This chapter discusses the growing challenges faced by cities as their population continues to grow and citizen services become more and more complex and interconnected. It argues that many of the inefficiencies and mistrust are not because of a lack of digitization of urban services, but because of fragmented data, agencies operating in silos, and a lack of transparency in government processes. With that in mind, the chapter introduces blockchain as a distributed ledger technology and a tool that governments can use to fundamentally redefine management of data, processes, and coordination across agencies while maintaining transparency. The chapter further dives into cases where blockchain has been used to manage public records, procurement, utilities, citizen participation, and resource management. It also discusses technical, legal, financial, and social limitations of the technology. The chapter concludes with an explanation of an implementation framework with a strong focus on long-term sustainability, inclusiveness, regulatory compatibility, and incremental adoption. The combination of these makes blockchain a strategic solution to reestablish accountability, transparency, and confidence in urban services instead of a panacea.
Baocheng Zeng, Jinhao Yang, Peilin Han, Kangnan He
Public cryptocurrency archives may appear usable when files exist, although factor research requires observations available and executable at each decision time. We audit public Binance BTCUSDT USD-M perpetual-futures data using event, publication, and availability times and separate proposal from deterministic auditing, evaluation, and holdout access. An initial gapless five-minute requirement for trade, mark, index, and open interest failed: the longest unrepaired intersection was 304.5729166666667 days. A disclosed revision made trade, mark, index, and realized funding the core streams and made open interest optional because its publication time was unverified. The revised mask retained 727 complete UTC days and supported a 436/145/146-day train, validation, and historical-holdout split. On 80 frozen known-rule templates, the auditor detected 40/40 violations and rejected 0/40 legal templates. Across ten null-signal paths, full auditing reduced mean false passes from 0.2910 to 0.0625. Under matched valid-candidate budgets, the audited adaptive agent tied random search and did not establish superiority. In the one-time historical holdout, all evaluated runs had positive IC but negative net Sharpe under primary costs. We therefore report a scoped negative result rather than a profitability or agent-superiority claim.
With the rapid evolution of the Decentralized Finance (DeFi) ecosystem, stablecoins have emerged as a critical infrastructure bridging the cryptocurrency market with traditional financial paradigms. However, stablecoin systems rely heavily on smart contracts to execute automated operations. The immutable nature of these systems post-deployment means that the exploitation of security vulnerabilities can lead to irreversible, massive economic losses and potentially trigger systemic financial risks. Current research on stablecoin smart contract security faces challenges such as a lack of domain-specific targeting and the obsolescence of static defense models. To address this, this paper systematically analyzes common attack vectors in stablecoin environments and proposes a practical, real-time dynamic defense architecture. By analyzing 12 real-world security incidents, we elucidate the underlying mechanisms of high-risk patterns such as reentrancy attacks, oracle manipulation, and composite flash loan attacks. Concurrently, we construct a real-time anomaly detection model utilizing multi-dimensional on-chain temporal features and the Bi-LSTM algorithm. Experimental results demonstrate that this model achieves a classification accuracy of 96.61\%, with an average recall rate of 97.70\% for malicious attack samples, and a single inference latency ranging from 1.5 to 2.8 milliseconds.
Abstract Vehicle-to-everything (V2X) systems combine safety-relevant telemetry, wireless communication, distributed identity, collaborative learning, and long-lived cryptographic trust, creating security dependencies that are difficult to evaluate when each mechanism is studied in isolation. This paper presents OmniGuard V2X, a six-layer research prototype that integrates post-quantum key establishment and signatures, privacy commitments, decentralized identity, a permissioned tamper-evident ledger, federated-learning experimentation, anomaly hooks, and authenticated runtime services in a unified C++/Go/Python stack. The contribution is an assumption-aware validation model rather than a new cryptographic primitive. The supported native path uses real ML-KEM-512 and ML-DSA-44 operations, while classical Pedersen and Schnorr-style assumptions are explicitly excluded from end-to-end post-quantum claims. The prototype further implements encrypted persistent PQC identity material, stable key identifiers, guarded rotation, signed key-transition evidence, bounded historical signature verification, fail-closed provider selection, replay-aware local control, and authenticated rollback-state checks. A retained warm-start pipeline measurement is 5.34 ms in one prototype environment and is reported only as a descriptive local result. Existing anomaly and federated-learning experiments are classified as sanity checks because they do not yet support statistically defensible robustness metrics. The study shows how machine-readable security boundaries and evidence-aware claim control can improve the reproducibility and interpretability of multi-mechanism V2X security prototypes.
