Natkamon Tovanich, Nicolas Soulié, Nicolas Heulot, Petra Isenberg
We demonstrate an interactive visualization tool to analyze Bitcoin mining pools. The tool allows analysts to see the evolution of mining pools distribution over time and relationships with external variables, i.e., Bitcoin statistics and news headlines. Moreover, we also display information about pool hopping among mining pools to help understand the internal dynamics of miners.
Trading based on distributed optimization is becoming a world-wide trend in energy markets. Without proper trading mechanism design, however, participants might act dishonestly, which further leads to potential trust crisis and even market failures. This trust issue has been overlooked in state-of-the-art trading mechanism design. Blockchain, known as the enabler of trust, is promising to address this challenge. However, current studies have not detailed how blockchain can disable dishonest participants in energy trading, and primarily give qualitative rather than quantitative analysis of blockchain value in energy trading. Here, we propose an energy trading framework by marrying blockchain and distributed optimization, where blockchain enables check and balance among participants and disables dishonesty. Our results on a multi-energy district demonstrate in a quantitative way how our proposed framework can help prevent energy market failures caused by dishonest participants.
Emanuele Viglianisi, Mariano Ceccato, Paolo Tonella
The peculiar novelty of smart contracts is a computational model where irreversible transactions are stored in a distributed persistent data storage, namely the blockchain. The technical nature of this new type of software opens to new kinds of faults, which require specific test capabilities to be revealed. In this paper we present SOCRATES, an extensible and modular framework to automatically test smart contracts. The distinctive features of SOCRATES are: (1) a collection of composable behaviours that exercise smart contracts in the blockchain; (2) it deploys a society of bots, with the purpose of detecting defects arising from multi-user interactions, which are impossible to reveal when deploying a single bot. Our empirical investigation demonstrates that SOCRATES is able expose both known and previously unknown faults in smart contracts that are actively run in the official Ethereum blockchain. Moreover, we show that a society of multiple bots is more efficient in fault exposure than a single bot alone.
Invoice financing has been third largest financing market and is still growing continuously during last few years. To meet up immediate liquidity requirements most of the companies often use Invoice Financing as a key approach. By way of Invoice Financing, companies can encash the amount from financial institutes against an unpaid invoice for their immediate needs instead of waiting for due payments from their clients. Major challenges faced in invoice financing are double spending due to lack of communication between financial institutes and information security. Implementation of Blockchain for Invoice Financing can be a major breakthrough to avoid such challenges. In this Paper, we are presenting Hyperledger Fabric based Blockchain scheme to prevent fraudulent tryouts of double spending which will also provide security to the invoice information in order to facilitate immutability. Results show that, Blockchain can be used as an effective way to provide security, transparency, decentralization and avoid double spending.
Abstract Permissioned blockchains are increasingly being used as a solution to record transactions between companies. Several use cases that leverage permissioned blockchains focus on the representation and management of real-world assets. Since the number of incompatible blockchains is quickly growing, there is an increasing need for a universal mechanism to exchange, or trade, digital assets between these isolated platforms. There currently is no universal mechanism for inter-blockchain asset exchange without a requirement for trusted authorities that coordinate the trade. We address this shortcoming and present XChange, a universal mechanism for asset exchange between permissioned blockchains. To achieve universality and to avoid trusted authorities that coordinate a trade, XChange does not provide atomic guarantees but leverages risk mitigation strategies to reduce value at stake. Our mechanism records the specifications and progression of each trade within records on a distributed log. XChange reduces the economic gains of adversaries by bounding the total amount of fraud they can commit at any time. After having committed fraud, an adversary is forced to finish its ongoing trades before it can engage in new trades. We first present a four-phased protocol that coordinates an asset exchange between two traders. We then outline how trade records can be stored on TrustChain, which is a lightweight distributed ledger specifically built for the tamper-proof storage of data elements. We implement XChange and conduct experiments. Our experiments demonstrate that XChange is capable of reducing the economic gains of adversaries by more than 99.9% when replaying a real-world trading dataset. A deployment on low-resource devices reveals that the latency added to a trade by XChange is only 493 milliseconds. Finally, our scalability evaluation shows that XChange achieves over 1’000 trades per second and that its throughput, in terms of trades per second, scales linearly with the system load.
