Blockchain Papers

Şule Öztürk Birim, Filiz Erataş Sönmez, Yağmur Sağlam

No abstract is available for this record.

Liu Yadong, Nathee Naktnasukanjn

As a financial asset, bitcoin has attracted the attention of many financial financial advisors and investors. This paper aims to analyze the dynamic correlation between bitcoin and two important financial assets, i.e., crude oil and gold. This paper selects weekly data from January 2014 to April 202

E. M. Roopa Devi, R. Shanthakumari, R. Rajdevi, S. Dineshkumar · 6 authors

No abstract is available for this record.

Kinshuk Dua

Recently, there has been a remarkable amount of research being done in both, the fields of Blockchain and Internet of Things (IoT). Blockchain technology synergises well with IoT, solving key problems such as privacy, concerns with interoperability and security. However, the consensus mechanisms that allows trustless parties to maintain an agreement, the same algorithms that underpins cryptocurrency mining, are usually extremely computationally expensive, making implementation on low-power IoT devices difficult. More importantly, mining requires downloading and synchronizing hundred of gigabytes worth of blocks which is far beyond the capabilities of most IoT devices. In this paper, we present an efficient, portable and platform-agnostic cryptocurrency mining algorithm using the Stratum protocol to avoid downloading the entire blockchain. We implement the algorithm in four different platforms- PC, ESP32, an emulator and an old PlayStation Portable (PSP) to demonstrate that it is indeed possible for any device to mine cryptocurrencies with no assumptions except the ability to connect to the internet. To make sure of ease of portability on any platform and for reproducibility of the reported results we make the implementation publicly available with detailed instructions at: https://anonymous.4open.science/r/cryptominer.

Paul Gambles

This paper answers three of the most frequently asked questions received in response to the paper crypt into crypto. We also received questions about stable coins, alt coins and Non-Fungible Tokens (NFTs), which we’ll cover in a future paper.

Ting Li, Mudassir Khan, Ashutosh Sharma, Mohd Dilshad Ansari

Abstract An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.

Ahmad Alsharef, Sonia Sonia, Monika Arora, Karan Aggarwal

No abstract is available for this record.

Micheal Olaolu Arowolo, Peace Ayegba, Shakirat Ronke Yusuff, Sanjay Misra

No abstract is available for this record.

Kunal Wasnik, Isha Sondawle, Rushikesh Wani, Namita Pulgam

Supply chain management frequently faced issues such as service redundancy, poor coordination between several departments, and lack of standardization as a result of the lack of transparency. Product counterfeiting is something which is very common now-a-days and it’s almost impossible to detect a counterfeit product just by looking at it. Counterfeiters cause significant challenges for legitimate firms, yet far too many people have no idea of the entire amount of counterfeit items’ influence on brands. There are several methods devised in the past to get away with this problem of product counterfeiting. The most popular methods are using RFID tags, Artificial Intelligence, QR code based systems, etc. But each of them had few disadvantages such as the QR code can be copied from a genuine product and placed on a fake product, artificial intelligence uses CNN and machine learning which needs heavy computational power and so on. The idea of this project is to improve detection of fake products by tracking its supply chain history. This is achieved with Blockchain technology which ensures the identification and traceability of real products throughout the supply chain. Blockchain based system, makes everything decentralized that may be accessed by several parties at the same time. One of its main advantages is that the recorded data is difficult to change without the consent of all parties concerned which makes the data extremely secure and protect from all vulnerabilities. This paper presents system designed using blockchain technology for detection of counterfeit products.

Si Chen

Digital currency is considered a form of currency which is used in the digital world such as digital forms or electronic devices. Several terms are synonyms for digital currency like digital money, electronic money, and cyber cash. Accurate prediction of the digital currency is an urgent necessity due to its impacts on the economic community. The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. Deep neural network (DNN) algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. The proposed method extracted features of related Bitcoin and used the informative ones. Transaction plan considered building nodes in terms of network. Development of deep learning algorithms opens the horizons for the development of electronic businesses that use digital currency. The proposed method achieved worthy results in terms of accuracy (53.4%) and correct prediction (MSE 1.02) and offers the prospect of other research in this area.

