Peter M. Resch, Christian Schroeder, A. Pourmovahed, Kristy Brinker Brouwer
Since the anonymous person or group known as Satoshi Nakamoto created Bitcoin in 2008, interest in the cryptocurrency has grown exponentially. In the past 14 years, thousands of additional cryptocurrencies have been created, some still active while others have faded into obscurity. Investors have flocked to cryptocurrencies for a variety of reasons from technology to decentralization to a hedge against inflation to simply chasing profits. For all the proponents that Bitcoin and other cryptocurrencies have, they also have their share of critics. Recently, many people around the world have criticized cryptocurrencies for the amount of energy that they consume for new coins to be mined. This article aims to assess current data regarding energy usage for mining cryptocurrencies as well as, understand what actions are being taken to reduce the carbon footprint of these cryptocurrency mining activities around the world.
As the backbone of the smart grid, smart metering systems enable customers, as well as utility companies, to have access to real-time information on consumption patterns in electricity, gas, and water. From a technical standpoint, smart metering systems (1) reduce the need to verify electricity, gas, and water consumption services, (2) allow for efficient management of electricity, gas, and water consumption patterns, and (3) protect natural resources. Additionally, smart metering systems provide a number of important functions that should be performed manually, such as automatic and remote measurement of electricity, gas, and water consumption, connect or disconnect service, tampering detection, outage identification and isolation, and collecting, aggregating, and analyzing real-time information of the fundamental parameters associated with customers’ services, i.e., voltage, current, etc. Besides, considering the number of customers, there are several limitations to accessing and sharing the data between utility companies and customers, and therefore, reliable and secure communication infrastructure is needed. To resolve such issues, a conceptual and technological Blockchain-based system is developed to securely share the real-time collected data from a set of various sensors for monitoring and control of electricity, gas, and water consumption, namely the multifunctional integrated smart metering system. The proposed system takes advantage of a decentralized structure, connectivity of the Internet of Things (IoT) nodes, data privacy, and transparency and auditability. The proposed Blockchain-based smart metering system allows customers to securely monitor their actual electricity, gas, and water consumption for a specific period of time and review the full history of consumption. This leads to finding solutions to rationalize the consumption of electricity, gas, and water resources.
Cryptocurrency blockchain data encounter a class-imbalance problem due to only a few known labels of illicit or fraudulent activities in the blockchain network. For this purpose, we seek to compare various resampling methods applied to two highly imbalanced datasets derived from the blockchain of Bitcoin and Ethereum after further dimensionality reductions, which is different from previous studies on these datasets. Firstly, we study the performance of various classical supervised learning methods to classify illicit transactions or accounts on Bitcoin or Ethereum datasets, respectively. Consequently, we apply various resampling techniques to these datasets using the best performing learning algorithm on each of these datasets. Subsequently, we study the feature importance of the given models, wherein the resampled datasets directly influenced on the explainability of the model. Our main finding is that undersampling using the edited nearest-neighbour technique has attained an accuracy of more than 99% on the given datasets by removing the noisy data points from the whole dataset. Moreover, the best-performing learning algorithms have shown superior performance after feature reduction on these datasets in comparison to their original studies. The matchless contribution lies in discussing the effect of the data resampling on feature importance which is interconnected with explainable artificial intelligence (XAI) techniques.
Smart grids are evolving towards intelligent electricity grid where the operation of systems is distributed and automatised. Technical solutions to achieve these future needs are proposed using blockchain with smart contracts in many studies, where smart contracts enhance automation. Fundamentally smart contracts will increase security because of their distributed nature and since it inherits the security of blockchain. However, smart contracts are software components, which have special features like the unstoppable nature of applications and may use special languages like Solidity. Our aim in this paper is to get a holistic review in the smart contract life cycle, what potential new vulnerabilities and threats will they introduce and how can they be prevented, and what smart contract specific issues programmers should focus on. We also propose a future direction to achieve more secure smart contracts in smart energy systems.
Md. Rafiqul Islam, Muhammad Mahbubur Rahman, Mohammed Ataur Rahman, Muslim Har Sani Mohamad · 5 authors
The alternative energy generation sources have increased drastically from centralized systems to distributed systems which increases the stability of energy distribution management systems and reduces the distribution cost as well. On the other hand, it reduces the probability of major area electricity blackout chances and decreases the energy distribution loss. For proper distribution and management of energy, there are different types of advanced technologies like artificial intelligence, and the Internet of Things (IoT) available, but a blockchain automated system is one of the best choices and is highly recommended. Various aspects of blockchain technology and energy management system have been discussed in this review paper where a total number of 423 journal papers, articles, and online information sources have been reviewed in the initial stage, and finally, 63 published research articles have been selected for review. There are several topics, including technology overview in energy management systems, blockchain application of energy trading, blockchain technology implementation challenges, distributed energy management system with Ethereum, and a conclusion with some recommendations have been discussed. Blockchain and Distributed Ledger Technology (DLT) are highly transparent, authenticate, and secure systems that can be used for distributing the energy between distributor and consumer without an intermediator which increases the overall efficiency of the system. This paper aims to highlight the blockchain and distributed ledger technology and how it works as well as optimize the transaction processing cost among the participants of the consortium network. This paper will make a significant contribution to the new research work and in the field of energy management systems.
