Blockchain Papers

Hao Wen, Xinpeng Cheng, Zhengjun Lu, Ruiyao Zhao

No abstract is available for this record.

Luis Angel D. Bathen, Divyesh Jadav

Abstract The emergence of crypto currencies such as Bitcoin and Ethereum have shown the value in decentralized technologies. The idea of having 24/7 access to programmable money peaked the interest in the field, and as a by-product, came the realization that the same core technologies that enable programmable dog money, can enable highly-available DNS services, highly-available storage services, 24/7 asset exchanges, and peer-to-peer marketplaces to name a few. This paper explores the use of smart contracts in multi-cloud environments in order to facilitate business processes across multiple providers speaking different languages in terms of policies, best practices, APIs, and SLAs.

Aswani Devi Aguru, Suresh Babu Erukala, Ilaiah Kavati

No abstract is available for this record.

Wipawee Nayam

Unlike traditional currencies that rely on centralized such as banks or governments, cryptocurrencies today have become popular due to its decentralized transactions. Decentralization takes advantage of no requirement for intermediaries, thus reducing transaction fees and processing time. However, investing in cryptocurrencies incurs risks and uncertainties due to price volatility and rapid changes. The fact that prediction of asset prices is complex due to the influence of multiple factors on price movements. This paper studied the technical factor to analyze the short-term returns of Ethereum in the periods of 1-10 days. The historical data containing Ethereum closing price are collected from CoinGecko. The twenty-two indicators are chosen from Momentum, Volatility, and Sentiment factors as candidates to provide valuable insights in market trends. The values of these indicators are calculated based on past Ethereum closing prices and then used for XGBoost learning to discover patterns in previous trading. The model performance is evaluated using the multi-class AUC-ROC metric, which measures the accuracy of predicting three types of Ethereum returns: Downtrend, Sideway, and Uptrend. The experimental results reported that the models achieved the values of micro-average ROC curve ranging from 0.65 to 0.67. Moreover, the study emphasizes the importance of considering momentum indicators when making investment decisions in Ethereum.

Syed Muhammad Sajjad, Muhammad Rafiq Mufti, Muhammad Yousaf, Waqar Aslam · 9 authors

DDoS (distributed denial of service) attacks have drastically effected the functioning of Internet‐based services in recent years. Following the release of the Mirai botnet source code on GitHub, the scope of these exploitations has grown. The attackers have been able to construct and launch variations of the Mirai botnet thanks to the open‐sourcing of the Mirai code. These variants make the signature‐based detection of these attacks challenging. Moreover, DDoS attacks are typically detected and mitigated reactively, making DDoS mitigation solutions very expensive. This paper presents a proactive IoT botnet detection system that detects the anomalies in the behavior of the IoT device and mitigates the DDoS botnet exploitation at the source end, which makes our proposal a low‐cost solution. Further, this paper uses a collaborative trust relationship‐based threat intelligence‐sharing mechanism to prevent other IoT devices from being compromised by the detected botnet. The researchers have evaluated the collaborative threat intelligence sharing mechanism using Ethereum Virtual Machine and Hyperledger. The performance of our proposed system can detect 97% of the Mirai botnet attack activities. Furthermore, our collaborative threat intelligence sharing mechanism based on the Ethereum Virtual Machine showed more scalability.

Jing Zhang, Yue Jiang, Jie Cui, Debiao He · 6 authors

Vehicular ad hoc networks (VANETs) connect all vehicles through wireless channels. They provide extensive real-time traffic information services that improve driving safety and traffic management efficiency. However, VANETs are vulnerable to security attacks because of the open wireless nature of their communication channels. Most security mechanisms for traditional VANETs are centralized and have certain limitations in satisfying security requirements, such as anti-single-point failure, distributed security authentication of messages, and privacy preservation in VANETs. To address these issues, herein, we propose a dual blockchain-assisted conditional privacy-preserving authentication framework and protocol for VANETs. The identity authentication and privacy preservation of vehicles in VANETs can be realized without relying on a centralized trusted third party. The proposed scheme also allows for the conditional tracking of illegal vehicles. The decentralized dynamic revocation of illegal vehicles can be realized through smart contracts, rendering the scheme efficient and scalable. We implement this scheme in an Ethereum test network to demonstrate its feasibility and conduct an in-depth security analysis and comprehensive performance evaluation of the proposed scheme. The results demonstrate that the proposed scheme is an effective solution for the development of a decentralized authentication system for VANETs.

