Blockchain Papers

Qi-kai Qu, Fujian Chen, Xiao-jian Zhou

No abstract is available for this record.

Alina Buzachis, Basilio Filocamo, Maria Fazio, Javier Alonso · 6 authors

In the last century, the automotive industry has arguably transformed society, being one of the most complex, sophisticated, and technologically advanced industries. Autonomous vehicles (AVs) are a main concept in the future of Intelligent Transportation Systems (ITS) since they provide an increase in safety and road efficiency. One of the most critical aspects of managing AVs is their behavior in proximity of intersections. Several research centers are developing algorithms to solve the intersections management, trying to avoid collisions and traffic congestion. As well as, given that many of these interactions transmit sensitive data such as identification, position, and speed of the vehicle, a high level of security and privacy insurance is a prerequisite for broad acceptation of these communication systems. In this paper, in order to address the issues those issues we propose a system that combines blockchain technology effectively to support the communication and the transaction between vehicles. The combination between FRFP and blockchain allows us to verify if all the AVs have the same ledger version (e.g the same priority list) to cross the intersection without collisions; as well as, in case of inconsistencies to establish an emergency situation to avoid any collision.

Jérémy Charlier, Radu Statem, Jean Hilger

Smart contracts are autonomous software executing predefined conditions. Two of the biggest advantages of the smart contracts are secured protocols and transaction costs reduction. On the Ethereum platform, an open-source blockchain-based platform, smart contracts implement a distributed virtual machine on the distributed ledger. To avoid denial of service attacks and monetize the services, payment transactions are executed whenever code is being executed between contracts. It is thus natural to investigate if predictive analysis is capable to forecast these interactions. We have addressed this issue and propose an innovative application of the tensor decomposition CANDECOMP/PARAFAC to the temporal link prediction of smart contracts. We introduce a new approach leveraging stochastic processes for series predictions based on the tensor decomposition that can be used for smart contracts predictive analytics.

Ferran Herraiz Faixó, Francisco-Javier Arroyo-Cañada, María Pilar López-Jurado, Ana Martínez Pérez

No abstract is available for this record.

G. Suriya Praba Devi, J. C. Miraclin Joyce Pamila

In this era of rapid growth of vehicles, the ratio of road accident increases day by day. Nowadays, Traffic incidents are persistent problems in both developed and developing countries which result in huge loss of life and property. No one in this world is ready to gaze what's happening around them. Nobody cares even when an accident occurs. This paper provides an innovative solution by developing an Accident Alert Message System using an Android Smartphone Application that can be used from the accident zone. The application uses GPS technology for location mapping and sends an alert and notification of an accident. The generated accident alert message is endorsed by the nearby registered users who also witness the accident to ensure the increased reputation of the message. Based on the endorsement of the message, the system will instantly transmit the location of the accident to the nearby emergency services. In this case, users usually lack the enthusiasm to generate or endorse alert messages because they might fear that their privacy will be breached. At the same time, users do not benefit from generating or endorsing alert messages which also makes them lack the enthusiasm or motivation to respond to messages. In order to provide a solution to resolve these issues, this paper presents a novel privacy-preserving Blockchain - Based Incentive Mechanism for Accident Alert Message System. The main objective of the paper is to encourage the users to generate and endorse accident alert messages from the accident zone without revealing the user's identity. Also, some incentives to the users are paid to the message generators and endorsers and the transactions get stored based on the Blockchain technology; hence the privacy of the user is preserved. Our proposed system ensures the reliability of alert messages without revealing the privacy of the user and is reliable and efficient in the non-fully-trusted environment.

Dinh Dung Nguyen, József Rohács

No abstract is available for this record.

Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li · 6 authors

Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this article, we present a distributed, secure, and fair deep learning framework named DeepChain to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a prototype of DeepChain and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.

International Transport Forum

This report examines how advances in data science and encoding could improve transport. It investigates three linked and rapidly changing areas: First, it discusses the deployment of blockchain and other distributed ledger-based approaches, that record transactions efficiently and in a verifiable and permanent way. Secondly, the study looks at open algorithms and other alternatives to traditional data-sharing. Finally, it reviews the development of a common data syntax for encoding mobility services.

