The swift expansion of IoT devices in smart cities demands decentralized and open systems of attentive exchange of assets in automotive supply chains. Nevertheless, the majority of the available blockchain-based solutions are focused on traceability and ignore scalability, conditional payment automation, and real-time IoT verification. To overcome those pitfalls, the research proposes a Blockchain-based framework implemented on Hyperledger Fabric, incorporating Non-Fungible Tokens, and escrow-based smart contracts, to facilitate verifiable, automated vehicle transactions. The payment is conditionally released, and the vehicle is represented as a discrete NFT that undergoes authenticated release under Fabric Certificate Authority with escrow verification. Sub-millisecond latency (0.0003 s), constant throughput, and minimal computational cost experimentally verify the effectiveness of the framework in terms of its efficiency, privacy, and scalability in the efficient and autonomous exchange of assets in next-generation smart cities.
Authentication is becoming essential due to the expansion of the Internet of Things (IoT) applications in smart cities, supply chain, and healthcare. In the healthcare sector, hospitals use centralized server-based systems to manage user information and patient medical records. However, this approach may lead to scalability, interoperability, security and privacy challenges. To address these issues, this paper presents a blockchain-based multi-factor authentication (MFA) framework for IoT healthcare systems. The framework uses the Ethereum blockchain and smart contracts to improve authentication security and minimize unauthorized access risk. It also uses the InterPlanetary File System (IPFS) to securely and efficiently store sensitive medical data. Performance and security are evaluated to show the effectiveness, reliability, and feasibility of the proposed system.
D C Saputro, Noor A Setiawan, Azkario Rizky Pratama, Avinanta Tarigan
The increasing adoption of blockchain technology in education has introduced alternative approaches to identity verification beyond centralized credential systems. This study proposes and implements a decentralized authentication mechanism for Moodle LMS using ERC-721 non-fungible tokens (NFTs) verified through MetaMask. Developed as a proof-of-concept following a design science methodology, the system links on-chain identity tokens to Moodle accounts without storing usernames or passwords. The architecture integrates Ethereum smart contracts, nonce-based digital signature verification, and Moodleâs Role-Based Access Control (RBAC) framework. Functional testing confirms that access is granted exclusively to users possessing valid ERC-721 tokens and verified wallet signatures. Replay attack simulations demonstrate effective resistance through nonce validation, while ensuring that no sensitive credential data is exposed during the authentication process, in contrast to default Moodle login mechanisms. Performance evaluation using Apache JMeter indicates stable operation under moderate loads. Although scalability and token management limitations remain, the results demonstrate the technical feasibility and enhanced security advantages of NFT-based authentication for learning management systems.
ABSTRACT Efficient disaster response requires scalable, transparent and trustworthy resource management systems. However, centralized approaches frequently suffer from coordination delays, data tampering risks, limited transparency and single points of failure, reducing reliability during largeâscale crises. This study presents a decentralised blockchainâbased framework that integrates smart contracts, decentralised multiâsource oracles for Internet of Things (IoT)âenabled field reporting, roleâbased access control and adaptive urgency scoring to improve allocation prioritisation and trust calibration. The architecture follows a structured threeâtier design. The edge layer supports realâtime sensing and secure data offloading through the InterâPlanetary File System (IPFS). The blockchain logic layer enforces operational policies, dynamic prioritisation, and reputation scoring using modular, gasâefficient smart contracts. An integration/API layer ensures secure interoperability among emergency agencies and stakeholders. A hybrid blockchain model combines Ethereum ProofâofâStake (PoS) for public transparency with a permissioned consortium chain for controlled governance. Natural Language Processing (NLP) derives urgency scores from textual disaster reports, while a dynamic supplyâdemand aware algorithm adapts resource allocation in real time. Multiâsignature governance and reputation mechanisms further enhance accountability. Experimental evaluation on a simulated testnet demonstrates throughput up to 1000 Transactions Per Second (TPS), alongside measurable improvements in fairness, auditability and allocation efficiency.
