Mengge Li, Miao Ma, Liang Wang, Bo Yang · 6 authors
With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the intelligent transportation system (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the abovementioned issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then, we propose reinforcement learning-based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.
Lingling Lu, Zhenyu Wen, Ye Yuan, Binru Dai · 10 authors
Blockchain, a distributed and shared ledger, provides a credible and transparent solution to increase application auditability by querying the immutable records written in the ledger. Unfortunately, existing query APIs offered by the blockchain are inflexible and unscalable. Some studies propose off-chain solutions to provide more flexible and scalable query services. However, the query service providers (SPs) may deliver fake results without executing the real computation tasks and collude to cheat users. In this article, we propose a novel intelligent blockchain analytics platform termediQuery, in which we design a game theory based smart contract to ensure the trustworthiness of the query results at a reasonable monetary cost. Furthermore, the contract introduces the second opinion game that employs a randomized SP selection approach coupled with non-ordered asynchronous querying primitive to prevent collusion. We achieve a fixed price equilibrium, destroy the economic foundation of collusion, and can incentivize all rational SPs to act diligently with proper financial rewards. In particular,iQuerycan flexibly support semantic and analytical queries for generic consortium or public blockchains, achieving query scalability to massive blockchain data. Extensive experimental evaluations show thatiQueryis significantly faster than state-of-the-art systems. Specifically, in terms of the conditional, analytical, and multi-origin query semantics,iQueryis 2 ×, 7 ×, and 1.5 × faster than advanced blockchain and blockchain databases. Meanwhile, to guarantee 100% trustworthiness, only two copies of query results need to be verified iniQuery, whileiQuery's latency is$2 \sim 134$× smaller than the state-of-the-art systems.
Abdallah Al-Zoubi, Tariq Saadeddin, Mamoun Aldmour, Luma Adi
An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in Solidity, to execute and manage data generated by IoT sensors and devices such as Raspberry Pi 4 and add-on hardware security modules. The proposed system runs a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem mastered by blockchain where a number of distributed IoT devices communicate and interact, thus forming a closed controlled environment. A prototype with four IoT handling units distributed over a wide geographical space was deployed to examine its feasibility, performance, and costs. Initial results indicate that big IoT data retrieval, storage is feasible, and interactivity is possible when meeting certain conditions of speed, cost and throughput.
The Device-as-a-service (DaaS) Internet of Things (IoT) business model enables distributed IoT devices to sell collected data to other devices, paving the way for machine-to-machine (M2M) economy applications. Cryptocurrencies are widely used by various IoT devices to undertake the main settlement and payment task in the M2M economy. However, the cryptocurrency market, which lacks effective supervision, has fluctuated wildly in the past few years. These fluctuations are breeding grounds for arbitrage in IoT data trading. Therefore, a practical cryptocurrency market supervision framework is very imperative in the process of IoT data trading to ensure that the trading is completed safely and fairly. The difficulty stems from how to combine these unlabeled daily trading data with supervision strategies to punish abnormal users, who disrupt the data trading market in IoT. In this article, we propose a closed-loop hybrid supervision framework based on the unsupervised anomaly detection to solve this problem. The core is to design the multi-modal unsupervised anomaly detection methods on trading prices to identify malicious users. We then design a dedicated control strategy with three levels to defend against various abnormal behaviors, according to the detection results. Furthermore, to guarantee the reliability of this framework, we evaluate the detection rate, accuracy, precision, and time consumption of single-modal and multi-modal detection methods and the contrast algorithm Adaptive KDE [ 19 ]. Finally, an effective prototype framework for supervising is established. The extensive evaluations prove that our supervision framework greatly reduces IoT data trading risks and losses.
As Bitcoin grows, more and more users are choosing to use Bitcoin as a means of transaction. For regulatory purposes, the need for retrieval and analysis of large amounts of bitcoin transaction data is raised. Faced with the problems of high retrieval time and cost overhead of current data retrieval tools, this paper proposes a new regulatory-oriented Bitcoin transaction data organization optimization scheme (BTDM). Firstly, BTDM reduces the index retrieval time by introducing Redis, then optimizes the data retrieval efficiency by using file offset mapping method, and finally reduces the cost of searching by transforming the data structure of transaction hash and bit mapping method. The experimental results show that BTDM has significant advantages in terms of performance and cost compared to other retrieval tools.