This paper presents libtxsize, a library to estimate the size requirements of arbitrary Bitcoin transactions. To account for different use cases, the library provides estimates in bytes, virtual bytes, and weight units. In addition to all currently existing input, output, and witness types, the library also supports estimates for the anticipated Pay-to-Taproot transaction type, so that estimates can be used as input for models attempting to quantify the impact of Taproot on Bitcoin's scalability. libtxsize is based on analytic models, whose credibility is established through first-principle analysis of transaction types as well as exhaustive empirical validation. Consequently, the paper can also serve as reference for different Bitcoin data and transaction types, their semantics, and their size requirements (both from an analytic and empirical point of view).
Wenjun Fan, Jinoh Kim, Ikkyun Kim, Xiaobo Zhou · 5 authors
Bitcoin and cryptocurrency rely on peer-to-peer (P2P) networking. Incorporating intelligence on Bitcoin miners to analyze and control networking can improve the information delivery and defend against networking threats. However, applying machine learning (ML) for building intelligence introduces a challenge because miners participate in the resource-intensive distributed consensus protocol and the ML application can consume much computing resources. In this paper, we study the feasibility and the interplay between ML algorithms and mining operations. Our prototype-based experiments measure and compare the performance of the ML algorithms to evaluate the implementation overhead and efficiencies of the ML algorithms and their impacts on mining operations, i.e., the mining reduction when the ML algorithm is running in parallel.
Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.
Blockchain technology is finding its application in a wide range of areas. Due to the highly decentralized and distributed nature of blockchain technology, it is essential to understand the behaviour of a system before its actual deployment. In this paper, we introduce the design and architecture of our blockchain simulator, BlockEval, which mimics the behaviour of concurrent operations that occur in a real-life blockchain system. We have established the correctness of our simulator by comparing it with an independent model that is built from the real Bitcoin-transactions data using deep-learning techniques. Unlike existing validation procedures which do not scale to large topologies, our method is scalable and efficient in terms of time and compute-infrastructure requirements. The observations made using our simulator are found to match with the results obtained from the model trained on real Bitcoin-transactions data.
In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient Blockchain-based ID3 Decision Tree Classification (BIDTC) framework for distributed networks. The proposed BIDTC takes advantage of three techniques: blockchain-based ID3 decision tree, enhanced homomorphic encryption, and stimulation smart contract to conduct classification while effectively considering the data privacy and the value of user data. BIDTC employs the data federation scheme based on homomorphic encryption and blockchain to achieve more training data sharing without sacrificing data privacy. Meanwhile, smart contracts are integrated into BIDTC to incentivize users to share more high-quality data. Our extensive experiments have demonstrated that the proposed BIDTC significantly outperforms existing schemes in constructed consortium blockchain networks.
Ping Cao, Guo‐Qing Zhu, Qingguo Zhang, Fan Wang · 6 authors
The historical training or exam data of an athlete produced in the past sport exercise or test activities have provided a promising way to objectively and accurately evaluate the real-time sport performance of the athlete. However, the continuous generation of sport training or exam data has placed a heavy transmission and processing burden on the traditional centralized data processing paradigm (e.g., cloud platform). Considering this drawback, a decentralized blockchain-based athlete sport data transmission and utilization solution is proposed in this research work. Moreover, the available athlete sport data produced in past sport exercise or test activities is often sparse and time-related, which call for a robust and time-aware data fusion and processing solution. In this situation, HMM model is employed in this article to cope with the data sparsity and dynamics and further make accurate sports performance prediction for athletes accordingly. Finally, we design a set of experiments on a real-world dataset to validate the feasibility of our proposal in terms of effectiveness and efficiency.