Abstract Smart contract vulnerabilities have caused billions of dollars in losses across decentralized finance. Finding reliable ways to detect such vulnerabilities has been a long-standing challenge for researchers. The growing capabilities of large language models (LLMs) are promising, but the factors that determine their reliability and capabilities remain poorly understood. This study investigates whether increasing inference-time computation using techniques like extended reasoning and structured prompting always improves vulnerability detection capability. It also identifies the most influential factors to select a model for this task. Using four prompting techniques, it evaluates 14 LLMs from seven families on 54 Solidity contracts. The experiment reveals a clear gap in detection capability across model classes. While six frontier models do not report false positives on verified-clean contracts, all three small open-source models report vulnerabilities in every case throughout the experiment. Moreover, a 11.5% drop in F1 score for one model was observed when increasing inference-time compute by enabling extended thinking. Also, prompting strategy has a limited effect on detection capability compared to model selection. The results challenge common assumptions and offer practical insights into the use of LLMs for smart contract vulnerability detection.
A One-Byte, Options-Free Market-State Monitor: Detection-Preserving Compression of Financial Data Streams with a Class-Discriminant Token Randolph James Ferlic, M.D., and Kimberly Kate Ferlic — Fieldstone Analytics, LLC Correspondence: randolphf@fieldstoneanalyticsllc.com Preprint · Zenodo DOI: 10.5281/zenodo.22101085 · CC-BY 4.0 Abstract A previously described class-discriminant encoder reduces a window of a multivariate stream to a single 8-bit token (statistical features projected onto a shrinkage-regularized linear-discriminant and principal-component subspace, quantized to a k-means centroid, read by a lightweight head), trading thousands-fold compression for a preserved decision. We ask, under strict pre-registration, how much of that property survives on financial data — the hardest domain for naive machine learning. Across five asset classes (equities, foreign exchange, rates, cryptocurrency, commodities) and thousands of trading days of real daily price/volume data, we find a sharp, consistent boundary. As a detector, the token is near-lossless: an unsupervised codebook fit on calm windows only flags market-stress windows by nearest-centroid distance at a mean AUC of about 0.90, within 0.01–0.04 of a full 50-feature detector, at roughly 500× compression and with no labels. Pooled across a thirteen-name basket, a one-byte-per-instrument, options-free Token Market-State Index tracks the VIX volatility index (Spearman 0.62) and detects market stress that neither a full-feature detector nor VIX statistically outperforms at this sample size — a result that survives purged, embargoed walk-forward validation and block-bootstrap confidence intervals across 2010–2026, and generalizes across all five asset classes, to intraday (hourly) frequency, and to a distinct cross-asset macro risk-off state. A second, complementary detector built from the same tokens — the cross-sectional co-movement of the per-instrument token distances — flags correlation and contagion regimes, adding information beyond volatility (a +0.11 AUC point-estimate increment) and rivaling the absorption-ratio systemic-risk measure computed from the full return covariance. As a classifier or forecaster, the same token is honestly limited: it pays a real 0.07–0.16 AUC tax on supervised volatility-regime and market-state classification (only partly recovered by multi-token product quantization), it is coincident rather than leading, and it shows no directional-return skill at any horizon. The unifying regularity is that the encoder retains what a detector needs and discards what a classifier or forecaster needs — the same compression boundary observed for physiological and industrial signals, now mapped in finance. We report all negatives, including two pre-registered red flags that caught bugs in our own code before they became false results. Highlights · The single 8-bit token is a strong coincident detector of market stress: an unsupervised calm-fit codebook flags stress windows at a mean AUC of ~0.90, within 0.01–0.04 of a full 50-feature detector, at ~500× — indeed as few as two bits — compression, with no labels. · A pooled, options-free, one-byte-per-instrument Token Market-State Index tracks VIX (Spearman 0.62) and detects market stress that neither a full-feature detector nor VIX statistically outperforms at this sample size — surviving purged/embargoed walk-forward and block-bootstrap intervals across 2010–2026, and generalizing across equities, FX, rates, cryptocurrency