The exponential growth of IoT devices in smart city infrastructures generates vast edge data, demanding secure, low latency, energy-efficient processing. Conventional cloud-centric models face bandwidth bottlenecks, latency overhead, and single-point vulnerabilities, necessitating decentralized management. This research introduces BQAREM: Blockchain-Secured Quantum Adaptive Resource Management for Edge Machine Learning, integrating blockchain security, quantum optimization, reinforcement learning. The framework employs timestamped identity verification, multi parameter trust assessment, and a weighted Proof-of-Stake consensus for secure coordination. Quantum adaptive scheduling and smart contracts ensure efficient, tamper-proof resource allocation, achieving superior latency, energy efficiency, SLA compliance. Comprehensive performance evaluation demonstrates that BQAREM significantly enhances reliability, scalability, intelligent resource orchestration across heterogeneous edge environments. Testing in various smart city scenarios including Smart Grid Control, Traffic Management, Healthcare Monitoring, Surveillance Systems, and Emergency Response demonstrates high accuracy (> 95%), reduced latency (< 50 ms), and efficiency improvements exceeding 80%, with balanced energy use. BQAREM uniquely unifies blockchain-backed trust like timestamped identity + weighted PoS, quantum-adaptive risk-sensitive RL, and multi-resource orchestration with a new Robust Performance Index (RPI) for secure, low-latency, energy-aware Edge-ML scheduling.
The rapid evolution of the Internet of Vehicles (IoV) necessitates secure, scalable, and low-latency route navigation mechanisms that can operate in highly dynamic vehicular environments. Emerging paradigms such as Vehicular Digital Twins (VDTs) further enhance IoV ecosystems by enabling real-time virtual representations of physical vehicles, facilitating predictive analytics, intelligent decision-making, and context-aware routing. However, conventional VANET-based approaches suffer from centralized trust dependencies, high computational overhead, and limited adaptability to real-time traffic conditions. This paper proposes BFRN-IoV, a blockchain- and fog-enabled route navigation framework that integrates lightweight ECC-HMAC-based mutual authentication, RSU-assisted fog routing, and global route validation via a Geo-Location Provider (GLP), while leveraging VDTs for enhanced situational awareness and dynamic route optimization. The framework ensures key security properties-including confidentiality, integrity, pseudonymity, unlinkability, and non-repudiation-using ECDH-derived session keys, HKDF-based key expansion, and HMAC verification, while preserving privacy through pseudonym-based identity management. A permissioned blockchain provides immutable and auditable logging of routing interactions without exposing vehicle identities. Simulation results using SUMO and implementation via Web3 demonstrate significant improvements in routing accuracy, along with reduced communication and computational overhead compared to existing approaches. Formal verification using the Scyther tool confirms robustness against replay, impersonation, and man-in-the-middle attacks. The proposed framework delivers a unified, secure, and efficient solution for real-time IoV route navigation, further strengthened by the integration of VDTs in next-generation intelligent transportation systems.
Wencheng Chen, Jun Wang, Jeng-Shyang Pan, R. Simon Sherratt ¡ 5 authors
The rapid advancement of Industry 5.0 has accelerated the adoption of the Industrial Internet of Things (IIoT). However, challenges such as data privacy breaches, malicious attacks, and the absence of trustworthy mechanisms continue to hinder its secure and efficient operation. To overcome these issues, this paper proposes an enhanced blockchain-based data storage framework and systematically improves the Delegated Proof of Stake (DPoS) consensus mechanism. A four-party evolutionary game model is developed, involving agent nodes, voting nodes, malicious nodes, and supervisory nodes, to comprehensively analyze the dynamic effects of key factorsâincluding bribery intensity, malicious costs, supervision, and reputation mechanismsâon system stability. Furthermore, novel incentive and punishment strategies are introduced to foster node collaboration and suppress malicious behaviors. The simulation results show that the improved DPoS mechanism achieves significant enhancements across multiple performance dimensions. Under high-load conditions, the system increases transaction throughput by approximately 5%, reduces consensus latency, and maintains stable operation even as the network scale expands. In adversarial scenarios, the double-spending attack success rate decreases to about 2.6%, indicating strengthened security resilience. In addition, the convergence of strategy evolution is notably accelerated, enabling the system to reach cooperative and stable states more efficiently. These results demonstrate that the proposed mechanism effectively improves the efficiency, security, and dynamic stability of IIoT data storage systems, providing strong support for reliable operation in complex industrial environments.
