X M Liu, Yilai Lian, Likai Jia, F H Wang ¡ 8 authors
With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.
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.
Korean Humanities and Social Science Solidarity, seongmin Hong
This study analyzes the structural causes and social impacts of fare evasion in public transportation systems and proposes comprehensive improvement measures for ticketing systems. Fare evasion, defined as intentionally avoiding or underpaying fares, threatens the sustainability and fairness of city rail transit, leading to significant financial losses, which have been estimated in prior studies to reach tens of billions of KRW annually. As evasion methods become increasingly sophisticated through technology such as mobile card sharing and data manipulation, existing automated systems show clear limitations due to the lack of real-time identity verification. To address these issues, this research suggests a phased strategic roadmap consisting of short, medium, and long-term solutions based on case studies from leading global cities like London, Tokyo, and Singapore. First, as a short-term measure, the introduction of real-name ticketing and improvement of physical gate structures are proposed. By requiring personal identification for the issuance of single-use and concessionary cards, the unauthorized transfer or lending of tickets can be prevented. Furthermore, the transition to a closed-gate system is essential to eliminate verification blind spots and ensure accurate real-time fare calculation. Second, as a medium-term measure, the adoption of NFT (Non-Fungible Token) based tickets and AI-driven enforcement systems are highlighted. NFT technology ensures the uniqueness and authenticity of tickets through blockchain, significantly reducing the risk of duplication. Simultaneously, AI-based video analysis can support the detection of physical evasion behaviors like 'tailgating' in real-time, improving operational efficiency. Third, as a long-term strategy, the implementation of biometric authentication systems (facial, fingerprint, or iris recognition) is suggested. This âPost-ticketâ approach uses non-fungible physical characteristics for verification, substantially mitigating ticket-related fraud. However, the study emphasizes that technological implementation must be accompanied by robust legal and regulatory frameworks for data privacy protection, as well as sufficient social consensus.
Rajesh Sehgal, Anish Gupta, G. Premananthan, Ahmed Anwer Jaafa ¡ 6 authors
Due to increased globalization and international travel, there is a growing demand for secure and private identity verification systems at border entries. Reliance on databases controlled by only a few hinders quick, safe, and easy border crossings for travelers. The paper proposes a system utilizing blockchain technology that would enable individuals to verify their identities in real-time when traveling internationally. The system suggested for travelers utilizes selfsovereign identity technology, allowing them to manage their verified IDs on their devices through wallets based on blockchain technology. Once biometric details, such as facial recognition, fingerprints, and iris scans, are collected from each individual, they are linked together on the identical federated, permissioned blockchain by the nation's and border agencies. With the help of smart contracts, it is possible to control entry such that only approved agencies verify traveler documents, and zero-knowledge proofs ensure that only essential attributes about a person's identity are disclosed. Simulations prove that the system reduces verification time to under two seconds, significantly outperforming traditional systems, and prevents nearly 90% more identity fraud. Additionally, since blockchain is decentralized and secure, it ensures that audit records are transparent and there are no single points of failure. The work introduces a manageable and compatible system that enables people to travel more efficiently while facilitating collaboration between border officials and countries. The next step is to do significant pilot deployments, connect to current travel technology, and face rules and differences among countries and regions to make sure air (/land) mobility grows for everyone.
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.
Syed Ibad Ali, Mohammad Shahnawaz Shaikh, Kush Bhushanwar, Smita Shahane
Today's cities face numerous challenges due to climate change and urbanization. The concept of a smart city aims to help cities to address these challenges by adapting modern information and communication technology. Smart mobility and transportation form one important aspect of smart cities. Inefficient mobility in cities can lead to problems such as traffic congestion, which results in frustration for residents and a decrease in the quality of life. Against the backdrop of global warming, cities also strive to reduce CO2 emissions, an attempt which requires sustainable and novel mobility concepts. Blockchain is a current technology, said to have huge potential, that is being investigated for application in many facets of smart cities. In the context of smart mobility, blockchain can be used for transactions relating to ridesharing and electric charging, handling of interactions of platoon members, or serving as a foundation for communication between vehicles. Although initial research about this topic exists, it is distributed among different use-cases and applications.
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.
With the widespread adoption of mobile smart devices, mobile crowd sensing(MCS) has provided better services for people. To meet the growing sensing demands within a limited budget, platforms have integrated two modesâopportunistic sensing and participatory sensingâto utilize their complementary strengths. However, location privacy issues may reduce workers' willingness to participate, thereby affecting task completion rates. Although existing methods have addressed privacy protection in a single sensing mode, there remains little focus on location privacy in hybrid sensing modes. There are two main limitations in privacy issues related to task allocation: (i) how to effectively preserve workers' location privacy in hybrid sensing modes, and (ii) the usual reliance on trusted thirdparty institution. To address these issues, we propose a privacypreserving hybrid multi-task allocation for MCS (PPHMA). This approach preserves workers' location privacy without relying on a fully trusted third-party institution, while maximizing the number of tasks completed. Specifically, for opportunistic task allocation, we employ zero-knowledge range proofs to protect workers' location , thereby avoiding location privacy leaks. Subsequently, based on the performance capability indicator of opportunistic workers, we select appropriate workers for task allocation. For participatory task allocation, we employ a worker location obfuscation generation algorithm to locally generate and upload obfuscated locations, ensuring that both the worker's real and obfuscated locations satisfy Ďľ-Geo-Indistinguishability within the protected range. Then, based on the execution capability indicator of the participatory workers, we screen for candidate workers and use a greedy immune clone algorithm to optimize the workers' travel distances. Finally, we verify the effectiveness of the scheme through experiments using two real-world datasets
The rise of Internet of Things (IoT) devices, fog computing, and blockchain technologies has reshaped modern distributed systems, but energy consumption poses a critical challenge. This chapter explores energy patterns in IoT, fog, and blockchain ecosystems, emphasizing the importance of efficiency. It discusses the interplay between these systems, energy usage in IoT devices, network protocols, cloud and edge computing impacts, energy needs in fog computing, and challenges in distributed fog nodes. It also examines the energy implications of blockchain consensus mechanisms like proof-of-work and proof-of-stake, sustainable protocols, and energy-efficient strategies such as machine learning. Real-world examples highlight successful energy-efficient deployments in smart cities and green energy systems. The chapter concludes by stressing the need for energy-efficient practices in designing and implementing these technologies for a sustainable digital future.
