Internet of Things (IoT) technologies in the healthcare industry, also known as the Internet of Medical Things (IoMT), have proven to greatly improve patient monitoring, diagnostics, and clinical decision-making. The increasing prevalence of resource-challenged medical devices, wireless connectivity, and cloud services, however, has brought new risks around security and privacy concerns that can now directly impact patient safety and data integrity. In this paper, a thorough study of 41 peer-reviewed research papers from January 2018 through May 2025 revealed the current state of security vulnerabilities and resilience strategies in healthcare IoT systems. It provides a comprehensive analysis of security threats at the device, network, and application levels such as unauthorized access, malware and ransomware, data breaches, and denial-of-service attacks delivered in a systematic manner. This contrasts with existing surveys, which consider single security mechanisms and improve upon various multi-layered security means such as AI-enabled anomaly detection, blockchain-based authentication and auditability, low-compute cryptographic techniques, and privacy-preserving methods such as federated learning. The outcomes also show that although emerging technologies add a great deal of security and trust capabilities, issues on scalability, interoperability, deployment, and regulations are not yet fully addressed. This review highlights important knowledge gaps and offers structured knowledge and future directions for research to address the design of secure, resilient, and practically deployable IoMT architectures for real-world healthcare environments.
Abstract Internet of Things (IoT) technologies and healthcare present revolutionary chances to improve operational efficiency, patient outcomes, and tailored medication transformation. This paper thoroughly investigates IoT in healthcare using Latent Dirichlet Allocation (LDA) to spot important trends and research gaps in current work. To achieve this, researchers have comprehensively analyzed 11,586 published papers from 2006 to 2024 which are extracted from Scopus database. Researchers have identified 2, 5, and 10 key topics to define significant areas of the research. Over time, it compares research topics to show how important areas, including wearable technology, artificial intelligence-powered analytics, blockchain for safe data management, and edge computing, have evolved. The paper additionally examines important issues, including data privacy issues, lack of interoperability, and restricted inclusiveness for underprivileged communities. Emphasizing inclusivity, ethical compliance, and pragmatic implementation tactics catered to different healthcare environments, a strategy framework is suggested to help solve these difficulties. This paper helps IoT implementation in healthcare advance by giving actionable insights, particular discoveries, and future research directions, thereby opening the path for more fair, efficient, and sustainable healthcare systems.
Network slicing and resource provisioning in 6G focus on creating multiple customized virtual networks over a shared infrastructure. However, these approaches also introduce challenges, like increased architectural complexity, higher implementation costs, security vulnerabilities between slices in resource optimization across highly dynamic and heterogeneous network environments. In this work, Exponentially Tactical Unit Algorithm (ETUA) is devised for network slicing in 6G. Initially, blockchain-enabled 6G network is simulated, and the set of features, like user device type, delay rate and packet loss rate are collected from various devices. Moreover, network slicing is done by ETUA that integrates Exponentially Weighted Moving Average (EWMA) and Tactical Unit Algorithm (TUA). Finally, resource allocation is performed using Attention High-order Deep Network (AHoNet) by considering the parameters that includes bit error probability, sum rate and trust. The efficacy of ETUA is examined by bit error probability, utility and latency with 0.012, 0.950 and 0.509 Sec.
The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model.
Ahmed A. Jasim, Noor Riyadh Issa, Hisham A. Shehadeh, Fadhil Mukhlif · 6 authors
Abstract The Wireless Sensor Networks (WSNs), which are deployed in harsh environments, are extremely susceptible to localized battery exhaustion as well as intelligent inside routing threats, especially sinkhole and data falsification attacks. In this paper, we present ECHVM (Enhanced Cluster Head selection by Voting and an ECC-based Blockchain Mechanism), a novel, secure, and energy-efficient routing framework to achieve an optimal trade-off between network security and hardware resource efficiency. We present the ECHVM protocol that combines Elliptic Curve Cryptography (ECC) with a lightweight, local distributed ledger to mitigate risks of centralized authority by transferring the verification of crucial network events from a single, highly vulnerable centralized alert sink node to a decentralized, voting-based consensus among neighboring nodes. In this study, a weighted voting algorithm based on node and distance proximity metrics is applied to cluster head selection, where a 51% neighbor consensus rule is leveraged to validate local ledger transactions against malicious acts. Moreover, a local energy density and topological communication geometry-oriented energy-efficient cluster head (CH) selection algorithm is crafted to ensure balanced CH distribution in the high-density areas of the network structure so as to avoid the premature energy hole problem. Using the standard first-order radio energy model, quantitative simulations performed in MATLAB show that ECHVM achieves a malicious node detection rate of 98.2% and extends the network lifetime by 30% compared to state-of-the-art protocols (e.g., ELSO and SEC-HDT) because of proactive topology defense and rapid node sleeping. The results of statistical validation using the Wilcoxon Signed-Rank test confirm that both the reduction in energy consumption and network longevity offered by ECHVM are highly significant ( p = 0.019), justifying ECHVM as a mathematically sound, permanent, scalable security framework suitable for resource-constrained, dense Internet of Things (IoT) and smart sensing applications for next-generation communication systems.
