The blockchain: State-of-the-art and research challenges

Demystifying blockchain: A critical analysis of challenges, applications and opportunities

Blockchain is considered one of the most disruptive technologies of our time. Numerous cities around the world are launching blockchain initiatives as part of the overall efforts toward shaping the urban future. However, the infancy stage of the blockchain industry leads to a severe gap between the knowledge we have and the actions urban policy makers are taking. This paper is an effort to narrow this rift. We provide a systematic literature review on concrete blockchain use cases proposed by the research community. At the macro-level, we discuss and organize use cases from 159 selected papers into nine sectors recognized as crucial for sustainable and smart urban future. At the micro-level, we identify a component-based framework and analyze the design and prototypes of blockchain systems studied in a subset of 71 papers. The high-level use case review allows us to illustrate the relationship between them and the four pillars of urban sustainability: social, economic, environmental, and governmental. The system level analysis helps us highlight interesting inconsistencies between well-known blockchain applicability decision rules and the approaches taken by the literature. We also offer two classification methodologies for blockchain use cases and elaborate on how they can be applied to stimulate cross-sector insights in the blockchain knowledge domain.

Blockchain for Cities—A Systematic Literature Review

In this paper, we propose a secure and efficient blockchain-based data trading approach for the Internet of Vehicles (IoV). First, we apply consortium blockchain technologies to ensure secure and truthful data trading, and propose a general blockchain-based data trading framework for IoV. Second, to improve the efficiency of data trading and encourage more participants to trade data, we propose an iterative double auction mechanism with the purpose of achieving social welfare maximization, in which pricing rules of buyers and sellers are designed to induce participants to submit bids and to decide the amount of traded data and its price among buyers and sellers. In particular, in our algorithm, the hidden information of individuals can be extracted gradually so that the privacy of participants in data trading can be protected well. Finally, the experimental results show the efficiency of our proposed algorithm. Moreover, the correctness of social welfare maximization, incentive compatibility, individually rationality, and weakly budget balance of our auction mechanism are verified in the experiments.

A Secure and Efficient Blockchain-Based Data Trading Approach for Internet of Vehicles

We introduce FairSwap -- an efficient protocol for fair exchange of digital goods using smart contracts. A fair exchange protocol allows a sender S to sell a digital commodity x for a fixed price p to a receiver R. The protocol is said to be secure if R only pays if he receives the correct x. Our solution guarantees fairness by relying on smart contracts executed over decentralized cryptocurrencies, where the contract takes the role of an external judge that completes the exchange in case of disagreement. While in the past there have been several proposals for building fair exchange protocols over cryptocurrencies, our solution has two distinctive features that makes it particular attractive when users deal with large commodities. These advantages are: (1) minimizing the cost for running the smart contract on the blockchain, and (2) avoiding expensive cryptographic tools such as zero-knowledge proofs. In addition to our new protocols, we provide formal security definitions for smart contract based fair exchange, and prove security of our construction. Finally, we illustrate several applications of our basic protocol and evaluate practicality of our approach via a prototype implementation for fairly selling large files over the cryptocurrency Ethereum.

/pdf/fairswap-how-to-fairly-exchange-digital-goods-a8z6543xlj.pdf

FairSwap: How to fairly exchange digital goods.

Government information resource (GIR) sharing, an important means for efficient government working, requires new technology to enhance its reliability and security. Blockchain, as an emerging technology for building decentralized applications, is gradually penetrating various fields. In this paper, we present a technical combination that consists of Blockchain structure, network sharing model and consensus algorithms. With these techniques, we design and implement a Blockchain-based GIR sharing system (BGIRSS), which is decentralized, to make the sharing procedure more efficient. The results of a series of emulational experiments show that our system is securer and more reliable than conventional sharing schemes, and can effectively promote the sharing efficiency of GIRs with lower implementation cost.

Blockchain-Based Government Information Resource Sharing

For many years, non‐repudiation in data delivery has not been satisfactorily achieved due to the difficulty of supervising delivery behavior. Existing fair protocols are still not entirely free of third parties who are usually not as trustworthy as expected. Moreover, most of them are built on complex encryption schemes, resulting in inefficiencies when handling large chunks of data and non‐exchanged data sharing. To be able to deliver data without third parties and repudiation, we propose the staged data delivery protocol (SDDP) that integrates fair data delivery into Blockchain consensus processes. SDDP confirms transactions of data delivery in a cascading manner where nodes sign on each transaction one after another. It consists of two stages, in which neither party will have a considerable advantage over the other. In this way, SDDP enables non‐repudiation of directly delivering large chunks of data through an understandable communication strategy; and the Blockchain network replaces third parties to preserve an immutable history of data delivery activities. The results of extensive evaluations show that SDDP is admissible for dealing with fair data delivery on the basis of its good qualities and performance.

Staged data delivery protocol: A blockchain‐based two‐stage protocol for non‐repudiation data delivery

Changing the store layout of a shopping mall is usually costly in terms of time, resources, and money. Balancing customer flow is obviously an economical way to rationalize the store layout without displacing stores or changing their locations. However, it has long been a big challenge for managers of shopping malls, because it is difficult to build trust among stores for the sake of regulating customer flow. This trust depends on a multi-party cooperation model, of which the agreements are implemented on asymmetric information. Unfortunately, any form of endorsement with human intervention cannot support building trust on asymmetric information. To solve this problem technically, this paper proposes a diversion-point system to dynamically divert part of customer flow from popular stores to less popular ones. The system operates diversion-points and -vouchers on an asymmetric basis. It also employs a Blockchain subsystem to replace the centralized endorsement and preserve the information asymmetry, thereby building trust into the cooperation among customers, the shopping mall, and the stores therein. The evaluation shows that the proposed system is effective in remedying imperfect store layout of the shopping mall.

https://www.mdpi.com/2073-8994/12/12/1946/pdf

Blockchain-Based Diversion-Point System for Balancing Customer Flow in Shopping Mall

In board games, game-logs record past game processes, which can be regarded as an accumulation of experience. Similar to a real person, a computer player can gradually increase its skill by learning from game-logs. Therefore, the game becomes more interesting. This paper proposes an extensible approach to mine experiential patterns from increasing game-logs. The computer player improves its strategies by utilizing these growing patterns, just as it acquires experience. To evaluate the effect and performance of the approach, we designed a sample board game as a test platform and elaborated an experiment consisting of a series of tests. Experimental results show that our approach is effective and efficient.

/pdf/mining-experiential-patterns-from-game-logs-of-board-game-3xzd62bj43.pdf

Mining experiential patterns from game-logs of board game

This paper presents a new method for the mining the hottest topics on Chinese webpage which is based on the improved k-means partitioning algorithm. The dictionary applied to word segmentation is reduced by deleting words is which are useless for clustering, and the dictionary tree is created to be applied to word segmentation. Then the speed of word segmentation is improved. Correspondence between words and integers is created by coding words. Then the title is expressed by integer set, and the cost of space and time for clustering is decreased largely. Determining the value of k is a shortcoming of stream data mining based on k-means. By this new method, the value of k is adjusted in clustering. Then both the accuracy and the speed are improved.

Mining the hottest topics on Chinese webpage based on the improved k-means partitioning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Liang Wang

Author Tools

Chat about Author