Blockchain Papers

이정우, Byungyun Yang

No abstract is available for this record.

Jiaxin Li, T Yu, Yannian Wang, Yue Sun

Bitcoin’ s anonymity greatly protects users’ privacy, but it also makes regulation difficult. In Bitcoin, a random number generates a public-private key pair, the public key generates an address, and the private key is used for digital signatures. Users can generate multiple pairs of public and private keys to trade with multiple bitcoin addresses. Discovering the relationships between these addresses and clustering the addresses of individual users helps infer the identity of the addresses. By analyzing the association of addresses in UTXO , it is found that multiple input addresses of a transaction are controlled by the same user, and thus the bitcoin addresses can be clustered. The transactions between the user data obtained after clustering are communality, so the Louvain algorithm is further used to analyze the relationship between users, the visual results are used to present the association between users, and the impact of the number of users on the algorithm results is analyzed. Finally, the Leiden algorithm proposed to solve the problem that Louvain algorithm may have poor connectivity or even disconnection between communities is used to discover the community of the clustered user data. Compare the results of Leiden algorithm and Louvain algorithm and analyze the difference between the two results.

Casey R. Lynch, Àlex Muñoz-Viso

Libertarian “exit” imaginaries project new social, political, and economic structures separate from existing institutions in which “sovereign individuals” can opt-in to the governing system that fits their ideals. This paper traces libertarian exit imaginaries through a variety of territorial and technological projects. Demonstrating how these imaginaries evolve, it describes a recent proposal to build a semi-autonomous, blockchain-based smart city in Nevada. Reflecting on these projects, the paper highlights (1) their inevitable failure as they confront reality, (2) their role as spectacle, spreading libertarian ideology, and (3) their real-life impacts on distinct places and communities even when they fail or never materialize.

Minh-Duc Nguyen, Van Tong, Sami Souihi, Abdelhamid Mellouk

In the past, Quality of Service (QoS) was taken into account to evaluate the performance of multimedia services (e.g., video streaming, file transfer, etc.). However, it cannot reflect the user's perception, which is considered a crucial consideration by these services nowadays. Therefore, the emergence of Quality of Experience (QoE) is a potential solution. QoE can be measured via many parameters provided by Internet Service Providers (ISP), Application Service Providers (ASP), or end-users. However, privacy concerns hinder data sharing between the parties involved. To address these limitations, this paper proposes a QoE estimation mechanism that leverages Federated Learning. This mechanism aims to guarantee data privacy when no party needs to disclose their data to others. Moreover, the proposed mechanism incorporates the concept of a Decentralized Autonomous Organization (DAO) to mitigate the risk of a single point of failure in the centralized architecture of Federated Learning. It enables all participants to evaluate and select the model efficiently. The experimental results illustrate that the proposal surpasses the centralized solutions and guarantees data privacy.

Manjula K. Pawar, Harshitha S Hiregowdar, Sukruti Joshi

This research paper presents the design and implementation of a land registry system using multiple nodes with Geth, a command-line interface for running Ethereum nodes. The system is designed to address the issues of security and efficiency in the current land registration system. The system uses a distributed database with multiple nodes to store land records, providing redundancy and fault tolerance. The use of a distributed database also ensures that no single point of failure exists, improving the system's reliability. To ensure that the land records are impenetrable and cannot be changed without permission, the system makes use of the Ethereum blockchain. A consensus mechanism is used to implement the blockchain, ensuring that every node on the network concurs with the database's current state. The use of a consensus algorithm ensures that the system is secure and resilient to attacks. To automate the transfer of land ownership, the system also includes a smart contract framework. The smart contract system ensures that all parties involved in a land transaction agree on the terms of the transaction before it is executed. This reduces the possibility of land transaction conflicts. The system was developed using Geth, a commandline interface for managing Ethereum nodes. The system was tested using a simulated network of nodes and was found to be reliable and efficient. The system showed high throughput and low latency, which qualified it for usage in a production setting.

