Search Results (293)

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods. Full article

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies. Full article

While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we propose $\mathbf{SplitML}$, a secure and privacy-preserving framework for Federated Split Learning (FSL). By integrating $IND-CP{A}^D$ secure Fully Homomorphic Encryption (FHE) with Differential Privacy (DP), $\mathbf{SplitML}$ establishes a defense-in-depth strategy that minimizes information leakage and thwarts reconstructive inference attempts. The framework accommodates heterogeneous model architectures by allowing clients to collaboratively train only the common top layers while keeping their bottom layers exclusive to each participant. This partitioning strategy ensures that the layers closest to the sensitive input data are never exposed to the centralized server. During the training phase, participants utilize multi-key CKKS FHE to facilitate secure weight aggregation, which ensures that no single entity can access individual updates in plaintext. For collaborative inference, clients exchange activations protected by single-key CKKS FHE to achieve a consensus derived from Total Labels (TL) or Total Predictions (TP). This consensus mechanism enhances decision reliability by aggregating decentralized insights while obfuscating soft-label confidence scores that could be exploited by attackers. Our empirical evaluation demonstrates that $\mathbf{SplitML}$ provides substantial defense against Membership Inference (MI) attacks, reduces temporal training costs compared to standard encrypted FL, and improves inference precision via its consensus mechanism, all while maintaining a negligible impact on federation overhead. Full article

Zero-Touch Network (ZTN) represents a cornerstone approach of Next Generation Networks (NGNs), enabling fully automated and AI-driven network and service management. However, their distributed and multi-domain nature introduces critical security challenges, particularly regarding service identity and data integrity. This paper proposes a novel blockchain-based framework to enhance the security of ZTN through two complementary mechanisms: decentralized digital identity management and oracle-assisted network monitoring. First, a Decentralized Identity Management framework aligned with Zero-Trust Architecture principles is introduced to ensure tamper-proof authentication and authorization in a trustless environment among network components. By leveraging decentralized identifiers, verifiable credentials, and zero-knowledge proofs, the proposed Decentralized Authentication and Authorization component eliminates reliance on centralized authorities, while preserving privacy and interoperability across domains. Second, the paper investigates blockchain oracle mechanisms as a means to extend data integrity guarantees beyond the blockchain, enabling secure monitoring of Network Services and validation of Service-Level Agreements. We propose a four-dimensional framework for oracle design, based on qualitative comparison of oracle types—decentralized, compute-enabled, and consensus-based—to identify their suitability for NGN scenarios. This work proposes an architectural and design framework for Zero-Touch Networks, focusing on system integration and security-aware orchestration rather than large-scale experimental evaluation. The outcome of our study highlights the potential of integrating blockchain-based identity and oracle solutions to achieve resilient, transparent, and self-managed network ecosystems. This research bridges the gap between theory and implementation by offering a holistic approach that unifies identity security and data integrity in ZTNs, paving the way towards trustworthy and autonomous 6G infrastructures. Full article

Although DNS has been continuously extended to improve usability and security, its centralized, server-based architecture leaves fundamental limitations unresolved, including single points of failure (SPOF), susceptibility to censorship, and exposure to DDoS. This study examines blockchain-based DNS (BDNS) as an alternative proposed to mitigate these structural issues. We first synthesize prior research and systems on BDNS, and then conduct a comparative analysis using practical deployability as the primary criterion. Specifically, we selected seven representative BDNS projects, including Namecoin, Handshake, and Ethereum Name Service (ENS), and evaluated them under a common set of criteria: (i) the record model, finality, and TTL semantics; (ii) friction along real resolution paths involving resolvers, browsers, and gateways; and (iii) interoperability with the legacy DNS, including DNSSEC and DNS over TLS(DoT)/DNS over HTTPS(DoH), together with migration scenarios. The analysis indicates that many systems rely on gateways and client-side extensions, limiting native resolution paths. It further finds that finality latency, dependence on off-chain indexing and availability, and the interplay of key management and tokenomics design increase operational complexity and raise barriers to adoption. Building on these findings, the paper derives operational requirements and proposes a coexistence-first, five-layer migration framework that incrementally integrates BDNS while retaining the legacy DNS. This provides an incremental path toward a more resilient, inclusive, and secure global naming infrastructure. Full article

