Schahram Dustdar, Pablo Fernández, José María García, Antonio Ruiz–Cortés
In this paper, we deal with questions related to blockchains in complex Internet of Things (IoT)-based ecosystems. Such ecosystems are typically composed of IoT devices, edge devices, cloud computing software services, as well as people, who are decision makers in scenarios such as smart cities. Many decisions related to analytics can be based on data coming from IoT sensors, software services, and people. However, they are typically based on different levels of abstraction and granularity. This poses a number of challenges when multiple blockchains are used together with smart contracts. This work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of IoT. We propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario, discussing the benefits in terms of performance and self-adaptability of our solution.
Schahram Dustdar, José María García, Pablo Fernández, Antonio Ruiz–Cortés
Smart contracts provide computation facilities to blockchains, enabling many application scenarios where automatic analysis and complex transactions can be performed. However, in situations where the flow of information is variable and there are time, quality, and/or cost constraints imposed, smart contracts do not usually offer enough performance. In this article, we describe an elasticity framework that enables dynamic adaptation of smart contracts with respect to used resources, analytics quality, and incurred costs in a transparent manner for both smart contract developers and users. We validate our framework in an Internet of Things and Smart City scenario where several analytics are computed using Elastic Smart Contracts that dynamically adapt the appropriate elasticity variables.
Aug 7, 2021·In: Awan, I., Benbernou, S., Younas, M., Aleksy, M. (eds) The International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021). Deep-BDB 2021. Lecture Notes in Networks and Systems, vol 309. Springer, Cham
On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.
Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.
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.
Despite the fact that it is publicly available, collecting and processing the full bitcoin blockchain data is not trivial. Its mere size, history, and other features indeed raise quite specific challenges, that we address in this paper. The strengths of our approach are the following: it relies on very basic and standard tools, which makes the procedure reliable and easily reproducible; it is a purely lossless procedure ensuring that we catch and preserve all existing data; it provides additional indexing that makes it easy to further process the whole data and select appropriate subsets of it. We present our procedure in details and illustrate its added value on large-scale use cases, like address clustering. We provide an implementation online, as well as the obtained dataset.
Collaborations among multiple organizations, such as financial institutions, medical centers, and retail markets in decentralized settings are crucial to providing improved service and performance. However, the underlying organizations may have little interest in sharing their local data, models, and objective functions. These requirements have created new challenges for multi-organization collaboration. In this work, we propose Gradient Assisted Learning (GAL), a new method for multiple organizations to assist each other in supervised learning tasks without sharing local data, models, and objective functions. In this framework, all participants collaboratively optimize the aggregate of local loss functions, and each participant autonomously builds its own model by iteratively fitting the gradients of the overarching objective function. We also provide asymptotic convergence analysis and practical case studies of GAL. Experimental studies demonstrate that GAL can achieve performance close to centralized learning when all data, models, and objective functions are fully disclosed.
The Bitcoin blockchain is managed by an under- lying peer-to-peer network. This network is responsible for the propagation of transactions carried out by users via the blocks (which contain the validated transactions), and to ensure consensus between the different nodes. The quality and safety of this network are therefore particularly essential. In this work, we present an open dataset on the peers composing the Bitcoin P2P Network that was made following a well defined and reproducible methodology. We also provide a first analysis of the dataset on three criteria: the number of public nodes and their client version and geographical distribution.
Jan A. Fischer, Andres Palechor, Daniele Dell’Aglio, Abraham Bernstein · 5 authors
Bitcoin is built on a blockchain, an immutable decentralised ledger that\nallows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins\nare associated with alpha-numeric addresses and are transferred via\ntransactions. Each transaction is composed of a set of input addresses\n(associated with unspent outputs received from previous transactions) and a set\nof output addresses (to which Bitcoins are transferred). Despite Bitcoin was\ndesigned with anonymity in mind, different heuristic approaches exist to detect\nwhich addresses in a specific transaction belong to the same entity. By\napplying these heuristics, we build an Address Correspondence Network: in this\nrepresentation, addresses are nodes are connected with edges if at least one\nheuristic detects them as belonging to the same entity. %addresses are nodes\nand edges are drawn between addresses detected as belonging to the same entity\nby at least one heuristic. %nodes represent addresses and edges model the\nlikelihood that two nodes belong to the same entity %In this network, connected\ncomponents represent sets of addresses controlled by the same entity. In this\npaper, we analyse for the first time the Address Correspondence Network and\nshow it is characterised by a complex topology, signalled by a broad, skewed\ndegree distribution and a power-law component size distribution. Using a\nlarge-scale dataset of addresses for which the controlling entities are known,\nwe show that a combination of external data coupled with standard community\ndetection algorithms can reliably identify entities. The complex nature of the\nAddress Correspondence Network reveals that usage patterns of individual\nentities create statistical regularities; and that these regularities can be\nleveraged to more accurately identify entities and gain a deeper understanding\nof the Bitcoin economy as a whole.\n
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.