and commodities, to intraday frequency, and to a distinct macro risk-off state. · A second detector from the same tokens — cross-sectional co-movement — flags contagion / correlation regimes, adding information beyond volatility (a +0.11 AUC point-estimate increment) and rivaling the absorption-ratio systemic-risk measure, from compressed tokens rather than the full covariance. · Honest limits, fully reported: the token pays a real 0.07–0.16 AUC classification tax, is coincident, not leading, and shows no directional-return skill at any horizon; a coincident de-risking rule reduces drawdown but is presented explicitly not as a trading strategy. Two self-caught bugs (a lookahead label and a zeroed factor) are disclosed. · No new method is claimed: the contribution is a pre-registered map of where an extreme-compression class-discriminant token is a detector and where it is not, on real public market data, with all outcomes — including those that missed their bands — reported verbatim. Statistical candor is explicit: only the market-state result carries bootstrap confidence intervals; the other comparative figures are single-split point estimates, pre-registered but multiplicity-uncorrected. What this record contains · The manuscript (PDF), with a fourteen-study summary table and eight figures. · A reproducibility archive (`PAPER_39_ZENODO_ARCHIVE.zip`): the eight pre-registration scopes with frozen outcome bands, the deterministic per-study runners (FIN-1 through FIN-14), the per-study JSON result summaries behind every figure and table value, and the figures. All data are public daily and hourly OHLCV series and the CBOE Volatility Index (VIX); no raw data is redistributed — the loaders fetch the public source at run time. Cite as R. J. Ferlic and K. K. Ferlic, "A one-byte, options-free market-state monitor: detection-preserving compression of financial data streams with a class-discriminant token," Zenodo, 2026, doi: 10.5281/zenodo.22101085. License and patent notice Released under CC-BY 4.0. Consistent with Section 2(b) of that license, no patent, patent-application, or other intellectual-property right of the authors is licensed, waived, granted, or otherwise conveyed by this publication or by any reuse of it; the methods described herein — including the single-token class-discriminant encoder, its multi-token product-quantization variant, and its unsupervised nearest-centroid-distance monitoring mode — are the subject of filed and pending U.S. patent applications, and attribution under CC-BY does not extend to those rights. Inquiries regarding licensing of the encoding methods may be directed to randolphf@fieldstoneanalyticsllc.com. Companion deposits Part of the single-token class-discriminant codebook family on Zenodo (community: spiral-domain-encoder-campaign), which includes the single-token industrial sensor substrate (doi: 10.5281/zenodo.20854722), the hardening-and-generality characterization (doi: 10.5281/zenodo.20802759), and the deterministic multi-token token-ladder (doi: 10.5281/zenodo.22003179). This deposit applies the same previously described encoding method to a new input domain — financial data streams. Keywords decision-preserving compression, class-discriminant codebook, market-stress detection, contagion and correlation regime, systemic risk, volatility regime, VIX, anomaly detection, edge computing, pre-registration, honest negatives.
Omar A. Esqueda, Mohammad Sharif Karimi, Daniel P. Liston, Saleh Ghavidel Doostkouei
This paper investigates the dynamic relationship between macroeconomic factors—particularly Bitcoin pricing—and the equity returns of firms in the financial technology (FinTech) sector. Using a Structural Vector Autoregression (SVAR) framework with daily data from July 2013 to March 2025, the analysis examines how shocks in major financial variables affect FinTech equity performance. The results indicate that positive shocks to the S&P 500 index are associated with a significant increase in the FinTech sector indicator, underscoring the sector’s close linkage with overall equity market performance. Shocks to the 10-year U.S. Treasury bond yield also generate a positive but comparatively weaker and delayed response, suggesting a secondary influence of interest rate dynamics. In contrast, Bitcoin price shocks do not produce a statistically significant effect on FinTech returns, implying limited spillovers from cryptocurrency markets to traditional FinTech equities. Robustness checks using PARCH and TARCH models confirm the stability of these findings. Overall, the evidence suggests that FinTech firms remain more sensitive to developments in conventional financial markets than to movements in digital asset prices, highlighting the sector’s growing integration with institutional finance rather than speculative crypto-based activity.