Abdullah Ayub Khan, Asif Ali Laghari, Hamad Almansour, Teerath Kumar ¡ 7 authors
Wearable health technology has revolutionized remote monitoring and personalized healthcare by allowing real-time surveillance of patient health measurements and vital signs. However, their widespread acceptance is hampered by issues with security, privacy preservation, data protection, and interoperability. Blockchain Technology (BT), in particular Zero-Knowledge Proofs (ZKPs) and smart contracts, present a viable way to enhance the privacy, provenance, and integrity of wearable health data. This paper proposes a BT-enabled system that guarantees decentralized, unforged data management, transparency, immutability, and dynamic traceability for wearable health devices, particularly smartwatches with biosensors. To evaluate the effectiveness of the proposed work, the main performance-related metrics-latency, throughput, computational overhead, security robustness, and scalability-are looked at. The experiment's simulated findings show that BT integration is effective, with a 99.33% improvement in data integrity and protection. Automated access control protocols demonstrate data protection by utilizing smart contracts, and ZKPs guarantee verifiable data exchanges without jeopardizing patient privacy. These results demonstrate improved interoperability, decreased processing time, and increased security in comparison to comparable cutting-edge centralized platforms.
An effective healthcare data system must safeguard individual privacy, foster public trust, and enhance societal resilience. To achieve this, access to critical health information must be provided in an ethical, secure, and reliable manner. This paper proposes a blockchain-based healthcare management framework designed to improve security, privacy, and transparency in healthcare administration. The architecture incorporates smart contracts, multi-signature wallets, and zero-knowledge proofs (ZKPs) to securely facilitate key operations such as patient registration, policy updates, and medical device management on a decentralized platform. Multi-signature wallets require authorization from multiple stakeholders for sensitive transactions, while ZKPs enable identity or access verification without disclosing confidential information. A built-in performance monitoring module collects key metrics, including transaction latency, gas consumption, and block time, which are visualized using JavaScript. Overall, the proposed system offers a secure, transparent, and privacy-preserving approach to decentralized healthcare management.
This paper presents the design, implementation, and evaluation of a decentralized system for issuing and verifying academic certificates based on blockchain technology. The proposed solution addresses common limitations of traditional certification models, such as susceptibility to forgery, reliance on centralized infrastructures, and inefficient verification processes. The system is built on the TRON blockchain and integrates smart contracts written in Solidity, a decentralized web application (dApp) for user interaction, and the InterPlanetary File System (IPFS) for decentralized storage of certificate metadata. The methodology comprised architectural design, smart contract development, and the implementation of a web-based interface, followed by functional, security, performance, and usability evaluations. Experimental results show that the system correctly supports certificate issuance and public verification, enforces access control, and resists common misuse scenarios. Performance analysis indicates low confirmation latency and negligible transaction costs, making the solution suitable for large-scale academic environments. Additionally, usability assessment using the System Usability Scale (SUS) resulted in a score of 76.67, indicating good user acceptance. Overall, the results demonstrate the technical feasibility and practical viability of the proposed approach, highlighting the TRON blockchain as an effective and cost-efficient infrastructure for decentralized academic certification systems.
The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functionsâincluding data management, security, device management, group coordination, and automated billingâto enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchainâs potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (â86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (â0.586â1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead.
Reliable data availability and transparent governance are fundamental requirements for distributed edge-to-cloud systems that must operate across multiple administrative domains. Conventional cloud-centric architectures centralize control and storage, creating bottlenecks and limiting autonomous collaboration at the network edge. This paper introduces a decentralized governance and service-management framework that leverages Decentralized Autonomous Organizations (DAOs) and Decentralized Applications (DApps) to to govern and orchestrate verifiable, tamper-resistant, and continuously accessible data exchange between heterogeneous edge and cloud components. By embedding blockchain-based smart contracts within swarm-enabled edge infrastructures, the approach enables automated decision-making, auditable coordination, and fault-tolerant data sharing without relying on trusted intermediaries. The proposed OASEES framework demonstrates how DAO-driven orchestration can enhance data availability and accountability in real-world scenarios, including energy grid balancing, structural safety monitoring, and predictive maintenance of wind turbines. Results highlight that decentralized governance mechanisms enhance transparency, resilience, and trust, offering a scalable foundation for next-generation edge-to-cloud data ecosystems.