Dynamic heterogeneous redundancy (DHR) architectures combine heterogeneity, redundancy, and dynamism to create security-centric frameworks that can be used to mitigate network attacks that exploit unknown vulnerabilities. However, conventional DHR architectures rely on centralized control modules for scheduling and adjudication, leading to significant single-point failure risks and trust bottlenecks that severely limit their deployment in security-critical scenarios. To address these challenges, this paper proposes a decentralized DHR architecture based on the Raft consensus algorithm. It deeply integrates the Raft consensus mechanism with the DHR execution layer to build a consensus-centric control plane and designs a dual-log pipeline to ensure all security-critical decisions are executed only after global consistency via Raft. Furthermore, we define a multi-dimensional attacker model—covering external, internal executor, internal node, and collaborative Byzantine adversaries—to analyze the security properties and explicit defense boundaries of the architecture under Raft’s crash-fault-tolerant assumptions. To assess the effectiveness of the proposed architecture, a prototype consisting of five heterogeneous nodes was developed for thorough evaluation. The experimental results show that, for non-Byzantine external and internal attacks, the architecture achieves high detection and isolation rates, maintains high availability, and ensures state consistency among non-malicious nodes. For stress tests in which a minority of nodes exhibit Byzantine-like behavior, our prototype preserves log consistency and prevents incorrect state commitments; however, we explicitly treat these as empirical observations under a restricted adversary rather than a general Byzantine fault tolerance guarantee. Performance testing revealed that the system exhibits strong security resilience in attack scenarios, with manageable performance overhead. Instead of turning Raft into a Byzantine-fault-tolerant consensus protocol, the proposed architecture preserves Raft’s crash-fault-tolerant guarantees at the consensus layer and achieves Byzantine-resilient behavior at the execution layer through heterogeneous redundant executors and majority-hash validation. To support evaluation during peer review, we provide a runnable prototype package containing Docker-based deployment scripts, pre-built heterogeneous executors, and Raft control-plane images, enabling reviewers to observe and assess the representative architectural behaviors of the system under controlled configurations without exposing the internal source code. The complete implementation will be made available after acceptance in accordance with institutional IP requirements, without affecting the scope or validity of the current evaluation. Full article

The construction industry continues to grapple with persistently high accident rates and fragmented workforce management systems, where manual record-keeping and siloed data impede effective safety compliance. While digital interventions exist, they often rely on centralized databases that are vulnerable to manipulation and opaque. This systematic literature review critically examines the application of blockchain technology as a decentralized infrastructure for enhancing safety compliance in construction. Adhering to the PRISMA 2020 guidelines, this study synthesizes findings from 115 peer-reviewed articles (2020–2025) retrieved from Scopus, Web of Science, IEEE Xplore, and Google Scholar. The analysis focuses on three core mechanisms: (1) the creation of immutable, timestamped safety logs to prevent retroactive data tampering; (2) the integration of IoT sensors for real-time, trustless hazard monitoring; and (3) the deployment of smart contracts to automate compliance verification and incentive distribution. The review juxtaposes theoretical frameworks with empirical evidence from global case studies, including pilot projects in North America and the Asia-Pacific, to quantify benefits such as reduced reporting latency and improved data integrity. Despite promising results, the analysis reveals significant barriers to widespread adoption, notably the “oracle problem,” scalability limitations of consensus protocols, and the lack of legal recognition for blockchain records. This paper concludes that while blockchain is not a panacea, it offers a necessary layer of trust and accountability absent in traditional Common Data Environments (CDEs). Future research directions are proposed to address interoperability with BIM standards (ISO 19650) and to develop energy-efficient consensus mechanisms suitable for resource-constrained construction sites. Full article

Blockchain consensus mechanisms are fundamental to the security and decentralization of distributed ledgers. In Proof-of-Stake (PoS) systems, which are lauded for their energy efficiency, the fair and unpredictable selection of block proposers is paramount and relies heavily on secure random number generation. The RANDAO random number generation mechanism in the Gasper protocol is susceptible to hash collision attack, which can introduce adversarial bias in the block proposer selection process. From the perspective of resisting adversarial bias attacks, this paper examines the optimization of the Gasper consensus protocol, focusing on security issues such as vulnerabilities to hash collisions in RANDAO and high latency in asynchronous network environments. By analyzing the spatial–temporal distribution of historical block hashes, we propose a dual-round random number verification mechanism that enhances reliability through multiple validation models. We develop a dynamic game-theoretic model under incomplete information to analyze node strategy selection and interaction dynamics. Our experimental results demonstrate that the improved protocol (RABA-Gasper) offers superior resistance to attacks, fairness, and efficiency compared to conventional protocols. RABA-Gasper outperforms conventional ones, achieving a 6.8% attack success rate (vs. 32.7% for RANDAO and 18.2% for Two Look-Back) with 94.3% hash collision detection, a proposer Gini coefficient below 0.23, 2.3x higher throughput retention than RANDAO in asynchronous networks, and a slightly increased random number generation latency of 125 ms. Supported by a game-theoretic model, it guarantees security when honest nodes account for ≥2/3 of the total. Full article

Blockchain technology has rapidly evolved as a cornerstone of decentralized computing, transforming how trust, data integrity, and transparency are achieved in digital ecosystems. However, despite extensive adoption, significant gaps remain in understanding how key blockchain variables, such as block size, consensus mechanisms, and network latency, affect system vulnerabilities and susceptibility to cyberattacks. This survey addresses this gap by combining qualitative and quantitative analyses across multiple blockchain environments. Using simulation tools such as Ganache and Bitcoin Core, and reviewing peer-reviewed studies from 2016 to 2024, the research systematically maps blockchain parameters to cyberattack vectors including 51% attacks, Sybil attacks, and double-spending. Findings indicate that design choices like block size, block interval, and consensus type substantially influence resilience against attacks. The Blockchain Variable Quantitative Risk Framework (BVQRF) introduced here integrates NIST’s cybersecurity principles with quantitative scoring to assess risks. This framework represents a novel contribution by operationalizing theoretical security constructs into actionable evaluation metrics, enabling predictive modeling and adaptive risk mitigation strategies for blockchain systems. Full article

