Game theory attack pricing for mining pools in blockchain-based IoT

The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network.The employed computing power carries out the pool mining attacks on the attacked mining pool.To realize the win-win game between the malicious mining pool and the employee,the paper proposes an Employment Attack Pricing Algorithm(EAPA)of mining pools in blockchain based on game theory.In the EAPA,the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack,the revenue increment of malicious mining pools,and the revenue of the employee.It establishes a game model between the malicious mining pool and the employee under the employment attack.Then,the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation.In the strategy,the malicious mining pool analyzes the conditions for the employment attack,and uses the derivative method to find the optimal utilization value of computing power,employees analyze the conditions for accepting employment,and use the derivative method to find the optimal reward value of computing power.Finally,the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation.The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee.The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time.The EAPA outperforms the state-of-the-art methods such as SPSUCP,DPSACP,and FPSUCP.

A collaboration strategy in the mining pool for proof-of-neural-architecture consensus

In most popular public accessible cryptocurrency systems,the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners.In many recent novel blockchain consensuses,the deep learning training procedure becomes the task for miners to prove their workload.Thus,the computation power of miners will not purely be spent on the hash puzzle.In this way,the hardware and energy will support the blockchain service and deep learning training simultaneously.While the incentive of miners is to earn tokens,individual miners are motivated to join mining pools to become more competitive.In this paper,we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning.The mining pool manager partitions the full searching space into subspaces,and all miners are scheduled to collaborate on the Neural architecture search(NAS)tasks in the assigned subspace.Experiments demonstrate that the performance of this type of mining pool is more competitive than that of an individual miner.Due to the uncertainty of miners'behaviors,the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure completion of the tasks of high reward miners.

Characteristics of the main polluting trace elements in the water environment of mining subsidence pools

Mining subsidence pools are water bodies formed by soil subsidence near mines. We studied the impact the surrounding coal production activities and power plants have on these waters by measuring the concentrations of harmful trace elements in these waters. The concentration of the four elements F, Hg, Se and As increased by 0.92%, 0.78%, 0.70% and 0.81%, respectively, in the Datong mining subsidence pool from November 2004 to November 2006. The four elements increased by 1.58%, 1.23%, 1.08% and 0.92%, respectively, in the Xie’er mining subsidence pool and 1.16%, 1.06%, 1.02% and 1.01%, respectively, in the Pansan mining subsidence pool over the same time period. The absolute levels of F, Hg, Se and As in the pool are related to the background levels of the elements. A close relationship between nearby coal mines and power plants and increasing levels of the measured elements is noted.

Composition and distribution of main pollution trace element in water environment of mining subsidence

The impact on these water bodies of the surrounding coal production activities and the power plant through research the content and characteristics of harmful trace elements in coal contained in these water bodies.F,Hg,Se and As these four trace ele- ments increased 0.92%,0.78%,0.70% and 0.81% respectively in Datong mining subsi- dence pool from November 2004 to November 2006;the four elements increased 1.58%, 1.23%,1.08% and 0.92% respectively in Xie'er mining subsidence pool;the four ele- ments increased 1.16%,1.06%,1.02% and 1.01% respectively in Pansan mining sub- sidence pool.The conclusions show that the absolute values of F,Hg,Se and As in mining subsidence pool are relate with their background value,while the increase in their concen- tration and their environment of mine and electricity plant surrounded are closely linked.