Simulation Model for Blockchain Systems Using Queuing Theory

Memon, Raheel Ahmed; Li, Jian Ping; Ahmed, Junaid

doi:10.3390/electronics8020234

Cited by 92 publications

(45 citation statements)

References 41 publications

(46 reference statements)

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“…This market is characterized by high volatility, no closed trading periods, relatively smaller capitalization, and high market data availability [ 1 ]. The financial feasibility of the cryptocurrency market in relation to other markets has been documented [ 2 , 3 , 4 ] and the algorithms upon which the cryptocurrencies operate have been validated in other fields as well [ 5 , 6 ]. The cryptocurrency market seems to behave independently from the other financial markets [ 2 ], but there is a strongly influenced by Asian economies [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Valencia

Gómez-Espinosa

Valdés-Aguirre

2019

Entropy

194

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Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.

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Section: Introductionmentioning

confidence: 99%

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Valencia

Gómez-Espinosa

Valdés-Aguirre

2019

Entropy

194

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“…In bitcoin, every 10 minutes a new block is added to the blockchain, thus tampering with existing block requires incredible computation power to mine all the succeeding blocks before addition of another new block. Mining operation is an expensive operation, in bitcoin, the complexity of mathematical puzzle of generated hash is adjusted after every 2 weeks [68]. The addition of latest hardware technologies to speed up mining process has increased difficulty level of mathematical puzzle, so that a normal computer would take more than year to solve it, this is the reason why ASIC machines have come in to play [69].…”

Section: B Blockchainmentioning

confidence: 99%

“…Memon et al proposed model based on two M/M/1 and M /M /n queues (one for memory pool another for mining pool) with fork/join stations, the simulation is also performed in JMT. The article presents detailed analysis for a variety of performance metrics useful for observing behavior of blockchain-based applications before deploying them over blockchain network [68].…”

Section: F Simulation Technique and Toolsmentioning

confidence: 99%

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DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things

Memon

Nazeer

et al. 2019

IEEE Access

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Integration of blockchain and Internet of Things (IoT) to build a secure, trusted and robust communication technology is currently of great interest for research communities and industries. But challenge is to identify the appropriate position of blockchain in current settings of IoT with minimal consequences. In this article we propose a blockchain-based DualFog-IoT architecture with three configuration filter of incoming requests at access level, namely: Real Time, Non-Real Time, and Delay Tolerant Blockchain applications. The DualFog-IoT segregate the Fog layer into two: Fog Cloud Cluster and Fog Mining Cluster. Fog Cloud Cluster and the main cloud datacenter work in a tandem similar to existing IoT architecture for real-time and non-real-time application requests, while the additional Fog Mining Cluster is dedicated to deal with only Delay Tolerant Blockchain application requests. The proposed DualFog-IoT is compared with existing centralized datacenter based IoT architecture. Along with the inherited features of blockchain, the proposed model decreases system drop rate, and further offload the cloud datacenter with minimal upgradation in existing IoT ecosystem. The reduced computing load from cloud datacenter doesn't only help in saving the capital and operational expenses, but it is also a huge contribution for saving energy resources and minimizing carbon emission in environment. Furthermore, the proposed DualFog-IoT is also being analyzed for optimization of computing resources at cloud level, the results presented shows the feasibility of proposed architecture under various ratios of incoming RT and NRT requests. However, the integration of blockchain has its footprints in terms of latent response for delay tolerant blockchain applications, but real-time and non-real-time requests are gracefully satisfying the service level agreement. INDEX TERMS Blockchain, Internet of Things, fog layer, DualFog-IoT, quality of service (QoS).

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“… Speed of Web: The Internet speed affects the processing of the operations by the participants across the network which makes the network slower as a whole.  Memory-Pool: It is a special place holder where the network stores all the TXs in a queue (in a case of using temporary shared memory of the participants that stores the unconfirmed TXs initially) [16]. If the block size is not enough to cover all the TXs made over the last epoch, some TXs are left in the Memory-Pool.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Transaction Validation Protocol for a Secure Distributed IoT Network

et al. 2020

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Blockchain is attracting more and more attention to its applicability in the fields of Internet of Things (IoT). In particular, it is able to store data in unalterable blocks, associated with its secure peer-topeer in a growing problem of transaction authorization in industrial and service provisioning applications. Moreover, it facilitates decentralized transaction (TX) validation and distributed ledger. The underneath algorithm of TX selection for validation may not be effective in terms of delay of various services of the applications. Because the existing random-based or fee-based selections are a delay insensitive that does not guarantee a minimum delay of a time-critical TX. This paper proposes a blockchain-based transaction validation protocol for a secure distributed IoT network. It includes a context-aware TX validation technique, where a TX is validated by a miner with the priority of a service. Besides, we adopt the Software Defined Networking enabled gateway as a middleware between IoT and the blockchain network in which the control operations and security of the network in a largescale are ensured. The proposed network model has evaluated and compared to the Core network. The results ensure the given priority in TX validation is more delay sensitive than the existing technique to provide quality of service of the network. INDEX TERMS Internet of things, blockchain technology, software defined networking, delay, security and privacy.

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Simulation Model for Blockchain Systems Using Queuing Theory

Cited by 92 publications

References 41 publications

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things

Blockchain-Based Transaction Validation Protocol for a Secure Distributed IoT Network

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