Admission control governs Quality of Service (QoS) in cognitive IoT networks (CIoTNs) in which secondary nodes opportunistically share spectrum with preemptive primary users. The optimal policy is of threshold type, but the threshold table is indexed by the 2N fully occupied channel configurations, so both model-free learning and on-chain enforcement scale poorly in N. We show that this table has low intrinsic complexity: across 55 randomised continuous-time Markov decision process (CTMDP) instances, a monotone step function of the aggregate secondary-user drain rate σ(n) carrying only 1.6–2.3 distinct levels reproduces 81–87% of the 2N table entries exactly, the remainder erring by a single queue slot. We give a quasi-static argument for why σ(n) is the right scalar summary. Using this regularity as an inductive bias, an index-pooled Q-learning read-out reduces discounted policy-value loss by up to 4.4× (1.55% to 0.36% at N=4, six seeds) at the largest sample budget, but adds variance and was worse than the per-configuration baseline in one of eight cells; we therefore also specify a deployment gate and report it as untested. We further report a negative result: projecting learned value differences onto the concave cone guaranteed by the structural theorem is inert at N=4 and yields at most a 0.22 percentage-point gain at N=3, because recovery is limited by configuration coverage rather than by shape violation. Finally, we implement Service Level Agreement (SLA) enforcement as a Solidity contract and measure it on a local Ethereum Virtual Machine. Index compression cuts policy-installation gas by 241.8× at N=16 (48.79M to 0.20M gas) and keeps installation in one transaction, but raises the per-decision cost by 14.4–18.4k gas; it is therefore a feasibility mechanism for large N and for frequently re-committed policies, not a uniform improvement.
This paper investigates the concept of "commitment lifetime" within the context of decentralized finance (DeFi) protocols, specifically focusing on the variability in the duration a user's commitment to a collateralized asset remains valid. The commitment lifetime, denoted as *LC(P)*, is defined as the difference between the release time and the birth time of the commitment, where *trelease* and *tbirth* represent the respective timestamps. The core objective is to analyze the probability distribution of commitment lifetimes, denoted as *P(LC > t)*, for different algorithmic approaches used in managing collateralized positions. We demonstrate that despite potentially differing underlying mechanisms, various DeFi protocols can arrive at identical conclusions regarding commitment lifetimes. This highlights the importance of understanding the mathematical representation of commitment lifetime distributions, which we term the "commitment lifetime distribution." This analysis provides a foundational understanding for risk management and parameter optimization within DeFi systems.
The evolution of autonomous systems is reverberating through all industry sectors. Autonomous Industrial Systems represent the evolution of the current “Automated Industry,” towards systems that operate independently, supported by continuous edge decision making and learning. Beyond control, orchestration of complex industrial-level processes can enable large-scale decision making based on a continuous market and regulations signals: without a centralized operator, such processes can operate independently. Autonomy also extends to “Smart Infrastructures” that enable the smart detection and connectivity of constituents and their connections to support the edge processing and decentralized decision making. The self-organization characteristics of these systems shall drive the market towards more the large-scale deployment of both connecting infrastructures and industry. The cross-regulatory nature of these systems requires an orchestration approach for the provision of interdependent services from numerous operators along a supply chain. Therefore, the direction of future development lies in the growing autonomy of the economy and the evolving edge-driven digital connection of smart infrastructures, service production, and regulation of the resources and ecosystem on which the digital representation relies.
Proof-of-Stake DAG-BFT consensus faces a trilemma between sybil resistance, reward fairness, and plutocracy. Existing protocols prioritize liveness over fair stake-based selection, driving longitudinal centralization. FairWave is a dual-channel DAG-BFT protocol that separates anchor selection from reward distribution. The selection channel is super-linear in stake, guaranteeing Sybil gain < 1 for K > 1; the reward channel is sub-linear via square-root stake normalization. DAG-derived uptime and latency metrics eliminate external oracles,and lagged reputation breaks circular dependency between selection outcomes and weights. Evaluated through approximately 550,000 Monte Carlo rounds against eight baselines, FairWave shows Gini 0.140 (vs. Pure-PoS 0.490, monotone HHI reduction from 0.039 to 0.020 over 50,000 epochs, and optimal Sybil split K * = 1. Safety follows unconditionally from the 2f + 1 commit rule; the liveness model predicts monotone degradation from 94.0% at b = 0.20 to 74.0% at b = 1/3, consistent with the architectural expectation of no discontinuous cliff.