Hamza Baniata, Attila Kertész

Trusted online credential management solutions are needed for instant and practical verification. Most of the available frameworks targeting this field violate the privacy of end-users or lack sufficient solutions in terms of security and Quality-of-Service (QoS). In this paper, we propose a Privacy-aware Fog-enhanced Blockchain-based online credential management solution, namely PriFoB. Our proposed solution adopts a public permissioned Blockchain model with different reliable encryption schemes, standardized Zero-Knowledge-Proofs (ZKPs) and Digital Signatures (DSs) within a Fog–Blockchain integrated framework, which is also GDPR compliant. We deploy both the Proof-of-Authority (PoA) and the Signatures-of-Work (SoW) consensus algorithms for efficient and secure handling of Verifiable Credentials (VCs) and global accreditation of VC issuers, respectively. Furthermore, we propose a novel three-dimensional DAG-based model of the Distributed Ledger (3DDL), and provide a ready-to-deploy PriFoB implementation. We discuss insights regarding the utilization and the potential of PriFoB, and evaluate it in terms of security, privacy, latency, throughput and power utilization. We analyze its performance in different layers of a Fog-enabled cloud architecture with simulation and emulation, and we show that PriFoB outperforms several Blockchain-based solutions utilizing Ethereum, Hyperledger Fabric, Hyperledger Besu and Hyperledger Indy platforms.

Xiaoxu Du, Zhenpeng Tang, Junchuan Wu, Kaijie Chen · 5 authors

The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is important for both market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms; then, the residual terms are decomposed and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are major cryptocurrency assets, are compared with other benchmark models constructed based on different ideas, and we find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.

Adrián Viéitez Mariño, Matilde Santos, Rodrigo Naranjo

No abstract is available for this record.

Agostino Capponi, Ruizhe Jia, Ye Wang

No abstract is available for this record.

Kankam O. Adu-Kankam, Luís M. Camarinha-Matos

The rapid proliferation of renewable energy communities/ecosystems is an indication of their potential contribution to the ongoing energy transition. A common characteristic of these ecosystems is their complex composition, which often involves the interaction of multiple actors. Currently, the notions of "networking", "collaboration", "coordination", and "cooperation", although having different meanings, are often loosely used to describe these interactions, which creates a sense of ambiguity and confusion. To better characterize the nature of interactions in current and emerging ecosystems, this article uses the systematic literature review method to analyse 34 emerging cases. The objective is threefold (a) to study the interactions and engagements between the involved actors, aiming at identifying elements of collaboration. (b) Identify the adopted technological enablers, and (c) ascertain how the composition and functions of these ecosystems compare to virtual power plants. The outcome revealed that the interactions between the members of these ecosystems can be described as cooperation and not necessarily as collaboration, except in a few cases. Regarding technological enablers, a vast panoply of technologies, such as IoT devices, smart meters, intelligent software agents, peer-to-peer networks, distributed ledger systems/blockchain technology (including smart contracts, blockchain as a platform service, and cryptocurrencies) were found. In comparison with virtual power plants, these ecosystems have similar composition, thus, having multiple actors, comprised of decentralized and heterogeneous technologies, and are formed by aggregating various distributed energy resources. They are also supported by ICT and are characterized by the simultaneous flow of information and energy.

Ursula von Schlenhenried

Non-Fungible Tokens, or NFTs, are digital assets based on blockchain technology and are steadily growing in popularity in the art market. The technology has created a novel way of establishing ownership through tamper-resistant cryptographic records. A majority of NFTs are created via the Ethereum protocol and are most notably associated with other assets, such as digital art. Even prominent auction houses, like Christie’s, have joined the action. NFTs offer a whole host of new and interesting legal concerns, including questions surrounding smart contracts. The concerns surrounding traditional art, however, are long-standing and include (but are not limited to) provenance, authenticity, title, copyright infringement, and various art crimes established by statute. The combination of existing law and new technology creates uncertainty and requires exploration. This note explores how NFTs may influence a few of the long-standing issues in art law, specifically if an NFT were to be associated with tangible artwork. Further, this note argues that NFTs show promise at resolving some of the issues surrounding provenance, title, and authenticity if the artwork is created with an NFT in mind; however, the technology can also complicate these same issues—most notably copyright issues—especially with existing artworks not created with NFTs in mind. The legal concerns surrounding NFTs are uncertain and only just emerging, and as is the case with most nascent technology, regulation lags. Yet, the potential benefits to artists are encouraging and ever evolving.