Bo Gu, Weiwei Hu, Shimin Gong, Zhou Su · 5 authors

Mobile crowdsensing (MCS) has emerged as a new sensing paradigm that relies on the sensing capabilities of the crowd to aggregate data. Unlike traditional MCS systems, where sensing data are traded via a third-party sensing platform, we propose a distributed data trading framework and investigate the potential of consortium blockchain to ensure the privacy and security of data transactions in MCS systems. The interactions between selling mobile users (SMUs) and buying mobile users (BMUs) are modeled as a Stackelberg game. Then, the amount of sensing time to purchase from each SMU and the price per unit sensing time are determined according to two auto-executing smart contracts. Notably, SMUs are compensated according to not only the amount of sensing time but also their reputation so that SMUs are encouraged to contribute high-quality data. Furthermore, the distributed ledger technology guarantees that the reputations of SMUs are updated and recorded in an immutable and traceable manner. Experimental results confirm that the proposed mechanism achieves near-optimal social welfare without requiring SMUs to know the price and data quality of each other.

Dawid Połap, Gautam Srivastava, Antoni Jaszcz

The rapid pace of development of the Internet of Things and the requirements of various devices have allowed us to perform calculations at the edge, especially in terms of consumer electronics. Such progress makes it possible to design new solutions for energy distribution and prediction for smart homes. In this paper, we propose a solution that can be used to optimize energy distribution by analyzing the energy demand in individual homes. The proposed methodology is based on edge technology, where a dedicated LSTM network with a multi-head self-attention network is trained with measurement data from different sensors for predicting energy demand. Training of this network is extended to a decentralized learning process with an additional aggregation decision module (that allows rejection of the model in case of worst adaptation to private data). In order to increase data security, we added a blockchain network with a Byzantine strategy and Proof of Stake (PoS) consensus. The solution was tested for a publicly available database in order to demonstrate the possibilities and advantages of such an architecture.

Lajos Kelemen, István András Seres, Ágnes Backhausz

This study, to the best of our knowledge for the first time, delves into the spatiotemporal dynamics of Bitcoin transactions, shedding light on the scaling laws governing its geographic usage. Leveraging a dataset of IP addresses and Bitcoin addresses spanning from October 2013 to December 2013, we explore the geospatial patterns unique to Bitcoin. Motivated by the needs of cryptocurrency businesses, regulatory clarity, and network science inquiries, we make several contributions. Firstly, we empirically characterize Bitcoin transactions' spatiotemporal scaling laws, providing insights into its spending behaviours. Secondly, we introduce a Markovian model that effectively approximates Bitcoin's observed spatiotemporal patterns, revealing economic connections among user groups in the Bitcoin ecosystem. Our measurements and model shed light on the inhomogeneous structure of the network: although Bitcoin is designed to be decentralized, there are significant geographical differences in the distribution of user activity, which has consequences for all participants and possible (regulatory) control over the system.

Jiali Yang, Kehua Yang, Zhu Xiao, Hongbo Jiang · 6 authors

With deepening urbanization and Internet of Vehicles (IoV) applications, the number of private cars has been increasing in recent years. However, because the surging number of private cars is not compatible with limited road resources, private car users have had unsatisfactory commute experiences during their daily travel. In this work, we focus on improving private car users’ commute experience based on an analysis of IoV trajectory data in a privacy-preserving way. Our idea is based on the following observations: 1) the commute experience of private car users is closely related to the departure time and the travel cost and 2) most travel costs are spent on urban hot zones. Motivated by these findings, we propose a novel blockchain-enabled model named Deep Improving Commute Experience (DeepICE) to improve private car users’ commute experience by predicting when to depart and when to arrive. In this model, a blockchain with a consensus mechanism is developed to address private car user privacy concerns. In addition, we propose a multitask learning-enabled graph convolution network (GCN) method to capture the highly complex features and relations between two tasks, i.e., the departure time and travel cost, and then develop the model to predict these two tasks. The experimental results demonstrate the superior performance of our proposed model compared to existing approaches. Our model can be applied to efficiently enhance private car users’ commute experience.

Natarajan Sankaran

Revolutionizing data processing, security and real-time decision making, the move to IoT edge intelligence is advancing the state of the art in how we approach these and all challenges of modern business. Latency, bandwidth constraints, security vulnerability are the traditional pain points of traditional cloud-based service models, edge computing is a critical solution. The IoT systems can be made more responsive, better able to utilize resources more effectively, and more secure by way of integrating ML driven visualization and edge AI strategies. Nevertheless, there are still some challenges about this such as scaling, data privacy, and computational efficiency. These risks can be mitigated with the solutions like federated learning, blockchain integration and then the anomaly detection, and all that data can actually flow seamlessly and securely. Edge AI takes the best of centralized cloud along with cost efficiency of distributed systems and results in reducing dependence on centralized cloud infrastructure, and optimizing data processing by doing the computation locally to lower latency and save bandwidth. Furthermore, ML based visualization tools help in making IoT applications efficient for smart cities, health-care and industrial automation domains. Though the technology was developed years ago, security continues to be a key consideration as blockchain technology ensures secure, tamper proof data management, while federated learning ensures that data is private because it is decentralized during training. It is expected that later IoT edge intelligence can be advanced further from emerging technology such as quantum computing and AI driven automation. Such advancements will enable more scalable, secure and efficient processing frameworks that would lead to making intelligent, autonomous decisioning in the real time environment. As organizations adopt the edge AI solutions, it is important to address their current limitations and exploit the future innovation for the further growth and efficiency of IoT ecosystems.