Securing the reintegration of outlier nodes in dynamic UAV networks is challenging. This challenge arises from the lack of time-sensitive access control in existing key management schemes. We propose the Salted Temporal Key scheme (STK), which combines blockchain-based dynamic key management with temporal validation. This work addresses the absence of a time-sensitive admission policy by coupling reintegration cost to a UAV’s verifiable disconnection time: short-term outliers reintegrate quickly, while long-duration, high-risk outliers face increasing barriers. STK binds reintegration difficulty to the block-broadcast interval $\tau$, making reintegration a computational challenge proportional to the number of missed consensus cycles. Experiments on swarms with 50–100 nodes show that STK efficiently manages reintegration latency, providing scalable and adaptable security for decentralized UAV networks. The results demonstrate that by adjusting $\tau$, operators can isolate UAVs with excessive delays and ensure reliable swarm communication. STK offers a flexible, non-interactive solution, significantly enhancing security and scalability for UAV swarm reintegration in diverse environments. Full article

Blockchain technology has emerged as the backbone of cryptocurrencies and decentralized finance, yet its long-term resilience is increasingly threatened by advances in quantum computing. Quantum algorithms, such as Shor’s algorithm, can undermine public-key cryptography, while Grover’s algorithm accelerates brute-force search, weakening proof-of-work schemes. In this paper, we propose a Quantum Blockchain Framework that integrates quantum communication protocols, quantum consensus mechanisms, and quantum-resistant cryptography. We construct a theoretical model of quantum-secured distributed ledgers, where qubits, entanglement, and quantum key distribution (QKD) enhance security and efficiency. Applications to cryptocurrency are explored, highlighting how quantum blockchain can mitigate security risks, improve consensus speed, and enable quantum-native digital assets. Full article

Objectives: The purpose of this study is to develop and verify a sandwich-type lateral flow immunoassay (LFA) integrated with a smartphone, enabling semi-quantitative 25-hydroxyvitamin D [25(OH)D] measurement including automated image analysis function, thereby establishing a reliable and accessible vitamin D evaluation system for point-of-care (POCT). Methods: A smartphone-based sandwich-type LFA was constructed, and 25(OH)D was measured semi-quantitatively. The system combined a customized test strip with an automatic image acquisition, calibration, and classification module integrated into an application dedicated to a smartphone. Analysis performance, reproducibility, and equivalence between sample types were comprehensively evaluated. Results: The developed analysis achieved a detection range of 5–100 ng/mL, and there were little interference and cross-reactivity for endogenous substances or structurally similar vitamin D derivatives. The image processing algorithm accurately classified the samples into three clinically important categories: deficiency (<20 ng/mL), insufficient (20–30 ng/mL), and sufficient (>30 ng/mL). Cross-platform testing between Android and iOS devices showed excellent reproducibility (r = 0.99, R² = 0.9967). Comparative analysis with the Atellica IM 1600 analyzer showed a high degree of agreement between 97.0% category consensus and κ = 0.951 (r = 0.99, R² ≥ 0.98). Comparative tests between serum and capillary samples also confirmed a 100% classification agreement rate and an overall diagnostic accuracy of 95.5%. Conclusions: This next-generation smartphone integration platform enables rapid, accurate, and semi-quantitative detection of 25(OH)D from fingerstick and serum specimens. By combining the sandwich-type LFA design with computational-based imaging analysis, the system effectively overcomes the major limitations of small-molecule immunoassay and has the potential to be applied to field diagnosis (POCT), decentralized diagnostics, and vitamin D screening in large populations. Full article

The rapid expansion of satellite networks for advanced communication and space exploration has ensured that robust cybersecurity for inter-satellite links has become a critical challenge. Traditional security models rely on centralized trust authorities, and node-specific protections are no longer sufficient, particularly when system failures or attacks affect groups of satellites or agent clusters. To address this problem, we propose a blockchain-enabled decentralized zero-trust model based on post-quantum cryptography (BEDZTM-PQC) to improve the security of satellite communications via continuous authentication and anomaly detection. This model introduces a group-based security framework, where satellite teams operate under a zero-trust architecture (ZTA) enforced by blockchain smart contracts and threshold cryptographic mechanisms. Each group shares the responsibility for local anomaly detection and policy enforcement while maintaining decentralized coordination through hierarchical federated learning, allowing for collaborative model training without centralizing sensitive telemetry data. A post-quantum cryptography (PQC) algorithm is employed for future-proof communication and authentication protocols against quantum computing threats. Furthermore, the system enhances network reliability by incorporating redundant communication channels, consensus-based anomaly validation, and group trust scoring, thus eliminating single points of failure at both the node and team levels. The proposed BEDZTM-PQC is implemented in MATLAB, and its performance is evaluated using key metrics, including accuracy, latency, security robustness, trust management, anomaly detection accuracy, performance scalability, and security rate with respect to different numbers of input satellite users. Full article