Much has changed in the power industry since the inception of commercial electrical power systems. What has not changed is the cycle of disruption and adaptation empowered by innovation. The next disruption is on the horizon and is fueled by the push to decarbonize the grid. In this article, I argue that the next disruption will be shaped by three technologies: artificial intelligence (AI), 5G networks, and distributed digital ledger (DDL). Even though historical elements in this article are drawn from the evolution of the power industry in the United States, parallels can be found elsewhere.
The digitalization of the power grid and advancement in intelligent technologies have enabled the service provider to convert the existing electrical grid into a smart grid. The transformation of the grid will help in integrating cleaner energy technologies with energy management to improve power network efficiency. Internet of things (IoT) and various network components need to be deployed to harness the full potential of the smart grid. Also, integrating intermittent renewable energy sources, energy storage, intelligent control of selected power-intensive loads, etc will improve energy efficiency. But deployment of this information and communication technologies will make the grid more vulnerable to cyber attacks from hackers. In this work, blockchain-based self-sovereign identification and authentication technique is presented to avert identity theft and masquerading. The proposed approach can minimize the chances of identity-based security breaches in the smart grid. This paper provides an overview of the model of identification and authentication of IoT devices in Smart Grid based on Blockchain technology. The Blockchain based implementation of identification and authentication of devices is proposed to validate the model in the distributed electrical energy network. The model is able to authenticate the device using Blockchain in a trusted model. The system works according to plan validating the authenticity of transaction in a node in log(n) time, which justifies presented result.
In recent years, blockchain technology has been developing rapidly. More and more traditional industries are using blockchain as a platform for information storage and financial transactions, mainly because of its new characteristics of non-tamperability and decentralization compared with the traditional systems. As a representative of blockchain 2.0, Ethereum has gained popularity upon its introduction. However, because of the anonymity of blockchain, Ethereum has also attracted the attention of some unscrupulous people. Currently, millions of contracts are deployed on Ethereum, many of which are fraudulent contracts deployed by unscrupulous people for profit, and these contracts are causing huge losses to investors worldwide. Ponzi contracts are typical of these contracts, which mainly reward the funds invested by later investors to early investors, and later investors will have no gain. However, although there are some studies for identifying Ponzi contracts on Ethereum, there is some room for progress in the research. Therefore, we propose a method to detect Ponzi scheme contracts on Ethereum-CTRF. This method forms a dataset by extracting the word features and sequence features of the smart contract’s code and the features of transactions. The dataset is divided into a training set and a test set. Oversampling is performed on the training set to deal with the problem of positive and negative sample imbalance. Finally, the model is trained on the training set and tested on the test set. The experimental results show that the model has significantly improved recall compared with existing Ponzi contract detection methods.
As we all know, the behavior of stealing electric energy governance is always the difficulty and key point in the management of electric power enterprises. In recent years, as bitcoin’s value continued to climb, the theft of electricity by bitcoin mining user began to appear. In order to solve the power theft problem of bitcoin mining users, we conducted an in-depth study on the power consumption behavior of such users based on the power data analysis technology. This paper analyzes the power consumption characteristics of bitcoin mining users and uses electric data acquire system to monitor power consumption behavior. The paper makes comparative analysis on the massive data such as voltage and power of the electric energy acquisition system, analyzes and calculates the Pearson correlation coefficient between the electricity consumption of each customer and the line loss statistics of the power station by using Pearson correlation algorithm combined with the power loss of the power station, and analyzes the suspected users of stealing electric energy by taking an actual example. Through our research, we found a total of 16 bitcoin miners suspected of stealing electricity. After on-site investigation and evidence collection, we found that 10 of the users did have abnormal power consumption, and the accuracy rate reached 62.50%. Therefore, the economic benefits of this research are very significant.
Information is one of the essential assets in any business. Due to the rapid technological changes, maintaining and protecting the information of individuals and organizations has become more imperative. Blockchain is a sequence of blocks that store information as a hashed value chained together as a ledger of records. This paper proposes a blockchain-based solution to manage water and electricity services. The solution keeps track of water and electricity consumption by consumers, allows them to view and pay bills, and secures their online transactions. It functions as a shared ledger between Abu Dhabi Distribution Company, Bin Moosa & Daly water and electricity supplement store, and Fazaa discount card. In addition, the proposed solution offers transparency and full trust between the different entities, controls the blockchain network, and validates transactions. Finally, the proposed application supports the UAE sustainability initiative by reducing paper usage.
