Kiseok Jeon, Junghee Lee, Bumsoo Kim, James J. Kim
As the value of Bitcoin increases, the difficulty level of mining keeps increasing. This is generally addressed with application-specific integrated circuits (ASIC), but block candidates are still created by the software. The overhead of block candidate generation is relatively growing because the hash computation is boosted by ASIC. Additionally, it is getting harder to find the target nonce; If it is not found for a block candidate, a new block candidate must be generated. A new candidate can be generated to reduce the overhead of block candidate generation by modifying the coinbase without selecting and verifying transactions again. To this end, we propose a hardware accelerator for generating Merkle trees efficiently. The hash computation for Merkle tree generation is conducted with ASIC to reduce the overhead of block candidate generation, and the tree with only the modified coinbase is rapidly regenerated by reusing the intermediate results of the previously generated tree. Our simulation results demonstrate that the execution time can be reduced by up to 98.92% and power consumption by up to 99.73% when the number of transactions in a tree is 2048.
An Electronic Health Record System (EHR) is a systematic collection of electronically-stored health records and information of a patient and population in a digital format. It is typically shared among healthcare stakeholders. While a lot of hospitals in developing nations still lack the facilities that are required to shift to an EHR system, its adoption has been on the rise globally. The traditional EHR systems however face a lot of problems. These problems are majorly related to privacy, security, and ease of use. The records are entirely controlled by the hospitals that have created them. To overcome such a system, the use of a decentralized system to store data is required. The project proposes a system built on top of a Blockchain Network which allows hospitals and patients across the world to be connected to each other. Blockchain has been an emerging technology for a few years now. The key features of blockchain which attract most developers are its immutability (data cannot be changed), decentralization (data is not centrally stored), transparency (data is uniform whenever it is accessed), and distributed ledger (data distributed over various computing machines).
Abstract Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.
Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a “fee” besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models.
Luka Jovanović, Ivana Strumberger, Nebojša Bačanin, Miodrag Živković · 6 authors
Machine learning as a subset of artificial intelligence presents a promising set of algorithms for tackling increasingly complex challenges. A notable ability of this subgroup of algorithms to tackle tasks without explicit programming coupled with the expanding availability of computational resources and information transparency has made it possible to utilize algorithms to forecast prices. In recent years, cryptocurrency has increased in popularity and has seen wider adoption as a payment method. Cryptocurrency trading and mining have become a potentially very lucrative venture. However, due to the instability of cryptocurrency prices, casting accurate predictions can be quite challenging. A novel way of approaching this challenge is by tackling it through time-series forecasting. A particularly promising method for tackling this type of problem is through the utilization of long-short-term memory artificial neural networks to attain accurate prediction results. However, the forecasting accuracy of machine learning models is highly dependent on adequate hyperparameter settings. Thus, this work presents an improved variation of the arithmetic optimization algorithm, tasked with selecting the best values of a long-short term neural network casting price predictions. The presented approach has been evaluated on publicly available real-world Ethereum trading price data. The attained results of a comparative analysis against several popular metaheuristics indicate that the presented method achieved excellent results, and outperformed aforementioned algorithms in one and four-step ahead predictions.
Roi Bar-Zur, Ameer Abu-Hanna, Ittay Eyal, Aviv Tamar
The security of proof-of-work blockchain protocols critically relies on incentives. Their operators, called miners, receive rewards for creating blocks containing user-generated transactions. Each block rewards its creator with newly minted tokens and with transaction fees paid by the users. The protocol stability is violated if any of the miners surpasses a threshold ratio of the computational power; she is then motivated to deviate with selfish mining and increase her rewards.Previous analyses of selfish mining strategies assumed constant rewards. But with statistics from operational systems, we show that there are occasional whales – blocks with exceptional rewards. Modeling this behavior implies a state-space that grows exponentially with the parameters, becoming prohibitively large for existing analysis tools.We present the WeRLman1framework to analyze such models. WeRLman uses deep Reinforcement Learning (RL), inspired by the state-of-the-art AlphaGo Zero algorithm. Directly extending AlphaGo Zero to a stochastic model leads to high sampling noise, which is detrimental to the learning process. Therefore, WeRLman employs novel variance reduction techniques by exploiting the recurrent nature of the system and prior knowledge of transition probabilities. Evaluating WeRLman against models we can accurately solve demonstrates it achieves unprecedented accuracy in deep RL for blockchain.We use WeRLman to analyze the incentives of a rational miner in various settings and upper-bound the security threshold of Bitcoin-like blockchains. We show, for the first time, a negative relationship between fee variability and the security threshold. The previously known bound, with constant rewards, stands at 0.25 [2]. We show that considering whale transactions reduces this threshold considerably. In particular, with Bitcoin historical fees and its future minting policy, its threshold for deviation will drop to 0.2 in 10 years, 0.17 in 20 years, and to 0.12 in 30 years. With recent fees from the Ethereum smart-contract platform, the threshold drops to 0.17. These are below the common sizes of large miners [3].