Muhammad Asim - Global Progress Volunteer Muhammad Asim - Global Progress Volunteer
UUI – UNIVERSAL UNIQUE IDENTITY (Enhanced) A Privacy-Preserving and Globally Interoperable Framework for Universal Digital Identity A Conceptual Research Framework for Inclusive Identity, Trusted Verification, Human Mobility and Digital Governance (Idea 2 & 32) Muhammad Asim – Global Progress VolunteerIndependent ResearcherORCID: 0000-0002-8575-4447 Abstract Identity is a fundamental requirement for participation in modern economic, social, governmental and digital life. Yet approximately 800 million people worldwide still lack official identification, while at least 2.8 billion people do not have access to a government-recognized digital identity capable of supporting secure online transactions. Existing identity ecosystems are also frequently fragmented across national jurisdictions, institutions, technologies and legal frameworks. This paper proposes the Universal Unique Identity (UUI) Framework, a conceptual model for a secure, privacy-preserving and globally interoperable identity ecosystem. UUI does not seek to replace national identity systems, citizenship, passports or sovereign authority. Instead, it proposes an additional interoperability layer through which authorized identity claims could be securely verified across participating jurisdictions. The framework integrates privacy-by-design, cryptographic verification, interoperable identity standards, artificial-intelligence-assisted verification, distributed technologies, cybersecurity, selective disclosure, consent mechanisms and independent ethical governance. The proposed architecture deliberately avoids assuming that a universal identity system should require a single centralized global database. Instead, it emphasizes federated and interoperable approaches in which identity information remains appropriately controlled by authorized entities while verifiable claims can be exchanged across trusted systems. The paper develops the author's original Global Identification concept introduced in 2016 and the subsequent UUI – Universal Unique Identity concept published in 2025. The present manuscript substantially expands those earlier works by incorporating contemporary digital identity principles, international identity-management standards, privacy safeguards, governance requirements, cybersecurity considerations, implementation stages, limitations and future research directions. The paper argues that a globally interoperable identity layer could potentially reduce identity fragmentation, improve trusted verification, facilitate inclusion and support legitimate cross-border activities. However, such a system would require rigorous safeguards against surveillance, discrimination, exclusion, unauthorized profiling, cyberattack and misuse of personal information. Keywords: Universal Unique Identity; UUI; Global Identification; Digital Identity; Identity Interoperability; Identity Management; Privacy by Design; Artificial Intelligence; Blockchain; Cybersecurity; Digital Inclusion; Human Rights; Global Governance; Verifiable Credentials
Smart contract deployment costs constitute an economic consideration in blockchain ecosystems, yet existing gas optimization approaches primarily focus on execution efficiency while neglecting deployment gas reduction. We analyze Solidity storage internals and identify that conventional variable packing–despite reducing storage slots–often increases deployment costs due to compiler-generated masking and shifting operations. This finding motivates State Variable Mutation , the first systematic approach designed to reduce smart contract deployment costs through guided reordering of state variable declarations. Our approach explores variable orderings to identify layouts that minimize gas-expensive storage operations while preserving semantic equivalence and storage efficiency. We implement our approach in DGRed , an open-source tool, and evaluate it on 300 real-world smart contracts. Results demonstrate deployment gas reductions of up to 32.72%, with an average reduction of 15.65% (52,850 gas units per contract), translating to total savings of 15,854,883 gas units across all contracts. Under high network congestion (200 gwei), these savings correspond to $12,381.4. Compared with state-of-the-art gas optimization tools, DGRed achieves superior deployment gas reductions (15.65% average vs. 4.91% for GasSaver and 2.83% for GASOL) while maintaining 100% semantic preservation and producing valid bytecode for all 300 contracts. In contrast, GasSaver introduces compilation errors in 148 contracts, while GASOL generates invalid bytecode in 234. DGRed 's state variable mutation produces identical execution gas to the original contract in all 100 contracts evaluated for execution gas impact, confirming that deployment optimization does not affect runtime efficiency. Additionally, DGRed 's 15.65% reduction is over 8 \(\times\) larger than the best achievable through Solidity compiler flag tuning alone, demonstrating that the two approaches are complementary. DGRed provides developers with a practical, semantic-preserving solution for deployment cost optimization without modifying contract logic or functionality. Because DGRed only reorders state variable declarations, it introduces no runtime trade-off, and entails no risk of behavioral regression.
