Oshani Seneviratne, Fernando Spadea, Adrien Pavao, Aaron Micah Green ¡ 5 authors
Temporal Web analytics increasingly relies on large-scale, longitudinal data to understand how users, content, and systems evolve over time. A rapidly growing frontier is the \emph{Temporal Web3}: decentralized platforms whose behavior is recorded as immutable, time-stamped event streams. Despite the richness of this data, the field lacks shared, reproducible benchmarks that capture real-world temporal dynamics, specifically censoring and non-stationarity, across extended horizons. This absence slows methodological progress and limits the transfer of techniques between Web3 and broader Web domains. In this paper, we present the \textit{FinSurvival Challenge 2025} as a case study in benchmarking \emph{temporal Web3 intelligence}. Using 21.8 million transaction records from the Aave v3 protocol, the challenge operationalized 16 survival prediction tasks to model user behavior transitions.We detail the benchmark design and the winning solutions, highlighting how domain-aware temporal feature construction significantly outperformed generic modeling approaches. Furthermore, we distill lessons for next-generation temporal benchmarks, arguing that Web3 systems provide a high-fidelity sandbox for studying temporal challenges, such as churn, risk, and evolution that are fundamental to the wider Web.
Abderahman Rejeb, Karim Rejeb, Heba F. Zaher, Steve Simske
This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchainâs foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development.
Joy Nnenna Okolo, Adesola Adul-Gafar Arowogbadamu, Samuel Adetayo Adeniji, Rhoda Kalu Tasie
The rapid adoption of mobile AI applications in areas such as healthcare, finance, and personalized services has raised significant concerns about data privacy and security. Traditional centralized machine learning (ML) models require mobile devices to transmit user data to cloud servers, posing risks of data breaches and regulatory non-compliance. Federated learning (FL) addresses these concerns by allowing decentralized AI model training directly on user devices, ensuring that raw data remains private and never leaves the device. However, FL faces security vulnerabilities and performance limitations, including model inversion attacks, data poisoning risks, and high computational overhead. This paper explores key privacy-preserving techniques such as differential privacy, secure aggregation, and homomorphic encryption, which enhance FL security while maintaining model accuracy. Additionally, emerging trends such as blockchain-integrated FL, post-quantum cryptography, and AI-driven optimization are analyzed to highlight the future of privacy-preserving mobile AI ecosystems. By integrating advanced cryptographic techniques and decentralized verification mechanisms, FL can enable scalable, secure, and regulation-compliant AI applications, ensuring a balance between data privacy and AI innovation.
This research examines smart mobility, specifically focusing on e-scooters as a mode of urban transportation. It underscores the advantages of e-scooters in smart cities while also addressing the issues stemming from incorrect riding practices. To understand the strategies of both cities and e-scooter companies, the study adopts a game theory approach. The research delves into how blockchain technology and hardware oracles can promote the appropriate use of e-scooters. In one scenario, the city allocates resources to infrastructure to facilitate e-scooter travel, while the e-scooter company defines its service. Nonetheless, continuous misuse of e-scooters negatively impacts both parties. Therefore, in another scenario, the research assesses how blockchain can detect and penalize incorrect behaviors using smart contracts. The findings reveal that while blockchain bolsters smart mobility and curbs the incorrect use of e-scooters, it might also dissuade certain users from utilizing the service, presenting a set of challenges to smart mobility. ⢠-E-scooters can enhance smart mobility compared to traditional transportation. ⢠-However, e-scooters can give rise to social issues when improperly used. ⢠-The integration of blockchain with hardware oracles can enhance smart mobility. ⢠-Blockchain can penalize improper behaviors through smart contracts. ⢠-Unfortunately, people who dislike blockchain may renounce to smart mobility.
Internet of Things (IoT) devices become more and more important because they are useful in different applications, for example, traffic monitoring, public safety, and environmental management. However, the vastness and diversity of IoT data and the requirement for real-time decision-making create a big challenge for anomaly detection frameworks. In this work, we propose SwinIoT, a new framework with hierarchical and windowed attention mechanisms of Swin Transformer that is especially good for the purpose of behavioral anomaly detection in IoT settings. SwinIoT would solve important problems of class imbalance, noisy data, and heterogeneous devices through the introduction of custom attention models embedding real-time optimizations. The proposed framework was benchmarked on nine datasets such as ARAS, CASAS, WESAD, and UCF Crime compared to the above-mentioned state-of-the-art algorithms like Active Learning-Based Anomaly Detection, Deep Support Vector Data Description (DSVDD), Deep Support Vector Data Description Contractive Autoencoder (DSVDD-CAE), and Federated Principal Component Analysis (FedPCA). It is proven to be better than the above algorithms by attaining up to 96% accuracy, 97% Mean Average Precision$(mAP)$, and excellent Area Under the Receiver Operating Characteristic curve (AUC-ROC) as well as Precision-Recall (PR) metrics, especially in low-resource and unbalanced data scenarios. The results indicate the potential of SwinIoT in scalable and accurate anomaly detection to the development of safer, smarter cities, ensuring reliability and security in critical systems.
