Blockchain Papers

Limeng Zhang, Rui Zhou, Qing Liu, Jiajie Xu · 5 authors

No abstract is available for this record.

Zeinab Shahbazi, Yung-Cheol Byun

Information sharing is one of the huge topics in social media platform regarding the daily news related to events or disasters happens in nature or its human-made. The automatic urgent need identification and sharing posts and information delivery with a short response are essential tasks in this area. The key goal of this research is developing a solution for management of disasters and emergency response using social media platforms as a core component. This process focuses on text analysis techniques to improve the process of authorities in terms of emergency response and filter the information using the automatically gathered information to support the relief efforts. Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to extract real-time content related to the emergency events to comfort the fast response in a critical situation. Similarly, the blockchain framework used in this process for trust verification of the detected events and eliminating the single authority on the system. The main reason of using the integrated system is to improve the system security and transparency to avoid sharing the wrong information related to an event in social media.

Zhihua Allen-Zhao, Zhihao Hao, Guancheng Wang, Dianhui Mao · 8 authors

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users’ emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions.

Mohammed Zia

In this paper, I present B-DRIVE—a blockchain-based distributed IoT (Internet of Things) network for smart urban transportation. The network is designed to connect a large fleet of IoT devices, installed on various vehicles and roadside infrastructures, to distributed data storage centers, called as Full-Nodes, to log and disseminate sensor generated data. It connects devices from around the city to multiple Full-Nodes to log timestamped data into the blockchain. These sensors vary from GPS (Global Positioning System), air quality meter, gyrometer to speed cameras in order to facilitate efficient urban mobility. The three identified hardware layers that comprise the network are the IoT layer, Storage layer, and User layer. They consist of Moving/Static-Nodes, Full-Nodes, and Smart devices, respectively. The Moving/Static-Nodes are primarily made up of moving vehicles and road-side infrastructures, respectively, thus acting as various data sources. Whereas, Full-Nodes and Smart devices are institutions and mobile phones, acting as data handler/disseminator and navigator/data visualizer, respectively. The data, or data blocks, received by Full-Nodes get appended into Full and Running-Blockchain, meant for specific purposes. The network is designed to be free from any block mining activity. It provides open access to anonymous sensor data to end-users, especially scientists, policy-makers and entrepreneurs, to develop innovative urban transportation solutions. It is believed that a system like B-DRIVE, along with existing VANETs (Vehicular Ad-hoc NETworks), is capable of answering some of the current urban transportation issues around traffic congestion, navigation, and vehicle parking. Other applications of blockchain data could vary from user activity mapping to VGI (volunteered geographic information) data quality assessment. Two identified limitations of the presented architecture are the low processing power of current IoT devices and the lack of urban IoT infrastructure.

Noura Metawa, Mohamemd I. Alghamdi, Ibrahim M. El‐Hasnony, Mohamed Elhoseny

Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.

Rawya Mars, Amal Abid, Saoussen Cheikhrouhou, Slim Kallel

Ethereum is a blockchain-based platform that pro-vides a global computational infrastructure to run smart contracts. In order to assign a cost to smart contract and transaction execution, the Ethereum Blockchain adopts a gas-based metering approach which is designed to motivate miners to operate the network and protect it against attacks. More precisely, miners receive fees from all transactions included in the mined block in addition to the mining reward. Hence, the higher the gas price in the transactions, the higher the fee paid to the miner will be, resulting in faster selection and execution of higher priced gas transactions. Therefore, an Ethereum transaction sender is exposed to the non-trivial task of having to choose an optimal gas price, as underpaying likely results in a transaction not being picked by miners, whereas overpaying leads to superfluous costs. This paper provides recommendation approach that proposes an appropriate gas price to users. More precisely, it investigates different approaches of forecasting algorithms applied for gas price predictions for the next block in Ethereum Blockchain. The gas price is predicted using the Prophet model and the deep learning models, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). It also aims to compare these approaches with the most used gas price oracles. An evaluation of the obtained results show that the LSTM and GRU proposed models outperform Prophet model as well as the gas price oracle Geth. In this case, LSTM and GRU provide a low mean squared error (MSE) of 0,008 whereas Geth gives an MSE of 0.016 and Prophet gives an MSE of 0.014.