Seng Kuang Yap, Zhongli Dong, Mark Toohey, Young Choon Lee · 5 authors
Blockchain technology has attracted significant industry, academic, and governmental attention since its emerged in 2008. Blockchain use cases are now being explored by traditional, transaction-oriented businesses in the finance, insurance, logistics and healthcare sectors to name a few. This has expanded further with the widespread use of Internet of Things (IoT) devices. Massive amounts of data are generated by IoT devices and are recorded in the blockchain. While blockchain provides many advantages, such as immutability and transparency, its serialized nature makes impossible to read in a single query. Multiple requests are required even for simple tasks, such as displaying an account's transaction history. This further leads to the difficulty in understanding the data in the blockchain. In this paper, we address the problem of smart contract visualization in a real-time manner. To this end, we design a visualization dashboard for smart contracts. A visual aid for massive amounts of data helps users understand the blockchain's overall activities, uncover operational risks and provide critical intelligence by visualising unusual activities and connections. Such insights may enable the user to investigate and predict any anomalies or reveal any network vulnerabilities. Cattle farm selected as a use case because the voluminous data can be acquired from IoT sensors on the farm cattle. Our dashboard has been proven to help visualize the life cycle of animals, the distribution of activities and time factor analysis. This visualization can give a user a better perspective of the token functions and results as well as animal management issues.
Zhongming Yao, Junchang Xin, Kun Hao, Zhiqiong Wang · 5 authors
Blockchain has become increasingly popular for data management in recent years. However, the existing blockchain systems lack efficient semantic queries, particularly keyword queries. To address this issue, we propose a learned-index-based semantic keyword query architecture on blockchain. First, our architecture records data semantics information to support semantic keyword queries. Second, we establish the lookup table index for semantic information among blocks and the block-level recursive model index for blocks to improve the query efficiency. We store the lookup table in the extended block headers to maintain the result’s completeness, and we store recursive model indexes off chain to optimize the maintenance efficiency. Third, we propose a verifiable query algorithm based on our proposed architecture to maintain the result’s correctness. Finally, the experimental results show that combining the lookup table and the learned index effectively improves the query efficiency on blockchain.
Manika Manwal, Ahmad Murtaza Alvi, Nagendra Kumar Turaga, Amit Mittal · 6 authors
People have conducted decentralized transfer transactions through Bitcoin addresses ever since the Bitcoin system launched online, drastically improving the convenience of transactions. Simultaneously, peer-to-peer transaction logs have become a subject of study. However, it requires a significant amount of time and computing capacity to examine the entire network directly, and it is not conducive to observing the transaction mode within the entity. Consequently, it is possible to construct and analyses the transaction network based on the entity service community and further investigate entity behavior and Bitcoin entities within the service community. A central node-based label propagation algorithm is proposed by enhancing the conventional label propagation algorithm, which divides the Bitcoin entity transaction network into districts and analyses the core communities, including exchanges and mining pools. The transaction method is realistic and straightforward to comprehend. The enhanced label propagation algorithm can converge more quickly and reduce the randomness of the results of community division. The experimental results demonstrate that the internal transaction modalities of various services vary. The simplified display makes the Bitcoin transaction network more readable.
Abstract In blockchain networks, topology discovery is a prerequisite when investigating the network characteristics (e.g., performance and robustness), which can provide a deeper comprehension of the behavior of the nodes and topology dynamicity. In this paper, we conduct a longitudinal study on the Bitcoin topology by collecting network snapshots from 2018 to 2022 with Node‐Probe, our topology discovery technique that uses recursive scanning to find all reachable nodes in the Bitcoin network. Using Node‐Probe, we have collected 5‐week‐long snapshots (36‐day‐long snapshots) of the Bitcoin main network and analyzed the network properties, community structure, and topology dynamicity. We confirm that our approach achieves a precision of 99% with a recall of 98% in inferring the topology. Analytical results on community structure show that the Bitcoin network has more communities than what should be expected from a random network. Meanwhile, analytical results on dynamicity indicate that the topology stands firmly on heavy and long‐running nodes. Improving the propagation mechanism using master nodes could improve the propagation delay by proximity compared with the Bitcoin default protocol. Considering a K‐anonymity attack, any transaction from one of the autonomous systems containing only a single Bitcoin node can easily be linked to real users' IP information.
