Abstract Bitcoin mining is not only the fundamental process to maintain Bitcoin network, but also the key linkage between the virtual cryptocurrency and the physical world. A variety of issues associated with it have been raised, such as network security, cryptoasset management and sustainability impacts. Investigating Bitcoin mining from a spatial perspective will provide new angles and empirical evidence with respect to extant literature. Here we explore the spatial distribution of Bitcoin mining through bottom-up tracking and geospatial statistics. We find that mining activity has been detected at more than 6000 geographical units across 139 countries and regions, which is in line with the distributed design of Bitcoin network. However, in terms of computing power, it has demonstrated a strong tendency of spatial concentration and association with energy production locations. We also discover that the spatial distribution of Bitcoin mining is dynamic, which fluctuates with diverse patterns, according to economic and regulatory changes.
S. Palmieri, Mario Bisson, Alessandro Ianniello, Riccardo Palomba · 5 authors
The expected demographic densification presents specific critical points where op-portunities for improving citizens' lives can be identified. For this reason, projects are underway to analyze and explore the dynamics of cities to adapt to new con-texts. Several European cities, including Milan, Paris, and Barcelona, are already implementing changes to encourage new types of neighborhood organizations which revolve around the concept of proximity, and primary services close to home. In this context, it seems fundamental to seek connectivity, encouraging new forms of relationships between citizens. The use of new digital tools, such as blockchain, favors new types of autonomous organizations that can manage activities on a neighborhood scale. Design should propose suitable and innovative models of ap-plication and act as a facilitator for their implementation. Through design, it is also possible to identify guidelines for the relationships in a neighborhood and to define activities and experiences with which citizens can relate.
The proliferation of IoT-based services for smart cities, and especially those related to mobility, are ever becoming more relevant and gaining attention from a number of stake-holders. In our work, we tackle the problem of characterizing people movements in a urban environment by using WiFi sensors connected to the cellular network. In particular, we leverage WiFi probe requests transmitted by people’s smartphones and a machine learning approach to detect people’s flows, while preserving users’ privacy. We validate our approach through a proof-of-concept testbed deployed in the proximity of our campus area. We consider two types of devices, namely, commercial, off-the-shelf WiFi scanners and ad-hoc designed scanners implemented with Raspberry PIs. They provide different levels of visibility of the captured traffic, preserving in different ways the privacy of the people’s movements. In our current work, we investigate the different trade-offs between mobility tracking accuracy and the level of provided people’s privacy.
Key messages Grounding practices within the materiality of geography is an important technique for studying the complexity of digital phenomena. The DIGO (Discourses, Infrastructures, Groupings, and Outcomes) framework uses these categories to guide data selection for locating digital phenomenon in material geographies. This article applies the DIGO framework to blockchain (using data about tweets, miners, firms, and ICOs) to show how this digital practice connects to and across material geographies.
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.
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.
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.
For first responders entering into a post-disaster situation, there is usually a severe lack of up-to-date ground truth. The initial period of time has multiple sources of conflicting information coming in and creating confusion about the situation. The most important immediate requirement is to create a traversal map, highlighting navigable paths to victims of the disaster and possible hazardous locations. Due to infrastructure damage, it is hard for existing centralized geospatial portals to quickly update and provide this information, which has become outdated. IoT solutions that can be deployed without extensive preparation provide the capability to quickly acquire and disseminate essential information to rescue teams. In this paper, we present a decentralized system, named DEIMOSBC, that is able to provide such a mapping service faster and more reliably, utilizing the work of volunteers and relying on a blockchain backend that is based on an IoT system. Our solution utilizes the availability of modern smartphones with GPS receivers and processing capabilities to collect sequences of GPS locations and chain them into trajectories. These trajectory data are submitted as entries into a blockchain after cleaning them through a purpose-built smart contract. DEIMOSBC relies on the inherent robustness and distributed nature of a blockchain to make collating and assembling a map from these paths more accurate and less susceptible to disruption. We describe how DEIMOSBC would work for a hypothetical disaster scenario of a Category 5 hurricane striking an area of the Gulf of Mexico.
Junaid Ahmed Khan, Kavyashree Umesh Bangalore, Kaan Özbay
Privacy preservation in contact tracing for COVID-19 is challenging as such applications tend to reveal users sensitive data which is shared together with their location. This paper proposes COVERT-Blockchain, a novel distributed ledger based platform for contact tracing without revealing users privacy where infected users only share their anonymized location traces on the Blockchain with a sliding window. To further reduce the chances of revealing the corresponding users' trajectories, in COVERT-Blockchain we employ an adaptive logging mechanism to store trajectory data for contact tracing only if the users stayed in a location for longer time duration. COVERT-Blockchain is evaluated for scalability and robustness in terms of overhead and delays in storing and retrieving data, results show it to be efficiently achieving contact tracing without privacy leakage.
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.
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.
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.
Sadaf MD Halim, Latifur Khan, Bhavani Thuraisingham
Mobile devices are a rich source of sensitive location data. In this paper, we propose a method for harnessing this data to provide better location predictions without sacrificing the privacy of the users generating this data. To this end, we propose utilizing Federated Learning to train locally on a user's mobile device, while simultaneously identifying and combatting the possibility of bad actors or adversaries that may deliberately report problematic data to hurt the training process. Furthermore, we propose using a blockchain instead of a centralized server for the training process, to ensure that the process is secure.
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.