Jun Feng, Laurence T. Yang, Yuxiang Zhu, Nicholaus J. Gati · 5 authors

Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from various CPSS devices. Numerous prevailing GAN models depend on a tacit assumption that several cyber-physical-social data providers present a reliable source to collect training data, which is seldom the case in real CPSS. The existing GAN models also fail to consider multi-dimensional latent structure. In our work, we put forward a novel blockchain-enabled tensor-based conditional deep convolutional GAN (TCDC-GAN) model for cyber-physical-social systems. The blockchain is employed to develop a decentralized and reliable cyber-physical-social data-sharing platform between numerous cyber-physical-social data providers, such that the training data and the model are documented on a ledger that is distributed. Furthermore, a tensor-based generator and a tensor-based discriminator are well designed by employing the tensor model. The results of extensive simulation experiments show the efficacy of the proposed TCDC-GAN model. Compared with the state-of-the-art models, our model gains superior estimation performance.

Seung Gyun Yoo, Byeongtae Ahn

No abstract is available for this record.

Kinanti Dhea Larasati, Arum Handini Primandari

Since its founding in 2008, Bitcoin (financial code: BTC) has emerged as a digital currency in market cap and continues to attract investors and policymakers' attention. In recent years, BTC has high price volatility, a substantial increase in 2016, followed by a significant decline in 2018. Unlike stock markets, BTC is open for 24x7 dan has no closing period. It means everyone can trade it for any time. However, this flexibility carries investment risk. This research attempts to forecast BTC's price by considering the blockchain's information to minimize the risk. We employ Long-Short Term Memory (LSTM), the artificial Recurrent Neural Network (RNN) architecture. Its model can avoid long-term problems. The data used is BTC's price and blockchain information data from August 4, 2018, to January 21, 2020. The model with 20 neurons and 500 epochs has the smallest MSE value. Then a prediction has an accuracy rate of 91.07%.

S. Sundaresan, K. Suresh Kumar, R. Nishanth, Harold Robinson · 5 authors

No abstract is available for this record.

Ivan Malakhov, Carlo Gaetan, Andrea Marin, Sabina Rossi

No abstract is available for this record.

Limeng Zhang, Rui Zhou, Qing Liu, Jiajie Xu · 5 authors

No abstract is available for this record.

Reem K. Alkhodhairi, Shahad R. Aljalhami, Norah K. Rusayni, Jowharah F. Alshobaili · 6 authors

Currently, Bitcoin is the world’s most popular cryptocurrency. The price of Bitcoin is extremely volatile, which can be described as high-benefit and high-risk. To minimize the risk involved, a means of more accurately predicting the Bitcoin price is required. Most of the existing studies of Bitcoin prediction are based on historical (i.e., benchmark) data, without considering the real-time (i.e., live) data. To mitigate the issue of price volatility and achieve more precise outcomes, this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open, high, low, and close (OHLC)—prices. Seeking a better prediction model, the present study proposes time series-based deep learning models. In particular, two deep learning algorithms were applied, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). Using real-time data, the Bitcoin candlesticks were predicted for three intervals: the next 4 h, the next 12 h, and the next 24 h. The results showed that the best-performing model was the LSTM-based model with the 4-h interval. In particular, this model achieved a stellar performance with a mean absolute percentage error (MAPE) of 0.63, a root mean square error (RMSE) of 0.0009, a mean square error (MSE) of 9e-07, a mean absolute error (MAE) of 0.0005, and an R-squared coefficient (R2) of 0.994. With these results, the proposed prediction model has demonstrated its efficiency over the models proposed in previous studies. The findings of this study have considerable implications in the business field, as the proposed model can assist investors and traders in precisely identifying Bitcoin sales and buying opportunities.

Dinh Dung Nguyen, József Rohács, Dániel Rohács, Anita Boros

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

Yuanhang Qi, M. Shamim Hossain, Jiangtian Nie, Xuandi Li

No abstract is available for this record.

Yizhuo Zhang, Yiwei Liu, Chi‐Hua Chen

Blockchain and deep learning have been important techniques for intelligent applications. This study surveys relevant hot research topics from January 2018 to August 2020. Furthermore, five topics of blockchain and deep learning which include (1) infrastructure, (2) finance and trade, (3) transportation and logistics, (4) smart contract, and (5) information security are discussed in this study.