Tokenized real-world assets trade continuously on public blockchains, but thevenues that price their underlyings do not. For roughly two-thirds of wall-clocktime, an on-chain protocol must value collateral against a market that is shut. This record accompanies "Coverage Inversion: Calibration-Transparent Fair-ValueOracles for Closed-Market Hours". The paper inverts the conventionalpoint-plus-confidence oracle interface: the target coverage level tau becomes apublished product input, and every served price band carries a calibrationreceipt that a third party can reconstruct from public data. CONTENTS The paper (67 pages) and the LaTeX source arXiv compiles. A reference implementation in three languages — the Python serving path, its Rust port (pinned to the Python by a 329-case parity harness), and the Anchor programs for the on-chain publish path. The calibration artefacts: the 20 deployment scalars that define the served bands, including the SHA-256-stamped frozen artefact used for out-of-sample validation. The public band archive: an append-only record of bands actually served, with Saturday width commitments published before Monday's open, so the claims can be audited after the fact rather than taken on trust. EVIDENCE Two closed-market panels over the same ten US-listed tickers, 2014-2026: 5,996 weekend windows (Friday close to Monday open) and 22,624 overnight windows (close to next open). The headline weekend result is held out by leave-one-symbol-out cross-validation at tau = 0.95: realised coverage 0.9497 +/- 0.0128, every fold passing Kupiec. On a 40-cell symbol-by-tau grid the architecture passes 40 of 40 Kupiec tests, against 31 of 40 for the strongest practitioner baseline (GARCH-t). The same architecture, with only its gap selector changed, carries from weekends to overnight gaps — calibration transparency is a property of closed market hours generally, not of weekends specifically. Because an earnings release has a publicly known date and session, the band widens deterministically ahead of it. No incumbent oracle exposes calendar-conditioned coverage. LICENSING This record is CC BY 4.0. The source code in the reference-implementation archive is Apache-2.0 and ships its own LICENSE file, which governs that archive.
The convergence of AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral methods constitutes a decisive research direction for next-generation logistics, especially when examined through the lens of Smart Mobility and Intelligent Transportation Systems [9]. Future work must prioritize real-time fusion of connected-vehicle and smart-infrastructure data streams, construction of scalable multi-resolution Digital Twin environments that span both supply chains and urban mobility networks, development of trustworthy AI models for predictive and prescriptive control, realization of practical hybrid quantum–classical optimizers for the combinatorial problems that dominate intelligent transportation and logistics, and computationally efficient multi-scale spectral analysis of complex temporal dynamics. Empirical validation across transportation, inventory, energy, and disruption scenarios incorporating advances in battery technologies [5], [10], solar-assisted and hybrid vehicle architectures [12], [28], and Industry 5.0 human-centric automation [27]will be indispensable. Realizing these advances will transform intelligent logistics from a conceptual integration into operational, sustainable, and resilient supply-chain systems that fully exploit the emerging capabilities of smart mobility ecosystems [1]–[28].