Internet of Things (IoT) is expected to improve our lifestyle in a noticeable way. However, although the IoT holds a lot of chances, it contains a lot of serious risks. This leads to a focus on developing security techniques that can increase the security level of IoT. Blockchain is considered as an innovative technique for securing IoT and sharing data in a secure and tamperproof way. The blockchain is a peer-to-peer connection system that performs transactions securely by using consensus algorithms with no need for a trusted third party. Blockchain proved its applicability in securing IoT networks, and the research in this area is still enticing researchers to delve deeper and deeper. Decentralized voting is considered the fundamental principle that blockchain relies on for making the appropriate decision that would offer a proper security level for IoT. In this research a novel decentralized blockchain Weighted Majority Consensus Algorithm is proposed. The algorithm is inspired by the well-known weighted majority voting algorithm in the ensemble data mining learning approach. A java implementation of WMCA has been created for testing several scenarios with the aim of confirming the applicability of the proposed WMCA and the results were very promising.
Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms. Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment. Improved data analytics model not only provides support insights in IoT data but also fosters process productivity. Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size, and monolithic service designing techniques. This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment. The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT. To demonstrate the proposed model effectiveness, we have used the IoT fitness application as a case study. Based on the designed predictive analytic model, a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness. Moreover, the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan. Finally, the proposed model is evaluated using Hyperledger Caliper in terms of latency, throughput, and resource utilization with varying peers and orderer nodes. The experimental result shows that the proposed model is applicable for diverse resource-constrained blockchain-enabled IoT applications and extensible for several IoT scenarios.
Befekadu G. Gebraselase, Bjarne E. Helvik, Yuming Jiang
Bitcoin has become the leading cryptocurrency system, but the limit on its transaction processing capacity has resulted in increased transaction fees and delayed transaction confirmation. As such, it is pertinent to understand and probably predict how transactions are handled by Bitcoin such that a user may adapt the transaction requests and a miner may adjust the block generation strategy and/or the mining pool to join. To this aim, the present paper introduces results from an analysis of transaction handling in Bitcoin. Specifically, the analysis consists of two-part. The first part is an exploratory data analysis revealing key characteristics in Bitcoin transaction handling. The second part is a predictability analysis intended to provide insights on transaction handling such as (i) transaction confirmation time, (ii) block attributes, and (iii) who has created the block. The result shows that some models do reasonably well for (ii), but surprisingly not for (i) or (iii).
The transaction and market of bitcoin is volatile, meaning it’s uncertain because it changes frequently. There have been a number of research studies that have presented bitcoin price prediction models, but none of them have looked at the controlling variables linked with bitcoin transaction timestamps. It might be that price is not the only key criteria influencing bitcoin transactions, or the available model for bitcoin price prediction is yet to consider timestamp as a determining factor in its transaction. A better and more accurate model would be required to predict how the Timestamp influences changes of bitcoin transactions. That is why this current study utilized a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the prediction timestamp influence on Bitcoin value. Bitcoin historical datasets which are converted to a nonlinear regression into a "well-formulated" statistical problem in the manner of a ridge regression are used. Simulation analysis indicates that bitcoin digital currency’s performance variation is highly influenced by its transaction timestamp with the prediction accuracy of 96%. The contributions of this research lies with the fact that specific Bitcoin transaction events repeat themselves over and over again, meaning that the Open-Price, High-Price, Low-Price, and Close-Price of Bitcoin price over timestamp developed a pattern that was predicted by NARX with less That means those involved in the transaction of bitcoin at the wrong timestamp will certainly face the uncertainty negative effect of the bitcoin market.
Hong Su, Bing Guo, Yan Shen, Zhen Zhang · 5 authors
Smart contracts are required to be instantiated in the predeployed stage, which consumes computation resources from then on. It is a big waste in the blockchain whose nodes are composed of IoT devices, as those devices often have limited resources (such as limited power supplies or a limited number of processes to run). Meanwhile, IoT devices are heterogeneous and different smart contracts are required. If those smart contracts are instantiated previously, numerous meaningless addresses are required. In this paper, we propose to delay the instantiation of a smart contract when used and terminate it when not used, which is similar to the life cycle of a variable. Then, a new kind of variable (the wrapping variable) is used to hide details of the instantiation and the address. The smart contract is instantiated in the construction function of the wrapping variable, or even it is delayed to the time when there are requests for it. The smart contract terminates when the variable is out of its scope. Then, different instantiation methods are proposed. Finally, we perform the qualitative comparison between the proposed approach and the predeployment method, and it demonstrates that the proposed methods optimize the life cycle of the smart contract and save calculation resources.