Since bitcoin has gained recognition as a valuable asset, researchers have begun to use machine learning to predict bitcoin price. However, because of the impractical cost of hyperparameter optimization, it is greatly challenging to make accurate predictions. In this paper, we analyze the prediction performance trends under various hyperparameter configurations to help them identify the optimal hyperparameter combination with little effort. We employ two datasets which have different time periods with the same bitcoin price to analyze the prediction performance based on the similarity between the data used for learning and future data. With them, we measure the loss rates between predicted values and real price by adjusting the values of three representative hyperparameters. Through the analysis, we show that distinct hyperparameter configurations are needed for a high prediction accuracy according to the similarity between the data used for learning and the future data. Based on the result, we propose a direction for the hyperparameter optimization of the bitcoin price prediction showing a high accuracy.
Raul Cristian Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Härdle
Living in the Information Age, the power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, $p$-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar (quantinar.com), to support quantitative analytics knowledge paired with code in the form of Quantlets (quantlet.com) or software snippets. The integration of blockchain technology makes Quantinar a decentralized autonomous organization (DAO) that ensures fully transparent and reproducible scientific research.
Sukrutha L. T. Vangipuram, Saraju P. Mohanty, Elias Kougianos, Chittaranjan Ray
Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.
The cryptocurrency, ether (ETH), often sees its price go through rapid fluctuations due to its popularity for being the foundation for various decentralized applications and the fuel of the Ethereum network which has simplified commerce and trade between both anonymous and recognized parties, unfavourable circumstances like political conflicts, natural disasters, and so on, causing the market to become extremely volatile and risky for the crypto investors and the developers. So, from the urge to have a specialized ether price forecasting system, this research aimed to find an accurate price prediction model for ether using the long short-term memory (LSTM) network. For this, ether time-series price data were fitted into multiple basic and hybrid LSTM network variants, with future prices predicted using both univariate and multivariate time-series analysis. Furthermore, a comparative analysis was conducted among the models and also some popular existing forecasting techniques like autoregressive integrated moving average (ARIMA) as the baseline forecast to understand the effectiveness of the LSTM networks, especially, the hybrid variant, in the prediction of future market behaviour.
Incorporation of Blockchain technology into our real-life scenarios serves in many aspects to improvise their services. Blockchain is a node-to-node decentralized distributed ledger system which provides clarity and unchangeable records of any digital assets while avoiding the intervention of a middleman. It is a new and revolutionary technology that is gaining a lot of attention because of its ability to eliminate threats and frauds on a large scale. The complete decentralized nature of blockchain has certain drawbacks wherein a restricted decentralized network can be accomplished in order to direct the overall process. The centralized server will be maintained along with its basic distributed nature with an authorized controller or miner. Every transaction will be stored in all the nodes and one node will be considered as a chief node wherein certain authenticating powers are provided to monitor and direct the operation in a successful manner. This process eliminates the involvement of multiple parties called miners to mine the block which reduces certain complications like time consumption for choosing miners, finding a fault node in case of breakdown and so on. Certain procedures are implemented to prove the presence of the records along with a process which encourages synergistic decision making for users using the Proof of Familiarity and Existence algorithm. Also, an approach of utilizing the nodes of any sector will be considered for smooth maintenance of an entire data in a blockchain platform. An approach of collaborating Proof of Familiarity and Proof of Existence will be carried out to enrich the decision-making system in both private and public sectors.
Bitcoin network is one of the most popular blockchain systems. Mining pools are the main components of the Bitcoin network that invest a large amount of computing power to maximize their expected mining payoffs, which guarantees the security of the Bitcoin network. Although many existing works about mining pools are available, the long-term evolution of mining pools, and their effects on both the Bitcoin system and end-users, remain to be investigated. To fill this gap, we trace over 2.54 hundred thousand blocks from Feb 2016 to Nov 2020 and collect over 12 million unconfirmed transactions from Mar 2018 to Nov 2020. We then conduct a broad range of analyses, including the pool evolution, labeled transactions, and labeled blocks. We make the following observations from our measured data: 1) A few mining pools control most of the peer-to-peer network's computing power. 2) The long-term computing power of top mining pools grows exponentially while itscontinuous-time mining strategydecreases linearly. 3) The computing power of the Bitcoin network converges to the Nash equilibrium. We then propose game-based strategies for mining activity analysis, i.e., the best-response strategies for mining pools when the mining revenue increases or decreases sharply. Moreover, we study the transaction fee dilemma of mining pools and the transaction fee strategies for end-users. Our models and analysis can help to understand and improve the Bitcoin system quantitatively.