There are several types of multimodal data such as text, image, audio, video, and sensor streams. It is evolving at a very high rate, which has resulted in not only complicated security issues but also enormous potential opportunities in advanced analytics and intelligent applications. The significant security issues in multimodal data management are analyzed in this chapter, such as data integrity, data confidentiality, authentication, access control, and privacy protection. Multimodal data is vulnerable to attacks such as adversarial attacks, data breaches, unauthorized access, and cross-modal inference, and due to its dispersed and heterogeneous nature, such attacks can compromise sensitive information. The chapter examines diverse security controls to address these challenges, such as safe multimodal fusion, blockchain architecture, cryptography, federated learning, differential privacy, and robust authentication processes. It is concentrated on scalable and real-time protection methods that are effective in edge-cloud designs and big data environments. This combination of approaches will enable businesses to ensure dependable multimodal data analytics that will safeguard user privacy and system reliability and allow making safe and efficient decisions in any field of application.
A convergent supply-chain architecture is proposed by integrating Digital Twins with Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), quantum computing, and FKF spectral analysis to address the growing complexity, uncertainty, and disruption risks in modern logistics. Digital Twins enable real-time virtual representation, simulation, monitoring, and disruption-response analysis, while AI extracts predictive and prescriptive intelligence from continuous IoT-generated data. Blockchain strengthens data integrity, transparency, security, and end-to-end traceability across supply-chain transactions. Quantum annealing supports computationally intensive logistics optimization problems, including routing, resource allocation, scheduling, and ULD configuration. FKF-based spectral features provide a mathematical representation of shifts, modulation effects, lead-time behavior, transient variations, and multiscale supply-chain dynamics. The integration of these complementary technologies enables information to flow from real-time sensing and trusted data management to spectral analysis, predictive intelligence, simulation, and advanced optimization. Together, the proposed architecture provides an adaptive, intelligent, sustainable, and resilient framework for monitoring supply-chain conditions, anticipating disruptions, evaluating alternative decisions, and improving overall logistics performance.
The convergence of artificial intelligence, cryptocurrency, and blockchain has created a communication crisis: professionals must navigate fragmented applications, wallets, agents, and protocols to accomplish what should be a single action. KHALL KIIâ„¢ introduces a voice-first communication instrument built natively for the programmable economy. KHALL KIIâ„¢ is the consumer-facing communication layer of the KHEMONAUTICS antientropic ecosystem. It combines the Kryptophonâ„¢ language for programmable value, the Khonverâ„¢ universal interoperability protocol, the Khotorâ„¢ computational motor, and the Khounterâ„¢ proof standard into a single, radically simple human interface. The fundamental interaction is Press. Speak. Release. The system understands intent, routes communication, verifies identity, executes authorized actions, preserves memory, and issues cryptographic proof across humans, AI agents, digital assets, and blockchain networks. This paper establishes the complete architecture, product family, vocabulary, hardware tiers, software platform, business model, legal framework, and intellectual property strategy for KHALL KIIâ„¢. Every claim is scoped to what is specified and what is designed; implementation status is clearly distinguished from specification status throughout.
This article examines cryptocurrency adoption in the Bicol Region of the Philippines through 14 months of multisited ethnography with the Bicol Blockchain Community (BBC) and three national government agencies. Against libertarian narratives framing blockchain as a tool of financial emancipation, the Bicol case reveals institutional absorption: the incorporation of a nominally anti-statist technology into existing hierarchies of governance, credentialing and capital accumulation. While agencies and community entrepreneurs forged mutually beneficial alliances, material and symbolic benefits accrued primarily to those with prior educational and economic advantages. Extending domestication theory and scholarship on techno-politics, the study develops institutional absorption as a concept for the cultural studies of technology: a culturally constituted process through which digital disruption is translated into legible, governable and hierarchical form. Rather than a universal account of the Global South, the concept offers a core analytical perspective for remittance-dependent, climate-vulnerable peripheral regions, with boundary conditions specified for comparative testing.
This study proposes a convergent framework integrating AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral analysis for sustainable and resilient supply chains. AI supports prediction and optimization, Blockchain improves transparency and traceability, IoT enables real-time sensing, and Digital Twins facilitate simulation and adaptive disruption management. Quantum annealing addresses selected combinatorial logistics problems, including ULD configuration, while FKF methods characterize temporal shifts, modulation, multiscale dynamics, and lead-time variations. The integration establishes a closed-loop architecture linking sensing, spectral analysis, intelligence, simulation, trust, and optimization for adaptive logistics decision-making.