Bert-Jan Butijn, Damian A. Tamburri, Willem‐Jan van den Heuvel

In recent years, the UK railway industry has struggled with the effects of poor integration of data across ICT systems, particularly when that data is being used across organizational boundaries. Technical progress is being made by the industry towards enabling data sharing, but an open issue remains around how the costs of gathering and maintaining pooled information can be fairly attributed across the stakeholders who draw on that shared resource. This issue is particularly significant in areas such as Remote Condition Monitoring, where the ability to analyse the network at a whole-systems level is being blocked by the business cases around the purchase of systems as silos. Blockchains are an emerging technology that have the potential to revolutionize the management of transactions in a number of industrial sectors. This chapter will address the outstanding issues around the fair attribution of costs and benefits of data sharing in the rail industry by proposing blockchains as a forth enabler of the rail data revolution, alongside ESB, ontology, and open data.

Petra Vrablecová, Anna Bou Ezzeddine, Viera Rozinajová, Slavomír Šárik · 5 authors

No abstract is available for this record.

Justin P. Jose, Vijaya Margaret, K. Uma Rao

In a Smart Grid environment the performance measure of the grid is calculated by considering the fact that how accurately and precisely a load forecasting (LF) is done. A true Load Forecasting is vital to make a current grid smarter and more reliable when it comes to its performance. Demand Response (DR) contracts is a type of program in smart grid where the customer is free to select a type of contract which is given by the utility and is one of the growing factor which affects the load forecasting results in the Smart Grid, therefore in order to do a complete evaluation of smart grid performance and to accomplish an accurate load forecasting results the different types of contracts should also be studied. The purpose of this study is to accomplish two goals. The first one is to develop a suitable model which can incorporate various factors that can affect the load forecasting results. The subsequent goal is to identify the impact of the demand response contracts on the load forecasting results. In the proposed study, Support Vector Machine-Regression (SVR) is selected as the base methodology to perform a Short — Term Load Forecasting (STLF) under smart grid environment.

Rashid Mehmood, Royston Meriton, Gary Graham, Patrick Hennelly · 5 authors

Purpose The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model. Design/methodology/approach A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. Findings This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers. Research limitations/implications The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities. Practical implications The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO 2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013). Social implications The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system. Originality/value Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

Atefeh Mashatan, Zachary Roberts

We discuss the current state of the Canadian real estate market and the impact blockchain technology could have on it. We start with a review of the current popular and scholarly literary landscape relating to blockchain technology and its real estate applications. Special focus is given to the impact the technology could have on transaction costs, transaction times and fraud deterrence in this market. Preliminary recommendations are provided in the form of blockchain based bidding and transaction systems.

Yong Yuan, Fei–Yue Wang

Blockchain, widely known as one of the disruptive technologies emerged in recent years, is experiencing rapid development and has the full potential of revolutionizing the increasingly centralized intelligent transportation systems (ITS) in applications. Blockchain can be utilized to establish a secured, trusted and decentralized autonomous ITS ecosystem, creating better usage of the legacy ITS infrastructure and resources, especially effective for crowdsourcing technology. This paper conducts a preliminary study of Blockchain-based ITS (B2ITS). We outline an ITS-oriented, seven-layer conceptual model for blockchain, and on this basis address the key research issues in B2ITS. We consider that blockchain is one of the secured and trusted architectures for building the newly developed parallel transportation management systems (PtMS) , and thereby discuss the relationship between B2ITS and PtMS. Finally, we present a case study for blockchain-based realtime ride-sharing services. In our viewpoint, B2ITS represents the future trend of ITS research and practice, and this paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.

Wei Xu, Nan Zhang

After detailed research on Chengguan Railway Line's transportation organization and its traffic control characteristic, we makes improvement for traditional CTC system and design a decentralized and autonomous CTC system named FZy-CTC system in this paper. Then the paper analyses the system software and hardware structure, discusses the key technologies as network security, stage-plan adjustment, logic train number tracking, dispatching command safety select-control, regional interlocking control, GSM-R communication and describes the system functions. Finally current system operation situation shows that FZy-CTC system accomplishes the function of remote and intellectualized control of train operation and shunting operation route with labor intensity relieved and production efficiency improved.