Blockchain Papers

Shajulin Benedict

No abstract is available for this record.

Kang Gao, Yijun Yuan

No abstract is available for this record.

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.

Qin Hu, Zhilin Wang, Minghui Xu, Xiuzhen Cheng

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.

Zeinab Shahbazi, Yung-Cheol Byun

One of the common transportation systems in Korea is calling taxis through online applications, which is more convenient for passengers and drivers in the modern area. However, the driver's passenger taxi request can be rejected based on the driver's location and distance. Therefore, there is a need to specify driver's acceptance and rejection of the received request. The security of this system is another main core to save the transaction information and safety of passengers and drivers. In this study, the origin and destination of the Jeju island South Korea were captured from T-map and processed based on machine learning decision tree and XGBoost techniques. The blockchain framework is implemented in the Hyperledger Fabric platform. The experimental results represent the features of socio-economic. The cross-validation was accomplished. Distance is another factor for the taxi trip, which in total trip in midnight is quite shorter. This process presents the successful matching of ride-hailing taxi services with the specialty of distance, the trip request, and safety based on the total city measurement.

Zeinab Shahbazi, Yung-Cheol Byun

The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system's efficacy and performance compared with other state-of-art models.

Matthew Tsao, Kaidi Yang, Stephen Zoepf, Marco Pavone

The era of big data has brought with it a richer understanding of user behavior through massive datasets, which can help organizations optimize the quality of their services. In the context of transportation research, mobility data can provide municipal authorities (MAs) with insights on how to operate, regulate, or improve the transportation network. Mobility data, however, may contain sensitive information about end users and trade secrets of mobility providers (MPs). Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we propose an interactive protocol between an MA and an MP, in which MA obtains insights from mobility data without MP having to reveal its trade secrets or sensitive data of its users. This is accomplished in two steps: 1) a commitment step and 2) a computation step. In the first step, Merkle commitments and aggregated traffic measurements are used to generate a cryptographic commitment. In the second step, MP extracts insights from the data and sends them to MA. Using the commitment and zero-knowledge proofs, MA can certify that the information received from MP is accurate, without needing to directly inspect the mobility data. We also present a differentially private version of the protocol that is suitable for the large query regime. The protocol is verifiable for both MA and MP in the sense that dishonesty from one party can be detected by the other. The protocol can be readily extended to the more general setting with multiple MPs via secure multiparty computation.

Yulin Liu, Luyao Zhang, Yinhong Zhao

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.

Matheus Leal, Flávia Pisani, Markus Endler

Abstract Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, portable equipment, autonomous robots). This paper presents our approach to recording spatio-temporal presence information in a secure and inviolable way using a Distributed Ledger Technology. We implemented this solution as a middleware service that uses Complex Event Processing on smartphones to record beacon-smartphone proximity data in a blockchain efficiently. We have built upon the previous version of our service to include access control to the stored information. We analyzed the impact of this addition on the service’s performance and observed that it introduced very little overhead while significantly increasing user privacy. Furthermore, we compared the effect of using different blockchain technologies on overall service performance and characterized scenarios where using either IoTeX or Ethereum can be suitable for this type of application.

Angel Hsu, Willie Khoo, Nihit Goyal, Martin Wainstein

Climate change has been called "the defining challenge of our age" and yet the global community lacks adequate information to understand whether actions to address it are succeeding or failing to mitigate it. The emergence of technologies such as earth observation (EO) and Internet-of-Things (IoT) promises to provide new advances in data collection for monitoring climate change mitigation, particularly where traditional means of data exploration and analysis, such as government-led statistical census efforts, are costly and time consuming. In this review article, we examine the extent to which digital data technologies, such as EO (e.g., remote sensing satellites, unmanned aerial vehicles or UAVs, generally from space) and IoT (e.g., smart meters, sensors, and actuators, generally from the ground) can address existing gaps that impede efforts to evaluate progress toward global climate change mitigation. We argue that there is underexplored potential for EO and IoT to advance large-scale data generation that can be translated to improve climate change data collection. Finally, we discuss how a system employing digital data collection technologies could leverage advances in distributed ledger technologies to address concerns of transparency, privacy, and data governance.

Bo Zhao, Xu Huang

No abstract is available for this record.

Mahdi Farnaghi, Ali Mansourian

Web-based public participatory GIS (PPGIS) has been used by governmental organizations to facilitate people's contribution to decision-making processes. However, these applications do not provide an open and transparent environment for public participation. This study suggests that PPGISs should be developed as decentralized applications (DApp) based on Ethereum blockchain technology to have a fully open, transparent, and accountable environment for public participation. In a blockchain-based PPGIS, the collected data are securely saved on the blockchain. The validity of the data, replicated on the nodes of the peer-to-peer blockchain network, is ensured through a consensus process without any central control. The data is tamper-free and immutable. Additionally, the data is openly accessible to institutions and citizens. A prototype PPGIS was developed as a DApp through which users can participate in the site selection of urban facilities. Using the application, they compare and rank different criteria. The system solves an analytic hierarchy process to calculate the weights of the criteria. A suitability map is generated afterward and published to be used by both citizens and decision-makers. The feasibility of the application, along with the issues that need to be considered while using blockchain technology for urban planning and development, are thoroughly discussed.