Abstract The exponential growth of IoT networks has made the supply of trustworthy digital IDs more important than ever. Limitations in scalability, transparency, and resilience to single points of failure are some of the inherent issues with modern centralised identity management systems. These problems are exacerbated in distributed IoT systems since there is no central authority to rely on for communication and trust establishment among the many devices and various parties involved. By introducing SecureChain-ID, a system that uses blockchain technology to provide distributed identity issuance, authentication, and lifecycle management, this article aims to solve the restrictions that now exist. For resource-constrained Internet of Things (IoT) devices, the system's use of elliptic curve cryptography (ECC) provides digital signatures that are both lightweight and efficient. Credentials may be verified with zero-knowledge proofs (ZKPs) as they do not divulge any personally identifiable information. The blockchain's smart contracts streamline the authentication and registration processes, enabling the widespread agreement on the smooth addition of new administrators and devices. The persistent documentation of all identity-related transactions ensures auditability and safeguards against manipulation. This includes updates, revocations, and access events. Unlike conventional approaches, SecureChain-ID establishes network-wide accountability and paves the way for decentralized governance to be aligned with administrative control, enabling flexible permissioning. Many Internet of Things (IoT) systems can benefit greatly from the proposed approach due to its emphasis on data security, low trust assumptions, and interoperability. This research successfully bridges the conceptual gaps between blockchain, identity, and Internet of Things (IoT) systems by introducing an efficient design that improves future cyber-physical systems' secure identification infrastructure.
The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.
E-Healthcare Systems (EHS) are transforming medical service delivery by enabling real-time data sharing, remote diagnostics, and integrated care via IoT and cloud infrastructures.However, the increasing volume of sensitive medical data being transmitted over distributed systems creates serious privacy and security concerns.This article reviews several papers on the EHS Data Privacy Framework, addressing critical issues such as illegal data access, identity exposure, and data integrity breaches.This review examines the existing data privacy frameworks used in EHS, focusing on four domains: traditional EHS, cloud-based EHS, IoT-based EHS and blockchain-based EHS with an emphasis on author, year, objective, and limitation.This framework provides a scalable and interoperable approach to protecting privacy for future healthcare systems.
Validation of a secure blockchain architecture's effectiveness and robustness may be achieved via a methodical process that involves comprehensive testing and implementation in real-world environments. The testing process includes analyzing requirements, setting up the environment, and conducting detailed evaluations. All aspects of the smart contract, from its logic and functionality to its defences against common attack vectors like Sybil and re-entrancy vulnerabilities, are evaluated in these reports. Performance testing under different loads and realistic network conditions is essential for assessing scalability, latency, and throughput, in addition to API and integration testing, which ensure that system components operate well together. System resilience to defects and hostile events is tracked, critical test cases are automated, and large-scale peer-to-peer networks are modelled to evaluate the framework's robustness further. Supply chain verification and clinical trial administration are two examples of real-world applications of blockchain technology that demonstrate its ability to secure sensitive activities on a large scale. These use cases also give light on the system's efficacy, data integrity, and anomaly detection capabilities. Verifying the scalability, security, and reliability of a blockchain architecture against real business objectives via integrated deployments and complicated testing methods is essential for a safe blockchain.