Sireejaa Uppal, Bindiya Kansekar, S. Mini, Deepak K. Tosh
The Healthcare industry demands increased privacy and security to protect confidential patient information and comply with regulations. Both these features can be incorporated into the existing systems using Blockchain technology. The only challenge faced here is the ease of users, but this can be quickly resolved by integrating the Internet of Things (IoT) and blockchain. IoT-based devices overcome limited computing capacity for personal intelligent health devices. Cloud-assisted IoT devices also require limited storage capacity for devices like wearable sensors. However, it must be considered that this system still has drawbacks, leading to its inefficiency. These problems include Data Privacy and Data sharing. This paper proposes an Interplanetary File System (IPFS) based solution to these problems. Here, the users continually upload the health data collected by IoT devices and add them to blockchain transactions that the other user nodes, such as physicians, pharmacists, insurance companies, hospital authorities, etc., can access. This system ensures the well-being of the users by monitoring the data gathered every 5 min and daily. It also facilitates the alarm feature in an emergency, making it reliable. The user receives daily notifications regarding his lifestyle, and the family members receive the notifications on his behalf if there are some chances of an emergency. The authorized doctors are also notified immediately in case an emergency is detected. Apart from this feature, the user can get consultations from doctors, prescriptions from the pharmacist, funds from insurance authorities, and hospital supplies, all through the transaction on the six blockchains of HealthDote using the cryptocurrency DoteCoins, which are designed specifically for this system.
Haoran Zhu, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić · 6 authors
Bitcoin is the largest Proof-of-Work (PoW) public blockchain but is vulnerable to various attacks like stubborn mining attack, which greatly downgrades both system throughput and benefits malicious miners (attackers). The existing works assume miners receive new blocks immediately after block generation, which is away from reality. This article aims to quantify the stubborn mining attack severity in an imperfect Bitcoin network in which there exists block receiving delay. In this article, we first develop an analytic model to capture blockchain dynamics, and then derive formulas of both relative revenue and system throughput, which are applied to study attack severity. Experiment results validate our quantitative analysis method and show that imperfect networks favor attackers. Moreover, the results recommend a blockchain system to be composed of small mining pools to get fair revenue distribution, and minimize its network delay and fork probability to get high TPS.
As coin-based rewards dwindle, transaction fees play an important role as mining incentives in Bitcoin. In this paper, we propose a novel mechanism called Efficient Dynamic Transaction Storage (EDTS) for dynamically allocating transactions among blocks to achieve efficient storage utilization. By leveraging a combination of Cuckoo Filter and Dynamic Transaction Storage (DTS) strategies, EDTS is able to improve the scalability while remaining sustainable even after the Bitcoin enters a transaction-fee regime. In addition to preventing deviant mining behaviors under the transaction-fee regime, EDTS can also provide differentiated transmission priorities based on transaction fees while allowing the investors to engage in pledging more transaction fees. In EDTS, we applied the multi-objective optimization algorithm U-NSGA-III to find the best DTS strategy and its corresponding attributes. Experimental results show that the EDTS mechanism together with the optimized DTS strategy can achieve a throughput of 325.3 TPS. The experimental results reveal that the scalability improvement of EDTS is superior to the performance of Bitcoin NG, which is the best known on-chain scaling solution, while maintaining the sustainability under the transaction-fee regime.
Prof. M. S. Kale, Ayush Gimekar, Zuveriya Tamboli, Vaishnavi Patil · 5 authors
Normal cash has developed and appears numerous downsides such as inaccessibility. It is inclined to burglary and is intensely directed by government offices. Cryptocurrencies have risen as a egotistic money related framework. They depend upon secure disseminated ledger data structure. Mining plays a critical portion in this framework. Basically, our cryptocurrency could be a conveyed database that keeps up tamper-proof information structure pieces containing his bunches of person exchanges. Blockchain innovation can be a widely emerging approach to data innovations. Bitcoin as a cryptocurrency has made several considerations since it was one of its earliest implementations. They discuss the key elements driving the development of sophisticated cryptocurrencies alongside Ethereum, a blockchain implementation with a focus on informed contracts. In its most basic form, our cryptocurrency may be thought of as a distributed database that keeps track of tamper-proof data structure blocks comprising batches of individual transactions.