Taochun Wang, Huimin Shen, Jian Chen, Fulong Chen · 6 authors

With the continuous innovative development and popularization of mobile smart devices , the application of Mobile Crowd Sensing (MCS) continues to be studied extensively. However, existing centralized MCS applications that use servers for task publishing and data collection exhibit common problems, such as single points of failure and security vulnerabilities . Accordingly, we proposed a hybrid blockchain-based identity authentication scheme for MCS called HBIA, which uses blockchain technology to resolve the single-point failure problem. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses it to construct a hybrid blockchain , with the cluster head node and internal cluster node authenticating on the public and private chains, respectively. We also implemented zero-knowledge proof (ZKP) to ensure the privacy of participants’ identities, thus balancing the contradiction between blockchain transparency and security. In addition, HBIA uses the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) technology to enable off-chain computing and on-chain verification, further reducing the blockchain’s workload. Finally,​ HBIA was evaluated based on the pavement crack detection task and tested on the Ethereum public test network known as Ropsten. The test results indicate that the identity authentication scheme proposed in this paper is superior to existing schemes in terms of authentication time.

Zubair Ahmad, Zahra Almaspoor, Faridoon Khan, Sharifah E. Alhazmi · 7 authors

<abstract><p>Cryptocurrency is a digital currency and also exists in the form of coins. It has turned out as a leading method for peer-to-peer online cash systems. Due to the importance and increasing influence of Bitcoin on business and other related sectors, it is very crucial to model or predict its behavior. Therefore, in recent, numerous researchers have attempted to understand and model the behaviors of cryptocurrency exchange rates. In the practice of actuarial and financial studies, heavy-tailed distributions play a fruitful role in modeling and describing the log returns of financial phenomena. In this paper, we propose a new family of distributions that possess heavy-tailed characteristics. Based on the proposed approach, a modified version of the logistic distribution, namely, a new modified exponential-logistic distribution is introduced. To illustrate the new modified exponential-logistic model, two financial data sets are analyzed. The first data set represents the log-returns of the Bitcoin exchange rates. Whereas, the second data set represents the log-returns of the Ethereum exchange rates. Furthermore, to forecast the high volatile behavior of the same datasets, we apply dual machine learning algorithms, namely Artificial neural network and support vector regression. The effectiveness of these models is evaluated against self exciting threshold autoregressive model.</p></abstract>

Rana Muhammad Zulqarnain, Imran Siddique, Sayed M. Eldin, Shahid Hussain Gurmani

One of the substantial innovations achieved through digitalization is cryptocurrencies, also known as simulated or digital currencies, which have been deliberated in the modern era as a new platform particularly suitable for financiers. Several cryptocurrencies, such as Bitcoin, Ethereum, Binance Coin, and Tether, do not trust a dominant expert. The classification and conduction of insecurity and the confirmation of digital currencies complicate decision-making. q-rung orthopair fuzzy hypersoft sets are an emerging arena of research intended to report the confidential restrictions of q-rung orthopair fuzzy soft sets on multiparameter indefinite functions. Such a function maps a tuple of sub-parameters to a power set of the universe. It emphasizes allocating attributes to their corresponding sub-attribute values in disjoint sets. These structures sort it an innovative systematic tool for addressing the obstacles of hesitancy. The q-rung orthopair fuzzy hypersoft set (q-ROFHSS) expertly compacts with tentative and ambagious facts equated to the existing q- rung orthopair fuzzy soft set and Pythagorean fuzzy hypersoft set (PFHSS). It is the most compelling mode for enlarging imprecise data in decision-making (DM). This investigation’s ultimate impartiality is presenting interactional algebraic operational laws for q-ROFHSS. Furthermore, some interaction aggregation operators (AOs) have been anticipated via our proposed operational laws, such as q-rung orthopair fuzzy hypersoft interactive weighted average (q-ROFHSIWA) and q-rung orthopair fuzzy hypersoft interactive weighted geometric (q-ROFHSIWG) operators with their essential properties. In reality, a mathematical illustration of DM obstacles is pondered to substantiate the proven technique’s dominance. Based on the projected interaction AOs, robust multi-criteria group decision-making (MCGDM) design has been offered, which carries the most practical consequences associated with predominant MCGDM methods. The significance spectacle is that the intentional methodology is more operative and steady in bearing weird facts based on q-ROFHSS.