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah
Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains such as surveillance, logistics, and disaster management. However, ensuring the safety, security, and trustworthiness of UAV operations remains a significant challenge, primarily due to vulnerabilities in centralized data processing architectures. Traditional UAV systems rely on remote cloud servers to perform machine learning (ML)-based analytics, which introduces issues such as data exposure, latency, scalability bottlenecks, and susceptibility to cyberattacks during data transmission and storage. These challenges underscore the urgent need for a decentralized, verifiable, and privacy-preser ving learning mechanism that can support collaborative UAV intelligence without centralized control. To address these limitations, this study proposes a blockchain-enabled distributed ML framework that facilitates secure, peer-to-peer collaboration among UAV nodes. The framework integrates blockchainâs immutable ledger and smart contracts with decentralized ML models, enabling UAVs to share and validate trained models rather than raw data. This ensures data confidentiality, integrity, and transparency throughout the learning process. A stacking-based ensemble mechanism is employed to enhance predictive performance through collaborative knowledge aggregation. The proposed system is experimentally validated using a collaborative intrusion detection (ID) scenario using the KDD99 network attack data set and real-world implementation. The results demonstrate significant improvements in detection accuracy, latency and F1-score compared to conventional centralized ML methods, achieving an average accuracy of 97.9%, latency 198ms, and F1-score exceeding 97%. These outcomes confirm that the integration of blockchain and decentralized ML effectively mitigates cybersecurity risks while enabling scalable, trustworthy UAV intelligence.
This deliverable describes the technical foundations and integration approach of the EUâDREAM Data Management Platform, developed under Task 3.4 between M4 and M18. The platform acts as the central layer enabling secure, consistent, and reliable data exchange across the EUâDREAM ecosystem, connecting components such as the Data Platform, Digital Twins, Distributed Ledger Technology, NLPâbased intermediator, and other energy applications. It defines the overall architecture in line with industry best practices, European recommendations, and earlier system designs, while detailing the operation of key components including identity and access management, API gateways, and DevOpsâbased deployment environments. The deliverable also introduces a reference infrastructure to support integration with external systems such as sensors, energy management systems, IoT devices, external databases, and Living Labs. Designed to be modular, scalable, and replicable, the platform allows components to evolve independently while enabling consistent deployment across multiple locations and use cases.
Recently, it has been noted that the convergence of blockchain technology presents a promising paradigm for secure, privacy-preserving, and transparent healthcare systems. Moreover, Digital Twins enable real-time replication of patients, hospital operations, and medical devices, and their dependence on continuous sensitive data streams introduces the latest trust and Cybersecurity challenges. A systematic literature review aims to investigate how distributed ledger and blockchain technologies have been applied to secure healthcare digital twins from 2020 to 2025. Furthermore, the review addresses the proposed architecture of blockchain, the security objectives targeted, integration approaches within digital twins, and evaluation methods with limitations. The study follows PRISMA 2020 guidelines. Web of Sciences, IEEE Xplore, PubMed, Scopus, and ACM Digital Library were searched from January 2020 to October 2025 by using defined Boolean queries. Also, the focus of the inclusion criteria is on peer-reviewed studies that discussed blockchain for DT security in healthcare. Data extraction captured blockchain type, metadata, security mechanisms, DT domain, and evaluation methods. From the 487 identified records, only 20 successfully met the inclusion criteria. The fact behind it is that most studies only employed permissioned blockchains like Quorum and Hyperledger integrated with digital twins for monitoring patients, device lifecycle tracking, and data provenance. Some main security objectives include provenance assurance, access control, and integrity. Moreover, only some studies provide formal threat analysis or real-world deployment. Blockchain technology is reliable because it increases digital twin security through immutability, smart-contract-based governance, and decentralized trust. However, interoperability, scalability, and privacy-preserving computation remain the main barriers for clinical adoption.