Mohammad Kamrul Hasan, Ali Alkhalifah, Shayla Islam, Nissrein Babiker Mohammed Babiker · 7 authors
The smart grid idea was implemented as a modern interpretation of the traditional power grid to find out the most efficient way to combine renewable energy and storage technologies. Throughout this way, big data and the Internet always provide a revolutionary solution for ensuring that electrical energy linked intelligent grid, also known as the energy Internet. The blockchain has some significant features, making it an applicable technology for smart grid standards to solve the security issues and trust challenges. This study will present a rigorous review of blockchain implementations with the cyber security perception and energy data protections in smart grids. As a result, we describe the major security issues of smart grid scenarios that big data and blockchain can solve. Then, we identify a variety of recent blockchain‐based research works published in various literature and discuss security concerns on smart grid systems. We also discuss numerous similar practical designs, experiments, and items that have recently been developed. Finally, we go through some of the most important research problems and possible directions for using blockchain to address smart grid security concerns.
Wei Wu, Hongjie He, Zhenyue Chu, Yi Liu · 9 authors
Nowadays, the construction of smart grids and new digital infrastructure is vigorously promoted by the government, so that the power infrastructure is gradually developing towards intelligence and digitization. However, various challenges such as the industry barrier difficult to remove, the unclear supervision policy, and unmatured technology specifications are brought into the present infrastructure construction. In order to improve the timeliness and accuracy of the power infrastructure fund payment, increase the credibility of data, and enhance the quality and effectiveness of supervision and review, a blockchain-based smart payment scheme for power infrastructure is proposed in this work. First, a smart payment business framework of power infrastructure based on blockchain technology is designed. On this basis, the overall framework of the smart payment scheme and the relationships between participants of the blockchain are analyzed. Then, the smart contract for power infrastructure fund payment based on Remix IDE is customized and the correctness and efficiency of the scheme are verified. Finally, the effectiveness and advantages of the scheme are analyzed from the perspective of reliability, security, and efficiency. The simulation result shows that the smart contract based payment process of power infrastructure funds can be optimized and simplified by the proposed scheme and the overall processing time can be reduced by more than 30%.
Felipe Benedet da Silva, Diego Fernandes Gonçalves Martins, Marco Aurélio Amaral Henriques
A blockchain é um sistema distribuído utilizado para registro de transações financeiras e de outras informações confidenciais, seguindo um mecanismo de consenso específico para garantir sua segurança. Atualmente, o Proof-of-Work é o consenso mais utilizado, mas tem limitações relacionadas a sua eficiência e consumo de energia elétrica, aumentando o interesse em outros mecanismos, como o Proof-of-Stake. Nosso grupo de pesquisa propôs anteriormente um novo consenso Proof-of-Stake que não requer comitês de validação, sendo assim mais eficiente que outros consensos. O propósito deste artigo é propor novos métodos para avaliar o novo mecanismo de consenso em cenários reais, considerando uma forte criação e disseminação de transações entre todos os nós. A avaliação também considera a criação e transmissão de blocos completamente carregados de transações, tornando a atividade da rede mais intensa e próxima de uma aplicação sob uma carga severa de trabalho.
Abdullah Musaed Alkhiari, Shailendra Mishra, Mohammed Alshehri
Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all smart meters are first registered in the blockchain, and then the blockchain generates a secret key. The blockchain verifies the user ID and the secret key during authentication matches the one authorized to access the network. The secret key is shared during transmission through secure quantum key distribution (SQKD). The second phase is the lightweight data encryption, for which we use a lightweight symmetric encryption algorithm, named Camellia. The third phase is the multi-constraint-based edge selection; the data are transmitted to the control center through the edge server, which is also authenticated by blockchain to enhance the security during the data transmission. We proposed a perfect matching algorithm for selecting the optimal edge. The fourth phase is a dual intrusion detection system which acts as a firewall used to drop irrelevant packets, and data packets are classified into normal, physical errors and attacks, which is done by Double Deep Q Network (DDQN). The last phase is optimal user privacy management. In this phase, smart meter updates and revocations are done, for which we proposed Forensic based Investigation Optimization (FBI), which improves the security of the smart grid network. The simulation is performed using network simulator NS3.26, which evaluates the performance in terms of computational complexity, accuracy, false detection, and false alarm rate. The proposed BLIDSE model effectively mitigates cyber-attacks, thereby contributing to improved security in the network.