Yilun Lin, Wei Hu, Xi Chen, Shuang Li · 5 authors

The development of smart cities has been a significant trend in recent years, aiming to improve the quality of life of citizens by leveraging technology and data. However, the current models of smart cities have limitations in terms of their centralized control and lack of citizen participation. City 5.0 proposes a new paradigm for smart cities that emphasizes the symbiotic relationship between humans and technology. This article presents the concept of Spatial Symbiotic Intelligence, which refers to the ability of a city to dynamically respond to the needs of its citizens and environment through the integration of data from various sources and the use of Decentralized Autonomous Organizations (DAOs) and parallel systems. This letter also discusses the potential benefits and challenges of implementing City 5.0 and highlights some of the ongoing initiatives in this area.

Sijie Huang, Guoju Gao, He Huang, Yu-E Sun · 8 authors

Mobile crowdsensing (MC), an excellent solution to large-scale spatiotemporal data sensing problems, has recently received lots of attention from both industry and academia. In the MC system, any requester can acquire the sensing data for his points of interest (PoIs) by offering some payments to attract a group of mobile users capable of completing these PoI-related sensing tasks. However, the current MC work neglected three vital factors, more or less. First, they assume that these distributed users are mutually independent in MC, ignoring the social effects. Actually, the sensing data collected by one user may be corroborated by others’ sensing data, so-called information corroboration. Second, all rational and selfish users are inclined to gather to perform these tasks due to information corroboration. Meanwhile, they may be strategic about their participation levels to maximize profits. However, more similar sensing data will undoubtedly lower the information value, so any user has a tradeoff between gather and scatter. Third, although mobile users can obtain some payments, privacy issues may still prevent them from participating in MC. In this article, we propose a secure blockchain-assisted socially-aware MC framework by adopting the smart contract technique of Ethereum. For this framework, we further devise a two-stage Stackelberg game model to assist the requester (i.e., the leader in the game) in properly pricing each PoI-related sensing task, so that mobile users (i.e., the followers in the game) can exactly select their tasks and determine their participation levels. To analyze the game equilibrium, we extend the traditional Hessian matrix method to a multidimension case involving the multiuser multitask hyperspace setting. We conduct extensive experiments to prove the equilibrium and effectiveness of the proposed solution. We also implement a prototype and deploy the smart contract to an official Ethereum test network to demonstrate the practicability of the proposed framework.

Faiza Iqbal, Ayesha Altaf, Zeest Waris, Daniel Gavilanes Aray · 7 authors

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply-demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data's security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users' privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user's wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.

Shan Jiang, Jiannong Cao, Hanqing Wu, Kongyang Chen · 5 authors

No abstract is available for this record.

Kazi Tamzid Akhter Md Hasib, Rakibul Hasan, Mubasshir Ahmed, AKM Bahalul Haque

We are living in the age of various modern technologies and Blockchain is one of the newest among them. Smart contract are used with Blockchain as an add on which brings automation in application. Smart card are also used nowadays widely to access smart services in various domains. Since the vast majority of people nowadays do not want to carry a lot of cards with them at all times. As a result, we came up with a solution to this conundrum. A single Card, which will function as the key card for all municipal services, will serve as the core hub for all of this decentralization. Also planned is the development of a Cryptocurrency wallet, which will allow smart cities to access anything inside the Blockchain Network directly from peer to peer. There will be no intermediates who will be able to access any citizen’s information or data; yet, if an event happens, such as criminal activity, there will be no one to blame. Using blockchain technology, law enforcement will be able to follow down the perpetrators of these crimes since all timestamps will be saved on our platform. Because of this, smart cities will be less prone to criminal activity in general. We can ensure a more efficient smart city by using blockchain technology. Therefore, people will have a greater sense of security at every level of service where Card will play a vital part. Finally, we can say that a Blockchain-based Smart Card will serve as a one-stop solution for all of humanity. Consequently, we don’t have to be worried with all of the services that are offered in any certain place. Life will be far better than it has ever been in the past. Blockchain technology, which was initially introduced in 2008 and is based on cryptography, is the fundamental technology that underpins the bitcoin cryptocurrency. Initially, it was exclusively utilized by the cryptocurrency bitcoin.