Gas is the internal pricing (metering system) for running a contract or in general any transaction in Ethereum. With the popularity of Ethereum, the deficiency of current Ethereum transaction pricing mechanism First Price Auctions is being amplified. The fee paid to miners is the gas used multiplied by the gas price. Hence, designing an effective and accurate gas price prediction method is of great significance for improving the efficiency, transparency and security of the Ethereum transaction mechanism. After the Ethereum “London” Hard Fork update, EIP-1559 has been proposed to change the historical gas mechanism and make transaction fees less volatile and more predictable. Therefore, we propose a machine learning based method to predict the gas price of next blocks combined with a dynamic feature exploited from mempool after the proposal of EIP-1559. Specifically, we consider the pending transactions and their gas price in the mempool and take them as a machine learning feature for the first time. Due to the update brought by EIP-1559, we refine more features than the related works. We use machine learning models combined with the mempool features for prediction. Experiments conducted on the dataset manifest that our model combined with the mempool data shows good prediction performance, especially significantly improving the two indicators MAE and RMSE. Furthermore, we analyze and discuss the challenges of our scheme and the potential profound effects brought by our work.
Bitcoin is a decentralized cryptocurrency. It is open-source; its design is public, nobody owns or controls it and everyone can take part. And Bitcoin, just like any other open-source projects, has been subject to forks.In this paper, we will consider some popular Bitcoin forks and we will examine if we can find a link between the value of the fork (i.e. its price) and the overall sentiment of the fork based on Twitter data. The forks we consider here are: Litecoin (LTC), Bitcoin Cash (BCH), Bitcoin Gold (BTG), Bitcoin Diamond (BCD), Bitcoin Private (BTCP), Bitcoin Atom (BCA), and Bitcoin SV (BSV). From the results, we can state that, for most cryptocurrencies studied here, the number of tweets follows the price trend more closely than the sentiment evolution does.The findings can have practical implications for both researchers and practitioners, indeed the results: (i) can be a stepping stone for future research on hard forks, (ii) can help practitioners identify the relevant indicators for hard forks price evolution.
In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.
For a long time predicting, studying and analyzing financial indices has been of major interest for the financial community. Recently, there has been a growing interest in the Deep-Learning community to make use of reinforcement learning which has surpassed many of the previous benchmarks in a lot of fields. Our method provides a feature rich environment for the reinforcement learning agent to work on. The aim is to provide long term profits to the user so, we took into consideration the most reliable technical indicators. We have also developed a custom indicator which would provide better insights of the Bitcoin market to the user. The Bitcoin market follows the emotions and sentiments of the traders, so another element of our trading environment is the overall daily Sentiment Score of the market on Twitter. The agent is tested for a period of 685 days which also included the volatile period of Covid-19. It has been capable of providing reliable recommendations which give an average profit of about 69%. Finally, the agent is also capable of suggesting the optimal actions to the user through a website. Users on the website can also access the visualizations of the indicators to help fortify their decisions.
Blockchain has emerged to be a pioneer fundamental technology for distributed applications. Not only it is limited to financial sector, but it also has extended in the fields of health & medicare, managing logistics of goods through effective supply chain management etc. Although there are numerous applications of blockchain, cryptocurrencies remains at the top, in terms of popularity and cryptographic security it provides in maintenance of digital assets. Miner(s) in a cryptocurrency is/are an individual/group of individuals who benefit after per-forming Proof-of-Work for validating a transaction. The top two cryptocurrencies according to market cap value are Bitcoin and Ether. Millions of transactions happen on their blockchain on a daily basis, but not all of them result in success. Some are also marked as failed/unconfirmed, even if they are less compared to the confirmed transactions. Some of the reasons for this behavior could be too many transactions present in mempool of miners or insufficient fees is provided as the incentive to the miners of the network. Though the number of transactions that go unconfirmed per day is very small compared to the ones getting confirmed, still the area of failed cryptocurrency transactions remain unexplored. This paper focuses on statistics of failed cryptocurrency transactions, some primary reasons of failure in a cryptocurrency transaction. Furthermore, it also presents existing approaches to minimize the failure of transactions.
The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.
The traditional identification method of the digital bond trading contract system is single chain identification. Due to a large number of digital bond trading users, single chain identification will affect the performance of the system in the trading process. Therefore, we aim to propose a digital bond trading contract system based on blockchain technology and study the system architecture. In this paper, the architecture of the bond trading system is optimized by using the technology of blockchain, and the software architecture is developed by using the technology of blockchain. Through the performance test of the system, the advantages of the system architecture in practical application are verified. Firstly, the overall hardware architecture of the system is designed, and the internal structure of the controller in the control layer is redeployed. Combining the Mork tree and Patricia tree, the data structure based on blockchain and the block header data structure of the transaction contract system are optimized. By analyzing the operation process of the smart contract, the process of the smart contract consensus algorithm is optimized. The system performance test results show that the performance of the designed system in system transaction throughput, transaction delay, and system security is better than the traditional system, which verifies the effectiveness and reliability of the designed system.