Pratik Ratadiya, Khushi Asawa, Omkar Nikhal

Data privacy and sharing has always been a critical issue when trying to\nbuild complex deep learning-based systems to model data. Facilitation of a\ndecentralized approach that could take benefit from data across multiple nodes\nwhile not needing to merge their data contents physically has been an area of\nactive research. In this paper, we present a solution to benefit from a\ndistributed data setup in the case of training deep learning architectures by\nmaking use of a smart contract system. Specifically, we propose a mechanism\nthat aggregates together the intermediate representations obtained from local\nANN models over a blockchain. Training of local models takes place on their\nrespective data. The intermediate representations derived from them, when\ncombined and trained together on the host node, helps to get a more accurate\nsystem. While federated learning primarily deals with the same features of data\nwhere the number of samples being distributed on multiple nodes, here we are\ndealing with the same number of samples but with their features being\ndistributed on multiple nodes. We consider the task of bank loan prediction\nwherein the personal details of an individual and their bank-specific details\nmay not be available at the same place. Our aggregation mechanism helps to\ntrain a model on such existing distributed data without having to share and\nconcatenate together the actual data values. The obtained performance, which is\nbetter than that of individual nodes, and is at par with that of a centralized\ndata setup makes a strong case for extending our technique across other\narchitectures and tasks. The solution finds its application in organizations\nthat want to train deep learning models on vertically partitioned data.\n

Zhaowei Ma, F. Richard Yu, Xiantao Jiang, Azzedine Boukerche

The extensive use of vehicles, especially with the emergency of autonomous driving, urges the improvement of traffic safety. Prevalent approaches, such as Global Positioning System (GPS), Internet of Things (IoT) system and Artificial Intelligence (AI), have demonstrated their strength in preventing road accidents, with the support of trustworthy data. However, in vehicular ad hoc networks (VANETs), data transmission and storage are unreliable due to various constraints such as limited physical resource and unsteady topology. Distributed schemes are widely applied in VANETs to enforce multifold protection on vehicular data. In particular, Blockchain has become a promising approach, as it implements the real-sense distributed solution with consensus algorithm and distributed ledger. To this end, we propose a novel system in this paper, which employs Blockchain technology to consolidate the traffic information sharing in VANETs and holds profound significance for intelligent applications. Our system focuses on sharing real-time visual traffic information at the frame level via Blockchain in VANETs. Integrity verification of frames based on their sequences and timestamps is imposed prior to the consensus in Blockchain, coupled with digital watermarking to protect the multimedia traffic data. Improved efficiency and reliability of sharing are achieved by the system dynamically adjusting transaction volume in terms of the frame type and number. With the fault tolerance and immutability of Blockchain, our proposal can solidly protect the traffic information sharing against vandalization in VANETs, and confidently escort the traffic with trustworthy safety guidance.

Abin Oommen Philip, R. A. K. Saravanaguru

Abstract Even though there is continuous improvement in road and vehicle safety, road traffic incidents have been increasing over last few decades. There is a need to reduce traffic incidents like accidents through predictive analysis and timely warnings while at the same time data related to accidents and traffic violations need to be maintained in a tamper proof storage system that can be retrieved for forensic analysis and law enforcement at a later stage. The Secure Incident and Evidence Management Framework (SIEMF) proposed in this work address these two challenges of predictive modeling for timely warning and secure evidence management for forensics analysis in case of accidents and traffic violations. The system proposes a deep learning based predictive incident modeling with blockchain and CP-ABE based access control for the incident data stored in blockchain.

Ashish Sharma, Yogesh Awasthi, Sunil Kumar Yadav

Nowadays vehicles are increasing on the road. Due to this, it is a challenge for society to manage traffic jams and road accidents all over the world. Artificial Intelligence (AI) such as Machine Learning (ML) algorithms are very helpful to improve the performance of the overall road safety management system. AI is used for many real-world applications to make any system be a smart system. The Smart Road Traffic Management System (SRTMS) easily recognizes the influence occurs for random changes on road safety. The SRTMS detects the unsafe driving patterns as well as convey the information to the respective authorities. The Internet of Things (IoT) is a boon technology to observe human activities in real-time. IoT devices or nodes are composed of sensors that are commonly utilized to identify and reply to electrical and other signals. Currently, Blockchain (BC) is the most trending technology to automate transactions, which means sharing or exchange of information between the IoT devices or nodes. BC technology facilitates for sharing of information on the network is decentralized, secure, persistent, anonymity, suitability and trustworthy manner. With consensus algorithms and smart contracts, Blockchain holds to manage communication among nodes without the involvement of a third-party or intermediary body. Simultaneously, AI has the ability to offer intelligent and decision-making machines similar to human beings' minds. This paper proposes the SRTMS model for solving the road accident, traffic jam and disseminate the information to all stakeholders. This proposed model is a combination of most trending technologies such as AI, BC, and IoT. This paper proposes the SRTMS model for solving the road accident, traffic jam and disseminate the information to all stakeholders. This proposed model is a combination of most trending technologies such as AI, BC, and IoT.