The European Commission's April 2026 age verification framework, built on software-based Zero-Knowledge Proofs (ZKP) atop the European Digital Identity (EUDI) Wallet, fails to achieve its stated privacy guarantees due to a structural enrollment binding problem: any ZKP scheme whose trust root is a government identity credential inherits that credential's linkability at the point of issuance. This paper proposes a replacement architecture based on hardware bearer credentials — physically issued FIDO2 tokens whose identity binding is discarded immediately after issuance — combined with an anonymous hardware-handle revocation list, offline kiosk enrollment, and a self-funding economic model. The proposal is technically feasible with current production technology, financially viable at EU procurement scale, and operationally self-sustaining through a €10 citizen co-payment at issuance plus a €30 replacement fee. A cost model for national deployment using Italy as a case study demonstrates that the system requires near-zero net public expenditure. The primary novel contribution is a game-theoretic mechanism embedded in the replacement fee structure that renders secondary market trading of credentials economically irrational without requiring any surveillance of credential holders.
Vehicle trajectory prediction in Internet-of-Vehicles requires collaborative learning over sensitive trajectories under intermittent connectivity and partially trusted participants. ChainDrive-FL-VRA coordinates semi-asynchronous federated learning on a permissioned consortium ledger using Practical Byzantine Fault Tolerance (PBFT), while keeping raw trajectories and raw model-update tensors off-chain. Each client submits an on-chain header containing a commitment and hash of the local update, together with zero-knowledge proofs that certify [Formula: see text]clipping and anchor-consistency. Validators admit only proof-checked updates, compute staleness- and reputation-aware robust weights, and publish a proof of correct aggregation that binds the aggregation commitment and the committed global model hash to the admitted committed updates under fixed-point weights. A contextual-bandit trigger selects aggregation timing under client churn. Experiments on NGSIM US-101 and I-80 show improved ADE/FDE/RMSE and improved robustness under staleness and anomalous updates, while on-chain artifacts remain at kilobyte scale per update and per aggregation event.
This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.
Muhammad Umar Farooq Qaisar, Weijie Yuan, Lin Zhang, Shehzad Ashraf Chaudhry · 6 authors
Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.
Edison A. Arteaga López, Gustavo A. Ramírez González, Carlos Alberto Astudillo
The growth of the Internet of Things (IoT) has highlighted the limitations of centralized data platforms, particularly in terms of security and scalability. Distributed Ledger Technologies (DLT) offer a solution, but traditional DLT architectures, such as blockchain, are often incompatible with low-power wireless IoT networks (LPWAN) due to their latency and operational cost. This paper presents a comparative and empirical performance analysis of two end-to-end data oracle systems designed to record data from a LoRaWAN sensor network. The first implementation uses IOTA Tangle, while the second is based on a blockchain compatible with the Ethereum Virtual Machine (EVM). By evaluating key indicators such as latency and transaction costs, our results demonstrate that the IOTA system offers significant superiority, with a predictable median latency of 4.47 seconds and transaction costs that are economically negligible. In contrast, the blockchain implementation incurred measurable gas costs and demonstrated an architecture with inherently higher and extremely unpredictable latency, with a median of 11.52 seconds and outliers exceeding 200 seconds. We conclude that the IOTA Tangle architecture is technologically and economically better prepared to support scalable and sustainable IoT applications over wireless infrastructures.