Blockchain is becoming an approachable data platform with several stakeholders having a shared history of transactions without being owned by an individual. The fundamental concepts of cryptographic hashing, peer-to-peer communication, and agreement protocols are sufficiently documented, and a lot of current research focuses on performance, security, privacy, and governance individually rather than as interacting dimensions. Recent blockchain research publications and deployments were structured based on a four-axis perspective that follows the dynamics of a system in terms of security, scalability, privacy, and governance. This lens was applied to consent mechanisms including proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, and to techniques such as sharding and layer-2 that are designed to enhance throughput. Basic throughput calculations using reported block sizes, transaction sizes, and block intervals indicate that configuration limits are usually much larger than the transaction rates achieved in practice, implying that protocol overheads and network behaviour require a major share of the budget. A survey of attacks and defences indicates that increases in speed and programmability often expand the attack surface at the consensus and smart contract layers, which motivates the development of better analysis and monitoring tools. The results are applied to draw design insights for domains of finance, supply chains, healthcare, identity, and smart city platforms, and to highlight remaining problems in benchmarking and cross-chain coordination. A practical mapping of major blockchain platforms, namely Bitcoin, Ethereum, and Hyperledger Fabric, onto the four-axis framework was also provided to demonstrate its utility for platform comparison and selection.
Distributed ledger technology (DLT) initiatives are frequently designed from a predominantly technical and functional perspective. Such a narrow focus often overlooks stakeholder-oriented non-functional requirements, thereby limiting adoption in complex socio-technical environments. To address this gap, this paper develops the SERS framework, a stakeholder-oriented framework that structures four key dimensions for the design and evaluation of decentralized systems: Security, Efficiency, Resiliency, and Sustainability. Following a Design Science Research approach, the framework is derived from a systematic literature review and refined through expert validation using the fuzzy Delphi technique. The resulting framework provides a domain-independent typology of stakeholder-oriented performance dimensions and associated assessment criteria. To demonstrate its applicability, the framework is used to guide the design of a multi-layer architecture integrating distributed ledger technologies, IoT, and Cloud/Edge/Fog computing within a healthcare context. The architecture is subsequently evaluated using the Fuzzy Analytic Network Process (Fuzzy-ANP) based on the SERS dimensions. The results indicate that the proposed architecture performs particularly strongly in terms of security and resiliency, while highlighting efficiency and sustainability as areas requiring further improvement. Beyond healthcare, the SERS framework provides a reusable mechanism for designing and evaluating decentralized socio-technical systems across a broad range of technology adoption contexts.
Open access
IoT and Edge/Fog Computing
Mobile Crowdsensing and Crowdsourcing
Innovative Approaches in Technology and Social Development
The global healthcare supply chain is experiencing increasing challenges with respect to maintaining the security of medications, adequate storage of medications, and identifying potential issues with medications that may pose a risk to patient safety. This chapter introduces a prototype called the 'Smarter, Safe Healthcare Supply Chain.' The system brings together fast telemetry (simulating 4G and 5G), edge computing, blockchain smart contracts, and explainable anomaly detection to help stop compromised medications from reaching patients. Included in the prototype's design are an IoT temperature and location data sensing simulator; an edge service that checks the signature of a device and executes "explainable" checks; and two smart contracts associated with device tracking and alerting. Tools like latency simulation, on-chain device tracking, device allowance (i.e., allowing only those devices that have been verified via smart contracts to access the network), and audit logs allow for the prototype to demonstrate how new technologies can enhance, accelerate, and streamline operations and build trust throughout the supply chain. Some of the key results of the prototype include faster-than-anticipated response times under 5G simulated conditions, successful verification of devices (both at the edge and enterprise-level) via smart contract(s), and accurate alerting developed based on a predefined set of rules. The framework upon which the prototype is built follows the principles of Zero Trust (e.g., NIST SP 800-207A, GSMA 5G IoT Guidelines, and use case-specific Healthcare Compliance Controls). Limitations exist within the prototype (such as being a single-node blockchain and the use of rule-based alerting versus leveraging full machine learning capabilities); however, it presents a viable operational model for practical application within a regulated supply chain (e.g., pharmaceuticals). Future work will include multi-party blockchain networks, evolving AI algorithms, and demonstrating full integration of the prototype into existing regulatory workflow(s).