Yingming Zeng, Liyu Deng, Haibin Zhang

No abstract is available for this record.

Manuel Pereira Lamela, Jesús Rodríguez-Molina, Margarita Martínez, Juan Garbajosa

The possibilities that Distributed Ledger Technologies (DLTs) offer for cooperation, development, and achievement of the Sustainable Development Goals (SDGs) are remarkable. This is because DLTs enable several key features, such as sharing complete information about every data transaction in the distributed system that participants belong to, the immutability of the recorded data transactions, or consensus among what data can be regarded as true, of great usefulness for the implementation of the SDGs. As far as developing countries are concerned, this information could be useful in trading locally produced goods, as it could enhance the reputation and profitability of Small-Scale Producers (SSPs). Unfortunately, it is rare to find a digitalized marketplace that has been specifically implemented for this application domain. This paper puts forward a blockchain-based marketplace that makes use of Smart Contracts and offers information about how the sold goods were produced and can be traced to their very origin. Besides, cloud computing has been conceived to be used in this development from the beginning to reduce the computational resources required by end user operations. An implementation with cloud computing facilities, software components running on the Ethereum blockchain and a web front end have been tested with satisfactory performance results.

Katarina Adam

No abstract is available for this record.

Swati Srivastava, Roheet Bhatnagar, Gaurav Srivastava, Urvashi Srivastava

No abstract is available for this record.

Ibrahim Barada

Blockchain transactions compete for limited space in blockchain blocks. Miners prefer to include transactions with higher fees into new blocks. Ethereum released EIP-1559 as an upgrade for its transaction pricing mechanism. The improvement proposal aims to stabilize the transaction pricing mechanism and improve the predictability of gas prices. In the context of Ethereum, gas price oracles predict fees such that transactions submitted at those fees make it into a block within a target delay. In practice, however, Ethereum gas price oracles are inaccurate, which makes it difficult for distributed applications to operate predictable services in terms of price and performance. To understand and measure oracle accuracy we define new gas prediction performance metrics. We demonstrate that oracles underprice transactions, causing them to miss the delay target. We also show that oracles overprice transactions, causing them to meet the delay target, but at a higher-than-necessary cost. As a result of oracles inaccuracies, users tend to either wait longer or pay more than sufficient gas prices for a transaction to get into a block. We provide a comparative analysis of five gas price oracles pre and post-the release of EIP-1559 showing their performance in terms of accuracy of acceptance, underpricing, and overpricing. We also discuss the factors that influence oracle accuracy and the effects of those inaccuracies in terms of time and money wasted. We apply our predefined metrics to study the performance of oracles pre and post-EIP-1559. We observe that EIP-1559 improved the transaction acceptance rate and shortened acceptance delays. On the other hand, we observe that EIP-1559 increased transaction overpricing. The current gas price prediction mechanisms required further investigation after the release of EIP-1559. Hence, we devised a new mechanism to predict gas prices of EIP-1559-compatible transactions on the Ethereum blockchain. The mechanism allows users to calculate gas prices based on the current block utilization and base fee. We measured the probability of acceptance, time wasted, and money wasted and noticed an increase in the probability of acceptance and a decrease in both time and money wasted in comparison to the currently existing oracles.

Amrita Jyoti, Gopal Gupta, Rashmi Mishra, Ankit Jaiswal

No abstract is available for this record.

Leonardo Alt, Martin Blicha, Antti E. J. Hyvärinen, Natasha Sharygina

Abstract Formally verifying smart contracts is important due to their immutable nature, usual open source licenses, and high financial incentives for exploits. Since 2019 the Ethereum Foundation’s Solidity compiler ships with a model checker. The checker, called SolCMC, has two different reasoning engines and tracks closely the development of the Solidity language. We describe SolCMC’s architecture and use from the perspective of developers of both smart contracts and tools for software verification, and show how to analyze nontrivial properties of real life contracts in a fully automated manner.

Ahmad Alsharef, Sonia Sonia, Monika Arora, Karan Aggarwal

No abstract is available for this record.

Neelam Chauhan, Rajendra Kumar Dwivedi

No abstract is available for this record.