time for pooled mining reward systems.…”

Section: Related Workmentioning

confidence: 99%

Emergence of Blockchain Applications with the 6G-Enabled IoT-Based Smart City

Gupta¹,

Ved²,

Kumari³

2022

Blockchain for 6g-Enabled Network-Based Applications

View full text Add to dashboard Cite

What are the optimal times for an Internet of Things (IoT) device to act as a blockchain miner? The aim is to minimize the energy consumed by low-power IoT devices that log their data into a secure (tamper-proof) distributed ledger. We formulate the energy-efficient blockchain mining for IoT devices as a multiple-stopping time partially observed Markov decision process (POMDP) to maximize the probability of adding a block in the blockchain; we also present a model to optimize the number of stops (mining instants). In general, POMDPs are computationally intractable to solve, but we show mathematically using submodularity that the optimal mining policy has a useful structure: 1) it is monotone in belief space, and 2) it exhibits a threshold structure, which divides the belief space into two connected sets. Exploiting the structural results, we formulate a computationally-efficient linear mining policy for the blockchainenabled IoT device. We present a policy gradient technique to optimize the parameters of the linear mining policy. Finally, we use synthetic and real Bitcoin datasets to study the performance of our proposed mining policy. We demonstrate the energy efficiency achieved by the optimal linear mining policy in contrast to other heuristic strategies.

show abstract

Section: Related Workmentioning

confidence: 99%