The convergence of agricultural digitalization and decentralized finance presents critical opportunities for mitigating carbon-related financial risks in emerging markets. However, the integrity of environmental, social, and governance reporting is frequently undermined by information asymmetries and inadequate audit trust. This paper introduces a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, blockchain-verified carbon disclosure pipeline. By deploying distributed Python middleware integrated with serverless computational nodes, the system programmatically extracts agricultural carbon intensity metrics and cross-references them against immutable blockchain ledgers. This methodology structurally eliminates manual reporting friction, providing rural credit institutions and multinational enterprises with deterministic, verifiable environmental data. Preliminary architectural evaluations confirm that integrating high-velocity Application Programming Interfaces with decentralized ledgers significantly reduces information asymmetry, establishing a highly scalable foundation for green finance and rural revitalization.
Learn more about Theta Network and its impact on the development of decentralized infrastructure via blockchain-enabled media distribution, edge computing, AI integration, and Web3 innovation. With this in-depth overview, you will gain valuable information about its technology, features, practical applications, and future perspectives, emphasizing the need for thorough research before making an investment decision. If you are interested in blockchain, then this article is for you!
Edison Andres Arteaga Lopez, Gustavo Ramírez-González, Andrea Sabbioni, Carlos A. Astudillo
Abstract Distributed ledger technologies (DLT) can enhance trust and auditability in the Internet of Things (IoT). Among them, IOTA has been specifically designed to support machine-to-machine interactions and IoT data anchoring through scalable DLT architectures. However, their integration with Low-Power Wide-Area Networks (LPWANs) remains limited due to device constraints, strict timing requirements, and the operational costs of on-chain transactions. The transition from the fee-less Stardust to the fee-based IOTA Rebased model introduces explicit transaction costs, questioning the viability of continuous IoT data anchoring. IOTA provides a suitable platform to examine the challenges of integrating distributed ledger technologies with LPWAN-based IoT systems. Its transition to a fee-based execution model raises important questions regarding cost predictability and performance in continuous data anchoring scenarios, particularly under the constraints of resource-limited and latency-sensitive environments. This article investigates the practicality of the execution and payment model introduced by IOTA Rebased for IoT scenarios requiring continuous data notarization. We provide an empirical evaluation of continuous IoT data notarization on the public IOTA Rebased Mainnet and characterize the performance implications on edge-oriented deployments, including resource-constrained and resource-rich devices. We implement a notarization oracle that ingests LoRaWAN uplinks from The Things Network (TTN), canonicalizes payloads, generates SHA-256 commitments, and records them on-chain through reusable notarization objects. The oracle enables continuous anchoring of IoT telemetry while minimizing transaction overhead through object reuse. Two 24-h experimental campaigns compare a notarization oracle on resource-constrained and resource-rich hardware under periodic workloads. Results show consistent steady-state gas consumption for UPDATE operations, indicating that object reuse enables stable on-chain cost behavior in IOTA Rebased regardless of the deployment platform. From a performance perspective, both environments achieve stable execution; however, the resource-constrained edge deployment exhibits higher median and tail latency, alongside tighter memory margins compared to the resource-rich centralized baseline. These findings confirm the feasibility of deploying notarization services on constrained edge infrastructure under the new fee-based model.
Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog-cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog-cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals λ = 0.5-2 tasks/s, random waypoint mobility 1-5 m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~ 92%, reduces energy consumption by approximately 28%, and improves latency by ~ 22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains ~ 100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving ~ 65% residual energy versus ~ 45% in conventional systems.
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
This paper proposes a universal post quantum privacy protection edge identity authentication framework to address the challenges faced by edge identity authentication in distributed cross domain networks, such as quantum attack threats, cross domain data privacy breaches, and difficulties in coordinating anonymity protection and compliance supervision. The framework adopts an optimized lattice based linkable ring signature protocol to meet the lightweight operation requirements of edge nodes and prevent the risk of leakage in identity data interaction; Design traceability constraints and controllable cross domain traceability mechanisms based on the linkability feature of signatures, balancing user privacy and regulatory requirements. Prove that the scheme possesses unforgeability, strong anonymity, and quantum resistance under the random oracle model. After optimizing the algorithm and interaction logic, the authentication efficiency is improved by 8% to 15% compared to similar solutions, and it is adapted to the low computing power and low latency characteristics of edge nodes. Combining zero knowledge proof to build a lightweight data collection mechanism and achieve privacy protection throughout the entire data process. This article uses the integrated aviation tourism system as a typical application case to verify that the proposed framework can be widely applied to various distributed cross domain networks and identity authentication systems.