This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction
Musse Mohamud Ahmed, Mohammad Kamrul Hasan, Muhammad Shafiq, Md Ohirul Qays · 7 authors
Utilities produce and supply products following local requirements and with the synchronizations which connect subscribers. Harmonics is a power efficiency/quality variable caused by electronic devices that domestic and industrial consumers use. The famous IEEE Standard 519 is maintained to calculate harmonic limits, which ensures power efficiency. In a standard power system, currents and voltages generate pure sine wave signals during regular operations. As harmonics influence the power system, they cause interference in the sine wave signals. So, the best practice method should be used to resolve the harmonics issue. One of the problem-solving techniques of harmonics is the measurement and reduction of harmonics detection, and it uses Fast Fourier Transform (FFT). Therefore, power output should assess in a peer-to-peer Blockchain scheme by measuring and minimizing harmonics detection. This paper uses a Shunt Active Power Filter (SHAPF). It describes the simulation analysis and reduction of harmonics detection in a peer-to-peer interconnected 3-phase power system with the help of an FFT algorithm. This research was carried out to assess the efficiency of the AC signal by collecting, processing, and evaluating power data. Using the shunt active filter, the proposed design outperformed the traditional methods for both six and twelve pulse rectifiers, achieving total harmonic distortion (THD) of only 1.42% and 0.92%, respectively.
Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.
Power system reforms worldwide have commoditized electric energy, and thus the electricity market has been developed. With this, trading of electric energy takes place in various time-domains like the day ahead, real-time, etc. These transactions take place over the counter (OTC) or Power Exchange (Px), which provide the market participants with the required platform and payment security. The transactions on OTC and Px requires a third-party platform and guarantee for contract and settlement, there incurs overhead cost. Since electric energy is a fungible commodity, it can be transacted very well with the old system like barter. Energy Banking is one such mechanism wherein one utility supplies the energy to another utility that needs it more and, in leisure, the energy can then be provided back. The requisite security of the transactions can be provided by blockchain technology. Energy banking is presently being done only on a MW quantum basis with no price tag, despite the cost being dependent on the demand-supply ratio. To ensure energy banking transactions in real-time and free from the perils of financial settlements, this article suggests the use of the Peer-to-Peer (P2P) model of blockchain technology for executing Smart Contracts mutually agreed upon by both parties and avoiding third party overhead costs. Doi: 10.28991/HIJ-2021-02-03-03 Full Text: PDF
Rohan Kumar C L, Ali M. Zain, Ali M. Zain, A V Prajwal · 5 authors
Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task.
Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)—were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.
Juan Carlos Olivares, Enrique Reyes‐Archundia, José Antonio Gutiérrez Gnecchi, Ismael Molina‐Moreno · 6 authors
The lowering cost of electricity generation technologies by renewable means has caused the role of end-users to change from being a simple consumer to a producer of their energy. This new role has been called a prosumer and has contributed to modifying electricity markets, by allowing prosumers to merchandize the energy surplus to electricity companies and other prosumers. The fundamental key to any economic transaction is trust between the parties. Recently, new technologies, such as blockchain, are transforming transaction security, and allowing decentralization of the smart power grid. This study presents the development of a transactional model of electrical energy using the infrastructure of smart metering and blockchain systems to allow the commercialization of electrical energy by prosumers. The results show that for the Mexican electricity market, they can be good alternatives by reducing subsidies and being more profitable than current rates.
A rede Bitcoin é um sucesso por permitir a transferência de criptomoeda com um baixo custo, de forma rápida, sem limites geográficos e sem a intervenção de um banco intermediador. Está sendo apontada como uma possível solução para mais de um bilhão de pessoas que não tem acesso ao sistema financeiro por causa dos altos custos. Por outro lado, a rede Biticoin é pseudo-anônima e tem sido usada para uma enorme variedade de atividades financeiras dúbias e ilegais. Este artigo investiga as atividades de lavagem de dinheiro na rede Bitcoin através de diversos mecanismos que procuram melhorar o desempenho de classificadores na análise de um conjunto de dados desbalanceado devido a uma classe minoritária com muito poucas amostras. A análise considera o conjunto de dados Elliptic com mais de 200 mil transações de Bitcoin, sendo o maior conjunto rotulado de dados publicamente disponível que existe hoje de todas as criptomoedas. Os experimentos realizados mostram a eficácia de cada estratégia na melhora da classificação das atividades de lavagem de dinheiro tais como: i) o percentual de repartição do conjunto de dados em treino e teste; ii) heurísticas de sobre-amostragem; ii) diferentes algoritmos de aprendizado de máquina; iv) algoritmo de reforço de aprendizado adaptativo e v) descoberta automática de características. Os resultados mostram um bom desempenho do algoritmo de sobre-amostragem AdaSyn e que o maior ganho em desempenho foi com o classificador floresta aleatória.