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).
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.
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.
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.
Yaroslav Meshcheryakov, Anna Melman, Oleg Evsutin, Vladimir Morozov · 5 authors
Cyber-physical systems and the Internet of things (IoT) are becoming an integral part of the digital society. The use of IoT services improves human life in many ways. Protection against cyber threats is an utmost important prospect of IoT devices operation. Malicious activities lead to confidential data leakage and incorrect performance of devices becomes critical. Therefore, development of effective solutions that can protect both IoT devices data and data exchange networks turns in to a real challenge. This study provides a critical analysis of the feasibility of using blockchain technology to protect constrained IoT devices data, justifies the choice of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for implementation on such devices, and simulates the main distributed ledger scenarios using PBFT. In this paper we investigate typical IoT network scenarios that can disrupt system performance. To ensure the adequacy of the models under study, we have analyzed the characteristics of real constrained IoT devices in terms of computing power and data rate. The simulation results demonstrate efficiency of the blockchain technology for constrained devices and make it possible to evaluate applicability limits of the chosen consensus algorithm.
Nishant Jagannath, Tudor Barbulescu, Karam M. Sallam, Ibrahim Elgendi · 8 authors
The Ethereum blockchain generates a significant amount of data due to its intrinsic transparency and decentralized nature. It is also referred to as on-chain data and is openly accessible to the world. Moreover, the on-chain data is timestamped, integrated, and validated into an open ledger. This important blockchain feature enables us to assess the network’s health and usage. It serves as a massive data warehouse for complex prediction algorithms that can effectively detect systemic trends and forecast future behavior. We adopt a quantitative approach using a subset of these metrics to determine the network’s true monetary value by developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) with the metrics most closely associated with the price as inputs. Since several hyperparameters regulate the learning process in an RNN, they are highly sensitive to their values. It is thus critical, to select optimal hyperparameters so that the training is quick and effective. Determining the optimal parameters of an RNN model is a tedious and complex process. Hence, previous studies have developed several self-adaptive approaches to determine the optimal values for various parameters effectively. However, none of the prior studies explore self-adaptive algorithms in deep learning models in conjunction with on-chain data to predict cryptocurrency prices. In this paper, we propose three self-adaptive techniques, each of which converges on a set of optimal parameters to predict the price of Ethereum accurately. We compare our results to a traditional LSTM model. Our approach exhibits 86.94% accuracy while maintaining a minimum error rate.
In this paper, we provide detailed analyses of the Bitcoin network and its main participants. We build a novel database using a large number of public and proprietary sources to link Bitcoin addresses to real entities and develop an extensive suite of algorithms to extract information about the behavior of the main market participants. We conduct three major pieces of analysis of the Bitcoin eco-system. First, we analyze the transaction volume and network structure of the main participants on the blockchain. Second, we document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, we analyze the ownership concentration of the largest holders of Bitcoin.
Internet of Things (IoT) consists of smart Things with evolution of ubiquitous computing. Fog Computing (FC) processes and analyzes data of these sensors near to users. However, the ever-increasing in number of Things and the consequent explosion in data traffic have led to fail traditional solutions of centralized storage. Blockchain is a new technology developed as a shared ledger build around peer-to-peer network to produce unchangeable blocks that contain multiple data. These blocks are linked to previous ones in sequence called chain through hash functions. Participants in blockchain selects a leader through one of consensus algorithms who adds new blocks in the chain to prevent dishonest nodes from creating invalid blocks. In this paper, we propose IoT based blockchain architecture named blockchain of Things to store medical records in a distributed manner. The architecture is emulated on Fog server Linux-based using Node.js and Postman. Three consensus protocols, namely: Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (PBFT) are emulated and communicated using WebSocket. The consensus algorithms are evaluated in terms of CPU utilization and memory usage. The results show that PoS compared to PoW and PBFT is more lightweight and beneficial to IoT application.