Web3 represents the next-generation value-driven Internet built on blockchain technology, whose realization heavily relies on mobile devices. However, the limited resources of these devices significantly restrict their ability to participate in transaction verification and ledger maintenance in blockchain networks. Existing offloading schemes often overlook storage offloading or adopt oversimplified joint strategies, failing to adequately consider the synergistic effects of storage and computation offloading on network performance. To address this issue, this paper proposes MEChain, a Mobile Edge Computing (MEC)-aided blockchain network that implements a two-layer joint computation-storage offloading mechanism involving edge service providers (ESPs) and cloud service providers (CSPs). The joint computation offloading, ledger storage, and resource pricing problem is formulated as a three-stage Stackelberg game to capture the complexity of multi-party interactions. An iterative algorithm based on backward induction is designed to efficiently solve the Nash equilibrium, thereby ensuring system stability. Theoretical analysis and numerical experiments demonstrate that the MEChain framework not only significantly improves the profit per unit time of mobile devices by 11.3% but also exhibits rapid convergence of the proposed algorithm, providing a practical and theoretical foundation for resource optimization in mobile blockchain systems.
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.
The high-level integration of generative artificial intelligence (AI) in edge computing systems has raised the question of the integrity and reliability of deploying Model-as-a-Service. Edge servers are not required to follow the so-called generative model to minimize computational cost, whereas users and service providers want validation mechanisms that do not compromise proprietary model information. To address this challenge, this study proposes a cooperative unmanned aerial vehicle (UAV)-swarm-enabled zero-knowledge verification framework for secure, privacy-preserving verification of edge-based generative artificial intelligence inference. The proposed framework involves edge servers producing an interactive cryptographic zero-knowledge proof to verify the execution of generative AI, and UAV swarms that fly freely to confirm verification operations, subject to mobility and energy constraints. The age of verification metric is proposed to trust verification information, jointly reflecting the unverified server reliability and verification freshness, and to provide dynamic priority to risky edge servers. To effectively plan the behaviour of a UAV swarm, a trust-based multi-agent reinforcement learning approach is developed that enables decentralized decision-making while training is centralized. Extensive simulation results show that the proposed framework significantly improves the state-of-the-art baseline schemes in verification timeliness, malicious server detection delay, energy efficiency, and scalability. The findings validate that integrating cooperative UAV swarms, trust-aware verification, and multi-agent learning is an efficient approach to providing reliable generative AI services in dynamic edge computing environments.
Academic research indicates an urgent need for safe, tamper-proof storage of sensitive medical information due to the rapid digitalization of healthcare data. Traditional systems are susceptible to both internal and external assaults because of their dependence on centralized servers. SEC-HEALTH implements a system for the secure storage of electronic health records (EHRs) by combining the immutable, distributed ledger technology of blockchain with the InterPlanetary File System (IPFS).This system use Solidity smart contracts to archive patient data and transaction records on the Ethereum blockchain. Comprehensive EHR files are preserved on IPFS and may be accessed via their blockchain hash addresses. The architecture guarantees data integrity, transparency, and safe access independent of trusted third parties.User modules include appointment scheduling, prescription management, patient and physician authentication, and platform registration. The graphics illustrate a fully operational web interface created in Python, implemented smart contracts, and the integration of blockchain with IPFS. The approach is resilient and decentralized, providing an alternative to traditional centralized health data management systems.
Local AI inference for browser tasks—including vision-language processing, speech recognition, and neural translation—requires significant computational resources that may exceed the capabilities of low-power devices such as smartphones, tablets, and older laptops. This paper presents the design of a distributed GPU compute sharing system for the Kathon cryptographic browser that enables peer-to-peer AI inference acceleration across trusted devices using libp2p networking. The system partitions neural network inference workloads across participating peers using tensor parallelism, with encrypted communication channels, verifiable computation proofs, and incentive mechanisms based on the .aioss cryptographic ledger. We address key technical challenges: heterogeneous device discovery with capability advertisement, dynamic workload partitioning for variable peer availability, encrypted inference that prevents input reconstruction, and fault tolerance through redundant computation. Simulated benchmarks across a 16-peer testbed demonstrate 3.8x speedup for Whisper transcription and 4.2x speedup for Qwen 2.5 VL inference on low-power client devices. A security analysis confirms that encrypted inference provides semantic security against honest-but-curious peers. The system enables Kathon to deliver AI features on devices that lack the local compute capacity for real-time inference. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores browser engine, privacy in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.