Emanuel Vieira, Paulo Bartolomeu, Seyed M. Hosseini, Joaquim Ferreira

The extensive use of smartphones combined with the rise in the Internet of Things adoption has fostered the emergence of several use-cases to provide added comfort and peace of mind in our everyday life. Public transportation payment in large cities, where frequent commuters and generic users often spend a significant amount of time buying and validating tickets, can become a cumbersome process. This paper presents the design and implementation of a seamless payment system named IOTApass, which handles payments through the usage of a smartphone and a distributed ledger technology to avoid the limitations of centralized payment solutions. The IOTApass enables a user to seamlessly pay for public transports using a smartphone App without explicit (user) interface interactions, thus reducing payment complexity. Besides describing the architecture, operation, and implementation of IOTApass, the paper documents its experimental validation and confirms its feasibility.

Constantinos Marios Angelopoulos, Amalia Damianou, Vasilios Katos

In order to contain the COVID-19 pandemic, several countries enforced extended social distancing measures for several weeks, effectively pausing the majority of economic activities. In an effort to resume economic activity safely, several Digital Contact Tracing applications and protocols have been introduced with success. However, DCT is a reactive method, as it aims to break existing chains of disease transmission in a population. Therefore DCT is not suitable for proactively preventing the spread of a disease; an approach that relevant to certain use cases, such as international tourism, where individuals travel across borders. In this work, we first identify the limitations characterising DCT related to privacy issues, unwillingness of the public to use DCT mobile apps due to privacy concerns, lack of interoperability among different DCT applications and protocols, and the assumption that there is limited, local mobility in the population. We then discuss the concept of a Health Passport as a means of verifying that individuals are disease risk-free and how it could be used to resume the international tourism sector. Following, we present the DHP Framework that uses a private blockchain and Proof of Authority for issuing Digital Health Passports. The framework provides a distributed infrastructure supporting the issuance of DHPs by foreign health systems and their verification by relevant stakeholders, such as airline companies and border control authorities. We discuss the attributes of the system in terms of its usability and performance, security and privacy. Finally, we conclude by identifying future extensions of our work on formal security and privacy properties that need to be rigorously guaranteed via appropriate security protocols.

Alexandros Bampoulidis, A. Bruni, Lukas Helminger, Daniel Kales · 6 authors

Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases [74]. While prior studies have exclusively relied on a mobile network operator’s subscribers’ aggregated data in modelling disease dynamics, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, or both. This work aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, validation techniques derived from zero-knowledge proofs, and differential privacy. Our protocol’s open-source implementation can process eight million subscribers in 70 minutes.

Alexandros Bampoulidis, A. Bruni, Lukas Helminger, Daniel Kales · 6 authors

Recent work has shown that cell phone mobility data has the unique potential\nto create accurate models for human mobility and consequently the spread of\ninfected diseases. While prior studies have exclusively relied on a mobile\nnetwork operator's subscribers' aggregated data in modelling disease dynamics,\nit may be preferable to contemplate aggregated mobility data of infected\nindividuals only. Clearly, naively linking mobile phone data with health\nrecords would violate privacy by either allowing to track mobility patterns of\ninfected individuals, leak information on who is infected, or both. This work\naims to develop a solution that reports the aggregated mobile phone location\ndata of infected individuals while still maintaining compliance with privacy\nexpectations. To achieve privacy, we use homomorphic encryption, validation\ntechniques derived from zero-knowledge proofs, and differential privacy. Our\nprotocol's open-source implementation can process eight million subscribers in\n70 minutes.\n

Bojan Radojević, Lazar Lazić, Marija Cimbaljević

The COVID-19 pandemic has imposed numerous, lasting adverse effects on the global tourism industry. At the same time, it exposed the competitive advantages that existing smart tourism infrastructure could provide for addressing urgent health issues and providing meaningful smart services. This paper initially provides examples of smart geospatial services based on COVID-19 pandemic-related data, such as algorithms for measuring social distancing through CCTV and proximity contract tracing protocols and applications. Indeed, smart destinations, as an evolutionary step of smart cities, very quickly became a practical and research framework in various other disciplines, from leisure and service-oriented to technical and geospatial domains. However, various technologies employed and interests of different stockholders create a constant need for rescaling of smart data to facilitate their usability in providing optimized smart tourism services. One of the pressing concerns is the functional alignment of geospatial data with tourism-related data. Thus, we aim to pinpoint the growing importance of smart geospatial services, by pointing to the main downturn of the current smart destination issue with geospatial data resolutions, and, by building upon the relations of the geospatial layer of data with the tourism-specific layer. To this end, we pinpoint two further research directions - reinvestigating spatial and temporal resolution as a core of data smartness and the need for contextual (tourism-oriented) scaling of smart technology. This could be of keen interest in post-pandemic tourism, where smart geospatial services will be of pressing concern, but also it still an issue to be resolved in further smart destination development.