Xingcheng Guo, Xianglong Guo

Blockchain technology has been widely used in finance, transportation, education, medical treatment, network security, management science, and other industries due to its characteristics of decentralization, high reliability, and traceability. Unlike other studies on Urban Intelligent Transportation Systems (UITS) in the past, we present a model framework of the Urban Intelligent Transportation Systems (UITS) applied by blockchain in developing countries. After a detailed elaboration of the situation of three representative Urban Intelligent Transportation Systems (UITS) in China, blockchain technology has been applied to build a new architecture model of the big data platform for urban intelligent transportation, as well as the design concept and conceptual model for the new generation of the Urban Intelligent Transportation Systems (UITS) in developing countries. Finally, this paper elaborates on the important direction of future development, areas, of concern, and open research challenges, which could be explored by researchers and urban intelligent transportation designers to make further advances in this field.

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.

Krati Paliwal, Siddhi Poojari, Mayur Singal

Exploring the integration of blockchain technology into land registry systems is the primary focus of this research, concentrating on augmenting efficiency, transparency, and security within the domain.Employing an extensive research framework, we rigorously investigate the functionality of blockchain in the context of land registries.Our analysis reveals substantial reductions in transaction times, bolstered data integrity, and increased resilience against fraudulent activities.These findings accentuate the pivotal role that blockchain can play in restructuring conventional land registry practices, instilling trust, and mitigating discrepancies.Beyond the immediate benefits, the study extrapolates into a forward-looking perspective, contemplating the widespread adoption and potential consequences of implementing blockchain technology in the field of land registration.Key aspects encompassed in this exploration include blockchain, land registry, efficiency enhancements, transparent data management, heightened security protocols, and reduced transaction times.It is important to note that while the study acknowledges the transformative potential of blockchain, it does not underestimate the challenges and considerations associated with its implementation.By shedding light on both the positive and potential pitfalls, this research seeks to contribute to a nuanced understanding of how blockchain technology can be leveraged effectively in the context of land registries.The outlined key terms encapsulate the essence of this investigation, providing a comprehensive overview of the multifaceted impact that blockchain integration can have on land registration systems.

Matheus Leal, Flávia Pisani, Markus Endler

Movement tracking information can reveal much about the daily operation of companies. Several applications can benefit from recording the physical places a mobile entity visits and how long it stays at each position. This paper discusses the need for an Available, Reliable, Transparent, Immutable, and Irrevocable service for Mobility Records (ARTIIMoR). We present an approach to recording spatio-temporal presence information in a reliable, immutable, and scalable way using a multilayer Distributed Ledger Technology (DLT) architecture for storing location information in variable levels of abstraction and aggregation. We implemented this multilayer solution as a middleware service that uses Complex Event Processing on smartphones to efficiently record nearby place-specific beacons in a DLT. We compare the performance of inserting data in the IOTA, Ethereum, and Hyperledger DLTs, the impact that different data aggregation techniques have on insertion performance, and the effect of the multilayer approach on data query performance.

Anthony Simonet-Boulogne, Arnor Solberg, Amir Sinaeepourfard, Dumitru Roman · 8 authors

The rapid development of Smart Cities is aided by the convergence of information and communication technologies (ICT). Data is a key component of Smart City applications as well as a serious worry. Data is the critical factor that drives the whole development life-cycle in most Smart City use-cases, according to an exhaustive examination of several Smart City use-cases. Mishandling data, on the other hand, can have severe repercussions for programs that get incorrect data and users whose privacy may be compromised. As a result, we believe that an integrated ICT solution in Smart Cities is key to achieve the highest levels of scalability, data integrity, and secrecy within and across Smart Cities. As a result, this paper discusses a variety of modern technologies for Smart Cities and proposes our integrated architecture, which connects Blockchain technologies with modern data analytic techniques (e.g., Federated Learning) and Edge/Fog computing to address the current data privacy issues in Smart Cities. Finally, we discuss and present our proposed architectural framework in detail, taking into account an online marketing campaign and an e-Health application use-cases.

