PRICHAIN: A Partially Decentralized Implementation of UbiPri Middleware Using Blockchain

Ochôa, Iago Sestrem; Silva, Luís Augusto; Mello, Gabriel de; Silva, Bruno Alves da; Paz, Juan F. De; González, Gabriel Villarrubia; García, Nuno M.; Leithardt, Valderi Reis Quietinho

doi:10.3390/s19204483

Cited by 14 publications

(11 citation statements)

References 34 publications

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“…In the context of time series, there are various scenarios and applications that demand data management and communications between different devices, users and data in general. There are several solutions that address data management based on privacy [14] and information security rules [15], including decentralized implementation [16], smart environments [17,18], disruptive technologies [19], and vulnerability prediction [20].…”

Section: Time Series Forecastingmentioning

confidence: 99%

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

Jesus¹,

Hoppe²,

Sartori³

et al. 2022

Preprint

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This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

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Section: Time Series Forecastingmentioning

confidence: 99%

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

Jesus¹,

Hoppe²,

Sartori³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the context of time series, there are various scenarios and applications that demand data management and communications between different devices, users and data in general. There are several solutions that address data management based on privacy [15] and information security rules [16], including decentralized implementation [17], smart environments [18,19], disruptive technologies [20], and vulnerability prediction [21].…”

Section: Time Series Forecastingmentioning

confidence: 99%

LSTM Applied for Commodity Futures Price Forecast

Jesus¹,

Hoppe²,

Sartori³

et al. 2022

Preprint

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This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.

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“…Different references show the use of the Ethereum platform for the development of applications in different areas. In [25,26], the authors developed implementations to ensure privacy in IoT environments. In [27], the authors developed a blockchain architecture to prevent fake news in social media.…”

Section: Blockchainmentioning

confidence: 99%

A Cost Analysis of Implementing a Blockchain Architecture in a Smart Grid Scenario Using Sidechains

Ochôa

Silva

Mello

et al. 2020

Sensors

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Smart grid systems have become popular and necessary for the development of a sustainable power grid. These systems use different technologies to provide optimized services to the users of the network. Regarding computing, these systems optimize electrical services by processing a large amount of the data generated. However, privacy and security are essential in this kind of system. With a large amount of data generated, it is necessary to protect the privacy of users, because this data may reveal the users’ personal information. Today, blockchain technology has proven to be an efficient architecture for solving privacy and security problems in different scenarios. Over the years, different blockchain platforms have emerged, attempting to solve specific problems in different areas. However, the use of different platforms fragmented the market, which was no different in the smart grid scenario. This work proposes a blockchain architecture that uses sidechains to make the system scalable and adaptable. We used three blockchains to ensure privacy, security, and trust in the system. To universalize the proposed solution, we used the Open Smart Grid Protocol and smart contracts. The results show that architecture security and privacy are guaranteed, making it feasible for implementation in real systems; although scalability issues regarding the storage of the data generated still exist.

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PRICHAIN: A Partially Decentralized Implementation of UbiPri Middleware Using Blockchain

Cited by 14 publications

References 34 publications

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

LSTM Applied for Commodity Futures Price Forecast

A Cost Analysis of Implementing a Blockchain Architecture in a Smart Grid Scenario Using Sidechains

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