Bojan Belušić

Potreba izdavanja maloprodajnog digitalnog novca od strane središnjih banaka za građanstvo sve se češće spominje kao nužnost, ponajprije zbog financijske inkluzije i smanjenja korištenja gotovine u proteklih nekoliko godina. Osim toga, središnje banke razmatraju korištenje tehnologije raspodijeljenih glavnih knjiga zbog ubrzavanja veleprodajnog plaćanja i trgovanja imovinom te izbjegavanja korištenja pružatelja usluga u tu svrhu. S tim u vidu niz središnjih banaka u svijetu u posljednjih nekoliko godina pokrenuo je projekte i pilote za testiranje DLT sustava u svrhu izdavanja, trgovanja i plaćanja CDBC-om. Ovaj rad donosi načine provedbe i zaključke iz najznačajnijih te najbolje dokumentiranih istraživanja i pilota provedenih u tu svrhu. Nakon toga, rad ukratko prolazi kroz specifičnosti najčešće korištenih DLT platformi u navedenim istraživanjima i pilotima. Naposljetku se rad bavi sigurnosnim aspektima izdavanja CDBC-a na DLT platformi, uključujući tehnike za unaprjeđenje povjerljivosti te moguće ranjivosti i programske greške pametnih ugovora, te najboljim praksama koje bi trebalo slijediti kako bi se osigurala sigurnost takvih sustava.

Yakub Kayode Saheed, Raji Mustafa Ayobami, Terdoo Orje-Ishegh

No abstract is available for this record.

Xueqing Zhao, Hao Liu, Shuning Hou, Xin Shi · 6 authors

No abstract is available for this record.

Ali Aljofey, Qingshan Jiang, Qiang Qu

No abstract is available for this record.

Zhou Jian, Shi Yan, Jie Zhang

Increasingly frequent illegal transactions hinder the security of Ethereum transactions, and the anonymity of electronic money makes it difficult to track and analyze problems. In this paper, the transaction data of the Ethereum trading platform is used as the data source, and the marked illegal account and the unmarked normal account data set are used as the training set. Based on the CatBoost algorithm, the overall prediction of the various types of illegal accounts is made. The process adopts multiple cross-validation, the accuracy of the established algorithm model prediction reached 94.07%, and the evaluation metric of the area under the curve of the receiver reached 0.9846. The proposed scheme accurately predicts illegal behaviors on the Ethereum trading platform and effectively improves the blockchain-based trading environment.

Yanjun Zuo, Zhenyu Qi

The oil and gas industry involves a high level of operational expenditure and often faces high risks of asset safety and operational failures. It has never been so important to monitor and control the oil field operations remotely in real-time to ensure safety and efficiency. The traditional monitoring and control systems for oil field operations are typically centralized, prone to failure, and lack efficiency. Blockchain technology mitigates the centralization problem by creating a decentralized, immutable and transparent control environment for automatic monitoring and control of industrial operations. In this study, we propose a blockchain-based IoT framework for real-time monitoring and control to increase oil field operation and asset efficiency and safety. We present the key components of the framework, including the system architecture, operation flows, algorithms, and smart contracts. As a proof-of-concept modeling, a smart contract is developed and validated on a blockchain test platform. A comparative analysis shows the advantages of using blockchain technology and smart contract to provide trustworthy and automatic monitoring and control for oil field operations.

Chandrashekar Jatoth, Rishabh Jain, Ugo Fiore, Subrahmanyam Chatharasupalli

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%.

P. Lavanya, N. Ananthi, K Kumaran, M. Abinaya · 7 authors

The availability of fake product in the Market is one of the biggest challenges of the online retail industry. These products appear to be genuine but they are imitations of the original branded products. Almost 20% of the products sold on online websites are fake. In recent times, block chain is receiving more engagement and various applications are been emerged from this technology. In this paper, to ensure that consumers need not depend on the distributers to know whether their products are authentic or not, we are using the decentralized Block chain technology approach. We describe a decentralization Block chain network with anti-counterfeiting items, which allows producers to deliver items without having to run clear outlets, lowering product quality assurance costs dramatically.