Bo-Sian Liao, Jung‐Shian Li, I‐Hsien Liu, Chuan-Kang Liu
Federated Learning (FL) has emerged as an innovative paradigm that enables heterogeneous and geographically distributed clients to collaboratively train models in a decentralized and privacy-preserving manner. However, FL systems face numerous challenges in real-world deployments, particularly passive participation caused by malicious attacks, where clients drop out due to attacks. This issue, though not intentionally designed by the system, significantly impacts training stability. In this study, we propose BAHA-FL (Blockchain-based Adaptive Historical Averaging Federated Learning. Our approach integrates adaptive historical averaging with exponential decay weighting to effectively compensate for missing parameters due to client dropouts. Our blockchainbased solution ensures the immutability and traceability of model update records, leveraging Distributed Ledger Technology (DLT) to maintain model integrity.
Nicholas Potteiger, Mudassir Shabbir, Scott Eisele, Mark Wutka · 5 authors
Networked Unmanned Aerial Vehicles (UAVs) can be used for complex tasks such as surveillance and reconnaissance, inspection of dangerous environments, and target pursuit. Typically, coordination between multiple UAVs has been shown to improve the ability and performance of accomplishing such tasks. However, networked UAVs introduce new vulnerabilities enabling cyber-attacks which can target critical elements and prevent the UAVs from achieving their goal. This chapter presents a distributed system architecture for coordination of UAVs that provides resilience against denial-of-service and integrity cyber-attacks. The developed architecture consists of a distributed ledger implementing an asynchronous Byzantine fault tolerant protocol exchanging data between distributed agents and a distributed learning algorithm based on vector consensus implemented on top of the distributed ledger. Performance and resilience of the architecture are evaluated using a target pursuit case study based on a hardware-in-the-loop testbed. The experimental results demonstrate that that the UAVs that are not under attack are still able to successfully cooperate and accomplish the desired task. [160 words]
Distributed Control Multi-Agent Systems
Distributed Sensor Networks and Detection Algorithms
F J Pérez, Francisco J. Quesada, Luis Martı́nez, Fco Javier Estrella Liebana
Purpose Integrating Internet of Things (IoT) networks with distributed ledger technology (DLT) and artificial intelligence (AI) presents critical challenges, particularly related to latency, scalability, hardware constraints and data security. Efficient data ingestion and validation are essential to enable real-time AI processing. The main contribution of this paper is the proposal of the Energy consensus algorithm, designed to minimize both latency and energy consumption in such environments. Design/methodology/approach Energy is a consensus algorithm tailored for public directed acyclic graph-based DLTs in IoT contexts. It introduces a flexible transaction validation mechanism that reduces or bypasses Proof of Work requirements. The algorithm’s performance is experimentally compared with IOTA under varying payload conditions. Findings Results show that Energy significantly reduces latency and energy consumption, especially for small payloads, which are common in IoT applications. These findings demonstrate Energy’s ability to enhance transaction efficiency and support real-time AI model updates based on verified IoT data streams. Research limitations/implications Future work should investigate the scalability of Energy in larger and more heterogeneous IoT ecosystems, as well as its compatibility with different AI frameworks. Evaluating its performance under diverse network conditions and hardware setups would further strengthen the generalizability of the results. Practical implications The Energy algorithm enables continuous AI model updates while ensuring data integrity, traceability and low latency. Its adaptability makes it a suitable solution for large-scale IoT deployments requiring secure and efficient data processing. Originality/value This paper presents a novel consensus algorithm that bridges the requirements of IoT, DLT and AI, with a particular focus on improving latency and energy efficiency. Energy offers a robust approach for optimizing data flow and transaction processing in real-time, AI-driven IoT systems.
The Web of Things (WoT) represents an interconnected network of sensors and smart devices. It faces significant security and privacy challenges, including potential data breaches, unauthorized access issues, and concerning scalability problems. This paper introduces a blockchain-based solution that aims to enhance the security and reliability of the WoT. Taking advantage of the inherent transparency of blockchain's distributed ledger, the elimination of single points of failure through consensus algorithms, the enforcement of access control and privacy through smart contracts, and the assurance of data confidentiality through encryption, I propose an innovative design for the WoT that could guarantee enhanced security and trust. I also incorporate the use of a homomorphic hash function to further boost data privacy and integrity. This paper presents a comprehensive framework that employs blockchain technology to transform the current WoT into a more secure and trusted network, which I term the “Web of Trust”.
The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.