Arpit Shukla, Pronaya Bhattacharya, Sudeep Tanwar, Neeraj Kumar · 5 authors

In Internet-of-Vehicles (IoV) ecosystems, intelligent toll gates (ITGs) connect nearby metropolitan cities through smart highways. At ITGs, existing solutions integrate blockchain (BC) and deep-learning schemes to leverage trusted and responsive analytics support for connected smart vehicles (CSVs) at ITGs. BC eliminates third-party intermediaries, and secures payments between vehicle owners (VO) and governing authorities (GA). Deep-Learning, on the other hand, facilitates accurate predictions for diverse and complex urban traffic conditions. However, due to fixed toll pricing schemes based on connected smart vehicles (CSV) type, VOs suffer from variable delays at different lanes due to dynamic congestion scenarios. To address the research gaps of such a fixed pricing schemes, we propose a BC-envisioned scheme DwaRa, that operates in three phases. In the first phase, future traffic is predicted based on Markov queues to balance the congestion at different lanes at ITGs efficiently. Then, we propose a novel spatially induced-long-short term memory (SI-LSTM) model to predict current traffic and weather based on historical repositories. Second, based on inputs by the Markov model, SI-LSTM, lane type, and vehicle type, a dynamic pricing algorithm is presented to improve the quality of experience (QoE) of the VO. Finally, based on dynamic price fixation between the VO and the GA, smart contracts (SCs) are executed and transactional data is secured through BC. The proposed scheme is compared against parameters like average mean-squared error (MSE), predicted traffic, scalability, interplanetary file system (IPFS) storage, computation (CC), and communication cost (CCM). At n = 100 test samples, and arrival rate β = 80, the obtained MSE is 0.0012, with a peak average value of 0.00526. The overall CC is 45.88 milliseconds (ms) and CCM is 53 bytes that indicate the proposed scheme efficacy against conventional approaches.

Santiago Figueroa-Lorenzo, Jon Goya, Javier Añorga, Iñigo Adín · 6 authors

The European Union is moving toward the “smart” era having as one of the key topics the smart mobility. What is more, the European union (EU) is moving toward Mobility as a Service (MaaS). The key concept behind MaaS is the capability to offer both the traveler's mobility and goods' transport solutions based on travel needs. For example, unique payment methods, intermodal tickets, passenger services, freight transport services, etc. The introduction of new services implies the integration of many Internet-of-Things (IoT) sensors. At this point, security gains a key role in the railway sector. Considering an environment where sensor data are monitored from sensor events, and alarms are detected and emitted when events contain an anomaly, this document proposes the development of an alarms collection system, which ensures both traceability and privacy of these alarms. This system is based on Ethereum blockchain events-log, as an efficient storage mechanism, which guarantees that any railway entity can participate in the network, ensuring both entity security and information privacy.

Hidenori Komatsu, Osamu Kimura

No abstract is available for this record.

Vikas Hassija, Vatsal Gupta, Sahil Garg, Vinay Chamola

The exponential surge in the number of vehicles on the road has aggravated the traffic congestion problem across the globe. Several attempts have been made over the years to predict the traffic scenario accurately and consequently avoiding further congestion. Crowdsourcing has come forward as one of the most adopted methods for predicting traffic intensity using live data. However, the privacy concerns and the lack of motivation for the live users to help in the traffic prediction process have rendered existing crowdsourcing models inefficient. Towards this end, we present an advanced blockchain-based secure crowdsourcing model. Not only does our model ensure privacy preservation of the users, but by incorporating a revenue model, it also provides them with an incentive to participate in the traffic prediction process willingly. For accurate and efficient traffic jam probability estimation, our work proposes a neural network-based smart contract to be deployed onto the blockchain network. The results reveal that the proposed model is highly efficient in terms of attaining high participation and consequently obtaining highly accurate predictions.