Malkar Vinod Ramchandra, K Dinesh Kumar, Abhijit Sarkar, Samrat Kr. Mukherjee · 5 authors

No abstract is available for this record.

Aman Thakkar, Nilay Rane, Amey Meher, Swapnil Pawar

Counterfeiting is a global issue affecting a wide range of industries including luxury goods, clothing and pharmaceuticals among others. Proving or disproving the authenticity of an asset can be a challenge because traditional supply chains are long, complex and lack transparency. However, placing the supply chain on a decentralized technology like blockchain will ensure that goods will have provenance due to its immutable transaction history, which in turn makes it difficult to masquerade counterfeit products as real and take their place in the supply chain. Blockchain can bring all entities like the manufacturers, suppliers and distributors together in a close-knit and transparent manner. It keeps records of all transactions and other necessary information visible to all concerned parties while minimizing the possibility of records being tampered with. Therefore we intend to provide a lightweight, cost effective implementation that makes a supply chain secure, decentralized and verifiable besides omitting inadequacies in the supply of genuine products and cutting back on costs involved in detecting faulty areas.

Shen Lvping

With the development of information technology and network technology, digital archive management systems have been widely used in archive management. Different from the inherent uniqueness and strong tamper-proof modification of traditional paper archives, electronic archives are stored in centralized databases which face more risks of network attacks, data loss, or stealing through malicious software and are more likely to be forged and tampered by internal managers or external attackers. The management of intangible cultural heritage archives is an important part of intangible cultural heritage protection. Because intangible heritage archives are different from traditional official archives, traditional archive management methods cannot be fully applied to intangible heritage archives’ management. This study combines the characteristics of blockchain technology with distributed ledgers, consensus mechanisms, encryption algorithms, etc., and proposes intangible cultural heritage file management based on blockchain technology for the complex, highly dispersed, large quantity, and low quality of intangible cultural heritage files. Optimizing methods, applying blockchain technology to the authenticity protection of electronic archives and designing and developing an archive management system based on blockchain technology, help to solve a series of problems in the process of intangible cultural heritage archives management.

Yuwei Liu, Biwen Shen, Shujing Yang

The advent of the data age is impacting the entire world, changing people's lives, work, and thinking. The advent of the big data era has also had a great impact and influence on the management work, and it is a new test of the archives department's archives management ability and level. The new data distributed storage technology-blockchain, provides new methods and ideas for data circulation and sharing through the characteristics of decentralization, timing, distributed ledger, open consensus, openness and transparency, and anti-tampering. Based on big data, this paper studies the construction of a decentralized digital authentication system for cultural archives management.

S. Ezhilin Freeda, T.C.Ezhil Selvan, I.G. Hemanandhini

Bitcoin is a decentralized digital currency that is completely virtual. It is not managed by a government or a bank. It's a digital file that can be shared from one user to the next. Bitcoin's popularity has risen in recent years, and many people have begun to invest in it. Since investment on bitcoin is increasing day to day and the bitcoin price fluctuates frequently, traders need a way to predict its price in prior so that the risks associated with it can be reduced and capital gain can be improved. The various existing works on price prediction have low accuracy and predict short term price only. Due to the difficulty of determining the exact existence of a Time Series model, it is difficult to generate appropriate forecasts. The Proposed technique uses deep learning to predict bitcoin prices with Recurrent Neural Network model using the time series data to provide the better accuracy. The novelty of work is to obtain a long-term prediction, the recurrent neural network model is trained and tested on the available dataset. This work predicts the value of bitcoin for the year 2021. When compared with other machine learning algorithms like Random Forest, Gaussian Naïve Bayes, Support Vector Machine, K-Nearest Neighbors algorithms the proposed work shows improved accuracy of 76.99% using RNN model.

Jihan Li, Yangqinzhe Xiao, Muhan Yao

Bitcoin (cryptocurrencies) is the hottest economic product in recent years. However, due to its highly volatile trend, it is difficult for investors to invest in a targeted manner in the direction of its market trend. And the correlation among cryptocurrencies is often overlooked. In this paper, to solve this problem, the currency data of the past year has been used to put into four regression models to predict and analyze the top five currencies(ranked by market cap) on the market. Among the models, the KNN model has the highest accuracy, reaching 0.923.