Aasem N. Alyahya, Muidh Awadh Algahtani, Amani Ibraheem, Naglaa F. Soliman · 8 authors
Industry 4.0 is evolving rapidly, 6G networks are emerging, and this has led to a dramatic increase in ultra-latency-critical, computationally demanding jobs in Industrial Internet of Things (IIoT) environments such as real-time digital twins, collaborative robots, and augmented reality-guided assembly. However, the conventional D2D-assisted mobile edge computing (MEC) systems suffer from the severe performance degradation due to the harsh factory propagation environment, severe security threats on the open D2D links, strict industrial data privacy requirements, selfish resource sharing behaviour, and frequent service migration due to device mobility. In this research, we propose a holistic secure task co-offloading system that integrates Reconfigurable Intelligent Surfaces (RIS), permissioned blockchain with smart contracts, and federated learning, into an integrated D2D-MEC architecture for the IIoT. A federated secure multi-armed bandit algorithm enables privacy-preserving decentralized decision-making without revealing sensitive industrial data. Blockchain records off-load transactions through immutable ledgers and enables smart-contract-based incentive enforcement. RIS renders unreliable wireless channels dynamic with minimum energy overhead. In scenarios with hostile and imprecise information, the collaborative design can lower the long-term cost of the system, including task latency, energy consumption, migration overhead, and blockchain transaction fees. The proposed framework, compared to state-of-the-art baselines, reduces the average job completion latency by 29.6%, energy consumption by 36.8%, and migration cost by 41.2%, as demonstrated by extensive trace-driven simulations in actual 6G-IIoT manufacturing scenarios. The experimental results also show the robust performance under the simulated willingness manipulation and poisoned federated updates. The permissioned blockchain architecture provides the architectural security against the Sybil attack and other trust-related threats.
In the current days with the growth of communication systems, the Internet of Things (IoT) has become a famous mechanism that allows large systems to be allowed with connectivity with heterogeneous frameworks. Nevertheless, it exists with technical complexity in the existing networks to manage certain massive systems in an effective way. Nowadays, the Software Defined Network (SDN) method with its elasticity and agility has been integrated with IoT to face the powerful flexibility and scale demands and create a novel IoT framework. Effective routing models with high security and low latency are needed, as the SDN-IoT architecture’s size is enhanced. However, the existing SDN routing models are still suspicious of flow control’s dynamic change, more importantly when the network is under threat. The IoT systems are normally performed in unattended and hostile environments. In addition, the routing in the present IoT framework becomes ineffective because of the existence of unauthenticated and malicious nodes, insecure routing, minimum network lifespan, and so on. In order to manage these problems, this work designs an effective SDN routing strategy-enabled IoT system with a blockchain mechanism to prevent malicious threats during data transmission. The deep learning strategy is supportive for recognizing suspicious IoT devices based on each node’s energy features. This article performs two significant tasks including the identification of malicious nodes and the selection of the optimal path. At first, the data of the IoT node is stored in the blockchain since the nodes in the IoT have a constrained lifetime. In addition, the nodes are validated to verify the authentication by applying a smart contract. The Cascaded Dilated Recurrent Neural Network (CD-RNN) is employed for recognizing the malicious and trusted nodes of the network. After recognizing the malicious node, the selection of the optimal route is carried out. In this, the routes are chosen optimally by the Transitive Phase of Pelican Optimization (TPPO). Lastly, the estimation is conducted by considering some factors including security, Packet Delivery Ratio (PDR), delay, and throughput. Hence, the suggested system offers better functionality than the previous approaches. The suggested scheme presents a hybrid mechanism, which combines a CD-RNN-based malicious node detection with TPPO-based routing optimization and blockchain-based trust management that guarantee a high level of security and performance in SDN-IoT settings.