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.
This paper presents an extensive assessment of employing blockchain technology in a land registry system, emphasizing its role in ensuring secure and transparent property transactions. The research explores the integration of blockchain to enhance the efficiency, security, and reliability of land registration processes. Various aspects, such as data integrity, transparency, and immutability, are considered in the context of utilizing blockchain for land registry purposes. The application of blockchain technology in the land registry system aims to address inherent challenges related to traditional property registration, including issues of fraud, lack of transparency, and complex verification processes. By leveraging blockchain's distributed ledger technology, a decentralized and tamper-resistant platform is created to record land ownership, transfers, and transactions securely and transparently. Through the implementation of smart contracts and cryptographic principles, the proposed blockchain-based land registry system ensures the authenticity of property records, facilitates automated execution of agreements, and eliminates intermediaries, thereby reducing transactional costs and enhancing trust among stakeholders. This study highlights the potential benefits of utilizing blockchain in land registry systems, emphasizing its ability to streamline property transactions, prevent fraud, and establish an immutable record of ownership. The application of blockchain technology in land registry systems represents a significant step toward creating more efficient, transparent, and trustworthy property management frameworks. Future research endeavors could explore the scalability, interoperability, and regulatory considerations associated with implementing blockchain in land registry systems. These investigations aim to further refine and optimize blockchain-based solutions tailored to the specific needs of land administration, fostering greater efficiency and reliability in property transactions and management.
Cryptocurrencies have attracted increasing attention worldwide. Cryptocurrency assets are likely to remain a viable choice for the public in long term. In this paper, the modelling of cryptocurrency price is explored in Bitcoin bubbles prior to and during the COVID-19 pandemic. As shown here, it is necessary and possible to understand the dynamics in plausible proxy variables. A similar methodology could be deployed in other situations where market bubbles occur.
Mobile crowdsensing (MCS) is an efficient approach for large-scale sensing data collection by leveraging the mobility and capability of mobile devices. To avoid the weaknesses of traditional centralized crowdsensing systems, blockchain has been introduced to secure the process of MCS. This paper studies a location-aware scenario, where privacy of users are protected in a blockchain- based MCS system, and formulates an optimization problem to maximize the coverage given a budget based on reverse auction. An incentive mechanism named MMCB is further proposed and implemented as smart contracts in blockchain to solve the problem. We demonstrate that the mechanism achieves a set of desirable properties, including computation efficiency, individual rationality, truthfulness, budget feasibility, approximation, and privacy preservation. To protect the identity privacy of workers and obtain anonymity, a linkable ring signature is employed in smart contracts. In addition, a Pedersen commitment is utilized for protecting workersâ bid profile and the submitted sensing data is encrypted and only accessible to the requester. We implement a prototype system based on the Hyperledger Fabric platform, and the evaluation results show that our privacy-preserving incentive mechanism architecture improves 36.2% coverage and reduces 53.1% payment with better security level compared to the state-of-the-art schemes.
Abstract Smart devices are widely used in every application area and they serve specific application purposes. While some devices have their own large memory and processing unit, others are limited to data collection without processing capabilities. The land registry, one of the oldest processes for economic growth and governance, traditionally involves mapping the land in the field and collecting data on-site. Despite the shift to online processes, measurement and verification still rely on the traditional system. Blockchain technology, known for its transparency, immutability, speed, security, and decentralized storage, processes data on an immutable distributed ledger. Each node in the network maintains an updated copy of the ledger after each block addition. In the context of land registry, blockchain records every detail while addressing security, privacy, and smart network challenges. In the proposed system, a geolocation-based land registry using blockchain technology is utilized to enhance security, transparency, privacy, and speed compared to existing blockchain-based land registry systems. The proposed scheme follows a three-step registry process. The first step involves handling previous owner records, while the second step focuses on creating new records using smart devices and blockchain. The third step includes record verification and finalization of the registry process with the new owner. This process ensures personal data minimization, secure transactions, and payment of registry fees using blockchain technology. Smart devices play a crucial role in the proposed scheme by verifying the correct land, taking land measurements, and directly recording entries on the blockchain. Based on geolocations and areas, all required parameters for the registry are calculated. The implementation of the proposed system can prevent fraud and document forgeries in the land registry. A security analysis is conducted, and the system is implemented using multichain blockchain. Smart filters facilitate communication between sellers and buyers. Overall, the proposed system offers a hassle-free, geolocation-based, privacy-preserving land registry using blockchain technology.
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.