This paper examines business model implementations in three leading European smart cities: London, Amsterdam, and Berlin. Through a systematic literature review and comparative analysis, the study identifies and analyzes various business models employed in these urban contexts. The findings reveal a diverse array of models, including publicâprivate partnerships, buildâoperateâtransfer arrangements, performance-based contracts, community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies. Each city leverages a unique combination of these models to address its specific urban challenges and priorities. The study highlights the role of PPPs in large-scale infrastructure projects, BOT arrangements in transportation solutions, and performance-based contracts in driving efficiency and accountability. It also explores the benefits of community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies in enhancing the sustainability, efficiency, and livability of smart cities. The paper offers valuable insights for policymakers, urban planners, and researchers seeking to advance smart city development worldwide.
Non-fungible tokens (NFTs), which are immutable and transferable tokens on blockchain networks, have been used to certify the ownership of digital images often grouped in collections. Depending on individual interests, wallets explore and purchase NFTs in one or more image collections. Among many potential factors of shaping purchase trajectories, this paper specifically examines how visual similarities between collections affect wallets' explorations. Our model characterizes each wallet's explorations with a LĂŠvy flight and shows that wallets tend to favor collections having similar visual features to their previous purchases while their behaviors vary widely. The model also predicts the extent to which the next collection is close to the most recent collection of purchases with respect to visual features. These results are expected to enhance and support recommendation systems for the NFT market.
Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.
Muhammad Saleem, Muhammad Sajid Farooq, Tariq Shahzad, Arfa Hassan ¡ 8 authors
In the recent era, the practical implementation of Autonomous Vehicular Networks (AVNs) with the vulnerable Vehicle-to-Vehicle (V2V) communication of autonomous vehicles and inadequate intelligent decision-making systems has become a primary concern in smart city mobility. This has led to the traffic congestion concerns such as time wastage, compromised safety, decreased durability and reliability of transportation infrastructure and V2V communication short delay and Roadside Units (RSUs), and reduced traffic flow. To address these issues, secure AVN communication and smart decision-making for autonomous vehicles in smart cities are of utmost importance. It ensures safety on roads, durability of the infrastructure, transparency, reliability, traffic congestion reduction and transportation efficiency. MapReduce is a reliable distributed computing paradigm which is able to analyze and process enormous AVN data in parallel. It contributes to smoother traffic flow by identifying the patterns and providing actionable insights for real-time decision making to decrease congestion. A private blockchain AVN can efficiently solve the problems of data security and reliability by providing tamper-proof record of all the transactions, hence enhancing reliability, and also offering a trusted solution of unauthorized access in real-time V2V communication. Explainable Artificial Intelligence (XAI) which is an efficient way to analyze fairness in traffic data over time providing transparency and availability of intricate traffic patterns, improving real-time traffic management with V2V communication and RSUs and reducing short delays that may occur as well as enabling traffic flow and the development of predictive traffic models that assist in decision making. This research proposed an XAI-based transparent model integrating MapReduce for processing large amounts of data and private blockchain technology for secured and tamper-proof vehicular communication. This proposed model is a promising solution for addressing the AVN data security issues and reliability of the system, mitigating negative effects of traffic congestion, and improving the transparency of decision making on the transport efficiency in smart cities. The proposed model provides a better performance than the previous approaches and gets 96% of the accuracy and 4% of miss rate.
Bitcoinâ s anonymity greatly protects usersâ 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.
Libertarian âexitâ imaginaries project new social, political, and economic structures separate from existing institutions in which âsovereign individualsâ 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.
Lajos Kelemen, IstvĂĄn AndrĂĄs Seres, Ăgnes 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.
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.
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.
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 citizenâs 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 donât 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.
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.
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.
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.
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.
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.
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.