Blockchains are significantly easing trade finance, with billions of dollars worth of assets being transacted daily. However, analyzing these networks remains challenging due to the sheer volume and complexity of the data. We introduce a method named InnerCore that detects market manipulators within blockchain-based networks and offers a sentiment indicator for these networks. This is achieved through data depth-based core decomposition and centered motif discovery, ensuring scalability. InnerCore is a computationally efficient, unsupervised approach suitable for analyzing large temporal graphs. We demonstrate its effectiveness by analyzing and detecting three recent real-world incidents from our datasets: the catastrophic collapse of LunaTerra, the Proof-of-Stake switch of Ethereum, and the temporary peg loss of USDC - while also verifying our results against external ground truth. Our experiments show that InnerCore can match the qualified analysis accurately without human involvement, automating blockchain analysis in a scalable manner, while being more effective and efficient than baselines and state-of-the-art attributed change detection approach in dynamic graphs.
K. Ramani, M. Jahnavi, P. Jagadeesh Reddy, P. VenkataChakravarthi · 6 authors
The most popular cryptocurrency in the world is Bitcoin, which enables users to perform secure online transactions. When carrying out quick transactions, including cash transactions, this aids in keeping your money secret. Most of Consumers have been interested in the Bitcoin ecosystem in recent years. Predicting the bitcoin price accurately is a difficult task due to its high volatility. In this paper, we used deep learning and machine learning algorithms namely Long Short-Term Memory, Autoregressive Integrated Moving Average , XGBoost, Prophet and Sentiment analysis were performed on bitcoin data.The algorithms were trained on live streaming finanacial data, and results are compared based on predicted metrics like Root mean Square Error,Mean Absolute Error and R2. The results show that Sentiment analysis combined with LSTM provide better performance in bitcoin price prediction of all other algorithms.
Yingjie Zhu, Jiageng Ma, Fangqing Gu, Jie Wang · 10 authors
Bitcoin is one of the most successful cryptocurrencies, and research on price predictions is receiving more attention. To predict Bitcoin price fluctuations better and more effectively, it is necessary to establish a more abundant index system and prediction model with a better prediction effect. In this study, a combined prediction model with twin support vector regression was used as the main model. Twenty-seven factors related to Bitcoin prices were collected. Some of the factors that have the greatest impact on Bitcoin prices were selected by using the XGBoost algorithm and random forest algorithm. The combined prediction model with support vector regression (SVR), least-squares support vector regression (LSSVR), and twin support vector regression (TWSVR) was used to predict the Bitcoin price. Since the model’s hyperparameters have a great impact on prediction accuracy and algorithm performance, we used the whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) to optimize the hyperparameters of the model. The experimental results show that the combined model, XGBoost-WOA-TWSVR, has the best prediction effect, and the EVS score of this model is significantly better than that of the traditional statistical model. In addition, our study verifies that twin support vector regression has advantages in both prediction effect and computation speed.
This work aims to conduct an investigation on 2-, 5- and 10 –output-step with 5 fixed input-step Bitcoin price prediction, using gated recurrent unit (GRU) and long short-term memory (LSTM). The effects of using 2 and 6 layers with regard to LSTM and GRU (2L- and 6L- LSTM and GRU) on the prediction performance are also examined. Two datasets with statistically distinct features, e.g., rather monotonic and non-monotonic, consecutively referred to Binance and Poloniex, the world's leading crypto trading and cryptocurrency exchange platforms are experimented for intensifying the investigation. Prediction performance evaluations include root mean square error (RMSE) and mean absolute error (MAE) along with Pearson correlation coefficient (Corr) are employed here. The best averaged results of all the measures are generated by 2L-GRU. 0.9873, 0.9777 and 0.9593 Corr means are generated by 2-, 5- and 10- output-step; and 0.9758, 0.9575 and 0.9259 Corr means are resulted by the same numbers of steps, respectively for Binance and Poloniex. Overall prediction performance based on more-simpler, monotonic Binance data is rather better than more – complicate, non-monotonic Poloniex data.
Stale blocks are not avoidable in blockchain, such as the Bitcoin network, when proof-of-work is used as the consensus protocol. However, as the economic loss to the miners and the security risk to the network cannot be ignored, research is needed to identify and analyse stale blocks. By analysing the factors influencing the generation of stale blocks, the authors propose a new machine learning model based on XGBoost. They propose a new data collection method for bitcoin nodes to obtain real data for training prediction model. Then, based on the model, they generate optimal mining strategies and analyse the economic benefits. The experimental data and application cases show that the real-time data detection and machine learning model that they propose can accurately identify and predict the generation of stale blocks and generate an economically optimal mining strategy in the Bitcoin network with the presence of stale blocks.