With the wide application of electric vehicles, smart robots and Internet of Things (IoT) devices, efficient scheduling of mobile charging systems has become an important research direction in smart energy management. However, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high reliability and privacy protection, and the existing scheduling strategies still have challenges in terms of energy optimization, task balancing and dynamic adaptability. To this end, this paper proposes an intelligent mobile charging scheduling method that integrates edge computing and biomechanical modeling, constructs a biomechanical-based charging demand modeling and energy consumption analysis framework, and combines bionic optimization algorithms to achieve efficient path planning. Meanwhile, an edge computing architecture is adopted to optimize resource scheduling, and a federated learning mechanism is designed to enhance cross-domain data processing capability. To safeguard user privacy, a multi-level privacy protection mechanism is proposed, combining differential privacy, homomorphic encryption and zero-knowledge proof to ensure data security. Experimental results show that the method outperforms traditional methods in terms of task response time, energy consumption optimization, load balancing and privacy security, and can significantly improve the charging scheduling efficiency and provide effective technical support for large-scale distributed charging networks. The research results provide a theoretical basis and engineering practice reference for the application of smart charging networks, edge intelligent computing and privacy protection technology.
Federated Learning (FL) has emerged as a transformative approach to distributed machine learning, enabling the collaborative training of models across decentralized and private datasets. Unlike traditional centralized learning paradigms, FL ensures data privacy by keeping raw data localized on client devices while leveraging aggregated updates to build global models. This survey explores the critical aspects of efficient federated learning, including communication reduction, robustness to system and data heterogeneity, and scalability in real-world applications. We discuss key techniques such as model compression, asynchronous updates, personalized learning, and robust aggregation to address challenges posed by resource-constrained devices, non-IID data distributions, and adversarial environments. Applications of FL across diverse domains, including healthcare, finance, smart cities, and autonomous systems, highlight its potential to transform industries while preserving privacy and compliance with regulatory frameworks. The survey also identifies open challenges in scalability, privacy guarantees, fairness, and ethical considerations, providing future research directions to address these gaps. As FL continues to evolve, it holds the promise of enabling privacy-preserving, collaborative intelligence on a global scale, fostering innovation while addressing critical societal and technical challenges.
Large quantities of processing resources with strict latency specifications are needed for Internet ofThings (IoT) devices due to the rise of compute-intensive and delay-sensitive mobile apps. One promising solutionis to transfer resource-intensive computational tasks from IoT devices to either edge computing servers or cloud computing servers. This paper aims to apply a simplified distributed ledger to an edge network to follow up the offloaded data and maintain the response time as much as possible. The voting process is used as a consensus to validate the new block, while the offloading decision is based on a fixed processing time offloading threshold value. The proposed model has been programmed and the experimental evaluation of the proposed model shows that the ledger did not significantly lengthen the response time and the offloaded task has been successfully tracked
Andreas Polyvios Delladetsimas, Stamatis Papangelou, Elias Iosif, George M. Giaglis
This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application’s needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment.
Yaakov Sokolik, Mohammad Nassar, Ori Rottenstreich
In blockchain networks, transaction latency is crucial for determining the quality of service (QoS). The latency of a transaction is measured as the time between its issuance and its inclusion in a block in the chain. A block proposer often prioritizes transactions with higher fees or transactions from accounts it is associated with, to minimize their latencies. To maintain fairness among transactions, a block proposer is expected to select the included transactions randomly. The random selection might cause some transactions to experience high latency following the variance in the time a transaction waits until it is selected. We suggest an alternative, age-aware approach towards fairness so that transaction priority is increased upon observing a large waiting time. We explain that a challenge with this approach is that the age of a transaction is not absolute due to transaction propagation. Moreover, a node might present its transactions as older to obtain priority. We describe a new technique to enforce a fair block selection while prioritizing transactions that observed high latency. The technique is based on various declaration schemes in which a node declares its pending transactions, providing the ability to validate transaction age. By evaluating the solutions on Ethereum data and synthetic data of various scenarios, we demonstrate the advantages of the approach under realistic conditions and understand its potential impact to maintain fairness and reduce tail latency.