The rapid growth of IoT devices in smart home environments has introduced significant challenges in ensuring secure, scalable, and efficient communication among heterogeneous devices. Centralized architectures suffer from a single point of failure, while blockchain-only solutions face high latency, limiting their use in real-time control. To address these issues, we propose a multi-layered decentralized framework that combines a consortium blockchain, a trusted off-chain coordinator, group-based zero-knowledge proofs (ZKPs), and a two-tiered access control policy (ACP) architecture. The consortium blockchain provides an immutable ledger for device identities and foundational, coarse-grained ACP enforcement through smart contracts, ensuring tamper-proof trust. For privacy-preserving mutual authentication, a group-based ZKP protocol enables collective device authorization without revealing sensitive keys. The off-chain coordinator complements this by enforcing dynamic security mechanisms, including fine-grained ACPv2 checksâsuch as rate limits, time-of-day restrictions, and device telemetryâin addition to anomaly detection for behavioral risk assessment. This proposed hybrid structure achieves both immutability and high efficiency over traditional methods. A performance evaluation highlighted the frameworkâs efficiency by demonstrating that the core ZKP verification for a 500-device group can be completed in just 190 ms. The framework drastically reduces on-chain costs, with critical access control policy transactions consuming only 82,748 gasâa reduction of over 90% compared to benchmarked on-chain systems. The complete end-to-end workflow, from user request to secure session establishment, has a latency bound of approximately 3s. Formal security verification with the BAN and AVISPA tools validates resilience against common attacks, including man-in-the-middle, replay, and impersonation, while static analysis using the Slither framework confirms the absence of critical vulnerabilities in the smart contract code. By combining an immutable on-chain foundation with intelligent, dynamic off-chain enforcement, our proposed framework provides a uniquely resilient, scalable, and adaptive security solution for modern smart home systems.
Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has emerged due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further ensured by a zero-trust security framework based on distributed ledger technology. When combined, these elements enable increasingly autonomous workload orchestration, trading centralized control for adaptive, decentralized operation with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum.
Zia Ullah, Zia Ullah, Sanam Shahla Rizvi, Ibrar Ali Shah ¡ 5 authors
Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.
Tan GĂźrpinar, Mehmet Akif Gulum, Melanie Martinelli
Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks.
Gauhar Ali, Sajid Shah, Mohammed ElAffendi, Naveed Ahmad
Introduction Digital Twins (DT) have appeared as a significant tool in Industrial Internet of Things (IIoT) environments, allowing real-time monitoring, predictive maintenance, and maximizing device performance. However, integrating DTs with IIoT initiates serious security issues, specifically in the deviceâs authentication and authorization. The state-of-the-art mechanisms are exposed to insider threats, single points of failure, and privacy issues. Methods This study proposes a blockchain-based access control framework for cross-domain DTs. The blockchain (BC) integration eliminates reliance on the centralized authentication server. It uses platform verification from the manufacturer to validate IIoT device integrity and mitigate insider threats. Moreover, the authorization mechanism is implemented using smart contract and access control policies stored in BC. The proposed Non-Fungible Tokens enable role and permission delegation. Results and Discussion The integration of Hyperledger Fabric BC, platform hash verification, and NFT-based authorization in the proposed architecture enhanced its resilience against cyber-attacks i.e., replay, DoS/DDoS, insider, and spoofing attacks. Moreover, the proposed framework validates its viability with response times (approximately 300ms) for the authentication and authorization phases. Additionally, identity resolution attains 67 % depletion in latency compared to its counterpart.
IoT networks require secure coordination but cannot tolerate the heavy computational and energy burden of mainstream blockchain consensus mechanisms. This paper introduces an adaptive Proof-of-Probability (PoP) model designed for ultra-low-power devices. Unlike proof-of-work or stake-based models, PoP assigns block proposal probability based on device reliability, historical behavior, and real-time trust signals. Each node maintains a local trust vector updated through lightweight observations such as uptime, packet integrity, and peer confirmation. We design a probabilistic leader election protocol that minimizes message overhead and supports rapid convergence. Simulations across 10,000-node IoT clusters show PoP reduces energy consumption by 65â78% compared to PoS-lite variants, while maintaining strong resilience against Sybil and eclipse attacks. We also evaluate a real hardware deployment using ESP32 devices to measure runtime impact. Results show near-linear scalability. The paper concludes with security proofs and guidelines for practical deployments.