Nabanita Das, Souvik Basu, Sipra Das Bit

No abstract is available for this record.

Jwalin Thaker

Abstract—This white paper explores the transformative potential of decentralized AI agents operating within the Web3 infrastructure to enhance user experiences through personalized and privacy- preserving browsing. We investigate how these AI agents can autonomously navigate the web, utilizing smart contracts for automated decision-making processes that prioritize user preferences and privacy. The paper outlines a robust technical framework that includes decentralized AI models running on blockchain networks, integration with existing Web3 protocols such as IPFS and ENS, and the implementation of privacy- preserving AI computation techniques, including zero-knowledge proofs. We present various use cases, including AI-powered decentralized search engines, autonomous content curation and recommendation systems, and smart contract-based content verification and fact- checking mechanisms. Additionally, we address the challenges associated with scalability of AI computation on blockchain, data privacy and sovereignty, token economics for AI services, and governance models for decentralized AI systems. The future impact of these developments on user interfaces in Web3, the democratization of AI services, and the emergence of new business models for decentralized AI is also discussed. This topic is particularly relevant as it addresses current limitations in Web3 user experience, combines two major technological trends, and has practical applications for both users and developers, while exploring novel economic models that contribute to the broader discussion of a decentralized internet. Keywords- Decentralized AI, Web3, Privacy, Smart Contracts, Blockchain, User Experience Index Terms—Decentralized AI, Web3, Privacy, Smart Con- tracts, Blockchain, User Experience

Huigang Chang, Yiming Liu, Zhengguo Sheng

The real-time intelligent perception and prediction of traffic situation can assist connected automated vehicles (CAVs) in path planning and reduce traffic congestion in Cognitive Internet of Vehicles (CIoVs). The centralized traffic congestion prediction solutions generally fail to adapt to the dynamic traffic environment and lead to significant communication overheads. Blockchain technology has attracted great attention in the information sharing of vehicular networks for its advantages in decentralization, transparency, traceability, and tamper-proof capability. However, due to the bottlenecks, such as high computational cost, current blockchains are incapable actuate on efficient online traffic situational cognition and prediction for CIoVs. Motivated by this, we propose a blockchain-enabled cognitive segments sharing framework for online multistep congestion duration prediction. We design a cognitive model of traffic situation based on anomaly detection and filtering mechanism to guarantee the accuracy of the cognitive segments before being packaged into the block. Furthermore, to improve the consensus efficiency, we design a credit evaluation mechanism and propose a credit-based delegated Byzantine fault tolerance (CDBFT) algorithm. Finally, we propose an online multistep prediction algorithm based on long short-term memory (LSTM) to predict future traffic congestion duration. Experimental results demonstrate that the proposed algorithms achieve shorter consensus latency and higher predictive accuracy than the existing algorithms.

Hoang H. Nguyen, Dmytro Bozhkov, Zahra Ahmadi, Nhat-Minh Nguyen · 5 authors

Social networks have become an inseparable part of human activities. Most existing social networks follow a centralized system model, which despite storing valuable information of users, arise many critical concerns such as content ownership and over-commercialization. Recently, decentralized social networks, built primarily on blockchain technology, have been proposed as a substitution to eliminate these concerns. Since decentralized architectures are mature enough to be on par with the centralized ones, decentralized social networks are becoming more and more popular. Decentralized social networks can offer both common options like writing posts and comments and more advanced options such as reward systems and voting mechanisms. They provide rich eco-systems for the influencers to interact with their followers and other users via staking systems based on cryptocurrency tokens. The vast and valuable data of the decentralized social networks open several new directions for the research community to extend human behavior knowledge. However, accessing and collecting data from these social networks is not easy because it requires strong blockchain knowledge, which is not the main focus of computer science and social science researchers. Hence, our work proposes the SoChainDB framework that facilitates obtaining data from these new social networks. To show the capacity and strength of SoChainDB, we crawl and publish Hive data - one of the largest blockchain-based social networks. We conduct extensive analyses to understand the insight of Hive data and discuss some interesting applications, e.g., game, non-fungible tokens market built upon Hive. It is worth mentioning that our framework is well-adaptable to other blockchain social networks with minimal modification. SoChainDB is publicly accessible at http://sochaindb.com and the dataset is available under the CC BY-SA 4.0 license.