Abhay Goel, Abhishek Sharma, Deepak Gupta, Ashish Khanna

No abstract is available for this record.

Arif Furkan Mendı, Alper Çabuk

No abstract is available for this record.

Alexandra Flynn, Mariana Valverde

A startling announcement came on a crisp, fall day in 2017: Sidewalk Labs, a sister company of Google, was awarded a contract to create a ‘smart city’ along a small stretch of Toronto’s largely con...

Yao Sun, Lei Zhang, Gang Feng, Bowen Yang · 6 authors

Blockchain has shown a great potential for Internet of Things (IoT) systems to establish trust and consensus mechanisms with no involvement of any third party. It has been not clear how the low complexity devices and the wireless communications among them can pose constraints on the blockchain enabled IoT systems. In this paper, we establish a fundamental analysis model to underpin the blockchain enabled IoT system. By considering spatio-temporal domain Poisson distribution, i.e., node geographical distribution and transaction arrival rate in time domain are both modeled as Poisson point process (PPP), we first derive the distribution of signal-to-interference-plus-noise ratio (SINR), blockchain transaction successful rate as well as overall throughput. Then, based on the analytical model, we design an optimal full function node deployment for blockchain system to achieve the maximum transaction throughput with the minimum full function node density. Numerical results validate the accuracy of our theoretical analysis and evaluate the relationship between blockchain full function node deployment and the density of IoT nodes.

Amitrajeet A. Batabyal, Hamid Beladi

We exploit the public good attributes of information and communication technologies (ICTs) and theoretically analyze an aggregate economy of two smart cities in which ICTs are provided in either a decentralized or a centralized manner. We first determine the efficient ICT levels that maximize the aggregate surplus from the provision of ICTs in the two cities. Second, we compute the optimal level of ICT provision in the two cities in a decentralized regime in which spending on the ICTs is financed by a uniform tax on the city residents. Third, we ascertain the optimal level of ICT provision in the two cities in a centralized regime subject to equal provision of ICTs and cost sharing. Fourth, we show that if the two cities have the same preference for ICTs then centralization is preferable to decentralization as long as there is a spillover from the provision of ICTs. Finally, we show that if the two cities have dissimilar preferences for ICTs then centralization is preferable to decentralization as long as the spillover exceeds a certain threshold.

Roman Overko, Rodrigo Ordóñez-Hurtado, Sergiy Zhuk, Pietro Ferraro · 6 authors

We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-of-Position mechanisms to provide protection against spam attacks and malevolent actors. In addition to enabling individuals to retain ownership of their data and to monetize it, the architecture also is suitable for distributed privacy-preserving machine learning algorithms, is lightweight, and can be implemented in simple internet-of-things (IoT) devices. To demonstrate its efficacy, we apply this framework to reinforcement learning settings where a third party is interested in acquiring information from agents. In particular, one may be interested in sampling an unknown vehicular traffic flow in a city, using a DLT-type architecture and without perturbing the density, with the idea of realizing a set of virtual tokens as surrogates of real vehicles to explore geographical areas of interest. These tokens, whose authenticated position determines write access to the ledger, are thus used to emulate the probing actions of commanded (real) vehicles on a given planned route by "jumping" from a passing-by vehicle to another to complete the planned trajectory. Consequently, the environment stays unaffected (i.e., the autonomy of participating vehicles is not influenced by the algorithm), regardless of the number of emitted tokens. The design of such a DLT architecture is presented, and numerical results from large-scale simulations are provided to validate the proposed approach.

Shazade Jameson, C. Richter, Linnet Taylor

In this paper, we investigate people’s perception of datafication and surveillance in Amsterdam Smart City. Based on a series of focus groups, we show how people understand new forms of hypervisbility, what strategies they use to navigate these experiences, and what the limitations of these strategies are. We show how people tried to discern between public and private sector actors, to differentiate who they trusted by building on the existing social contract. People also trusted the objectivity of data in relation to prior experiences of social contexts and discrimination. Lastly, we show how the experiences of some of the inhabitants in our study who were most vulnerable to hypervisibility highlight the limits to strategies based on the neutrality of data. By asking about perceived surveillance rather than emphasising actual practices of surveilling, we show differentiated contexts and strategies, providing empirical grounds to question the dominant technical framing of smart cities.