Security in resource-constrained IoT deployments remains a persistent challenge: devices used in industrial control, smart healthcare, and transportation must authenticate quickly, consume minimal energy, and resist physical attacks — yet existing protocols rarely address all three requirements at once. To the best of current knowledge, no prior protocol jointly optimises security, energy, and latency within a single formally verified framework. This paper presents OPAQUE-IoT, an Optimization-driven PUF-Blockchain AKA Protocol for constrained IoT networks. The framework integrates PUF-based hardware identity verification, a permissioned blockchain for decentralized trust management, and the Adaptive Security-Energy Trade-off Optimizer (ASETO), which jointly minimizes authentication latency and energy consumption under formal security constraints. Convergence of ASETO is proven under Lipschitz-continuous objective functions. Formal security analysis under the Real-or-Random (RoR) model with explicit Random Oracle and ECDH hardness assumptions demonstrates resistance to replay, impersonation, man-in-the-middle, PUF modeling, insider, and side-channel attacks, with a security advantage bound of approximately 2^(-68). Simulation results across heterogeneous IoT topologies ( N = 50 to 5000 devices) show 31.8% lower energy consumption, 30.2% reduced authentication latency, and 41.1% higher throughput compared to the best-performing blockchain-capable baseline, with O(log N) Merkle-indexed blockchain query complexity and O(T_max·N·P) per-epoch optimiser complexity.
Abstract In the era of artificial intelligence, the AI-generated image (AIGI) market is an emerging sector that faces significant challenges related to ownership, privacy, and security. These issues, especially prevalent in NFT markets, can be effectively addressed by the integration of advanced technologies such as blockchain, the InterPlanetary File System (IPFS), and Quantum Key Distribution (QKD). This study proposes a comprehensive trading framework that incorporates state-of-the-art methodologies and algorithms to simulate the entire AI image trading process. For image generation, the framework utilises diffusion models (LCM-LoRA + SDXL) and Generative Adversarial Networks (GANs), employing LCM-LoRA and LCMScheduler from Stable Diffusion XL Base 1.0 to accelerate image generation and reduce inference steps. Implementation is carried out using PyTorch and the Diffusers library, running on a CUDA-enabled GPU. The generated images are securely stored in the distributed IPFS storage system, while decentralised trading is facilitated through integration with the Hyperledger MiniFab tool. The framework supports multiple trading mechanisms, including Blind English Sealed-Bid Auctions (BESEA), fixed-price auctions with a first-come, first-served (FCFS) model, Dutch auctions with royalty redistribution, and fractionalized auction trading. To ensure secure communication between buyers and sellers, the BB84 QKD protocol is employed to generate a shared secret key with information-theoretic security. The generated key is processed through key sifting to derive a symmetric key, which is zero-padded to the 256-bit length required by AES-256-CBC. It is then directly used as the encryption key to encrypt AI-generated images and their associated metadata before storage on IPFS. Experimental evaluation across four auction mechanisms and up to 250 NFTs shows the Dutch auction achieving the highest sales volume (up to 211 NFTs sold) and the Fractionalized auction the highest revenue ( 98,270). The IPFS storage maintains sub-0.75-second upload latency with 100% file verification success. Blockchain-layer benchmarking across 100–500 participants records mean chaincode latency ranging from approximately 22 to 53 seconds and throughput of 0.018–0.045 TPS.
Abstract This manuscript per the authors addresses secure software update delivery for Internet of Things (IoT) devices. Existing IoT update mechanisms suffer from centralized trust dependency, attribute privacy leakage, and limited scalability. This manuscript proposes ZBA-SU (Zero-Knowledge Blockchain Attribute-Based Software Update), a methodology integrating zero-knowledge proof (ZKP), blockchain smart contracts, and ciphertext-policy attribute-based encryption (CP-ABE). The proposed framework achieves privacy-preserving device attribute verification via ZKP, tamper-proof atomic update delivery via blockchain, and fine-grained access control via CP-ABE. Experimental results on Raspberry Pi, ESP32, and Ethereum private chain show ZKP generation time of 1.8 seconds, attribute leakage reduction of 97%, verification within 3 seconds, and throughput improvement of 40%. Security analysis confirms resistance against attribute forgery and proof replay attacks. ZBA-SU provides effective security enhancement for IoT software updates in smart cities and industrial IoT.
The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain.