Bhupinder Kaur, Deepak Prashar, Leo Mršić, Ahmad Almogren · 7 authors
Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.
One of the modern areas of blockchain technology application is the Internet of Things (IoT). An important component of blockchain technology is the consensus layer. It includes consensus protocols that are used to establish and maintain consensus, as well as to ensure network security, accuracy, and protection of the registry from unauthorized access. Currently, there are a large number of different consensus protocols, including those for blockchain-based IoT networks. Therefore, choosing the most suitable consensus protocol for a specific distributed ledger system, in particular, for an IoT blockchain solution, is an important task. The problem of optimal blockchain consensus mechanism selection in IoT networks can be considered a multi-criteria decision-making problem. This paper presents the step-by-step development of a conceptual model of a system of optimal consensus protocol selection for blockchain-based IoT networks. Following this step-by-step approach, the final goal is to transform the conceptual framework into a practical, adaptive, and efficient decision-making system for blockchain-based IoT networks. The obtained results can be useful for developers and researchers working in the field of blockchain technology and the Internet of Things and contribute to improving the efficiency and security of IoT networks.
Herman Zahid, Adil Zulfiqar, Muhammad Adnan, Muhammad Sajid Iqbal · 7 authors
This review explores the transformative architecture of Smart Grid 3.0 by integrating cutting-edge technologies. It presents novel architectural frameworks to transform nanogrid, microgrid, and VPP topologies to their Grid 3.0 counterparts. This study systematically analyzes the application of advanced algorithms and technologies across all hierarchical subsystems—nanogrid 3.0, microgrid 3.0, VPP 3.0, and Smart Grid 3.0. These digital technologies have transformative capabilities. The digital twins can perform real-time monitoring, simulation, and predictive analysis; blockchain ensures secure, decentralized energy transactions; and the metaverse creates immersive, interactive environments for system management. This review also explores the role of AI in power grid which is to optimize energy scheduling, fault detection, and energy management. This paper adds to the literature by systematically addressing subsystems of Smart Grid 3.0, including energy generation, transmission, distribution, communication, and storage. Challenges such as interoperability, scalability, data integrity, and cybersecurity are discussed, and solutions are proposed which highlights the need of interdisciplinary approach. These include cyber-attack detection and mitigation mechanisms, advanced simulation tools, and robust policy frameworks. A thorough review of literature enabled this paper to present practical implementation strategies and real-world examples of digital technologies integrated smart grids. By integrating these technologies across hierarchical energy systems, this study establishes a foundation for future research in transforming conventional smart grid infrastructure into a resilient, efficient, and interconnected cyber-physical energy network called Smart Grid 3.0 as the peak of this evolution so far.
The integration of blockchain with 6G networks offers secure, decentralized solutions for emerging consumer applications by addressing key challenges such as device reliability, interoperability, and security. Classical blockchains rely on cryptographic primitives for data integrity and trust, but these are vulnerable to quantum attacks and face scalability challenges in ultra-dense environments. To address these issues, we propose a quantum blockchain framework based on temporally entangled GHZ states, enabling inherent quantum encoding of block linkage and integrity. A four-qubit blockchain data structure is designed and implemented using IBM Q quantum processors, with a quantum hash circuit developed to link consecutive blocks securely. The system’s resilience is validated against two representative quantum attacks, CNOT and Ping Pong, demonstrating its ability to maintain tamper resistance in noisy environments. The experimental results achieved a fidelity of 65.79% and offered greater structural security than classical hash-based chains under similar conditions. This enables secure, scalable, and quantum-resilient blockchain integration for 6G consumer applications such as telemedicine, IoT device authentication, decentralized finance, and immersive edge services.
Shivesh Kumar, S. Harsha Vardan, Adithya Yuvan, S. S. Subashka Ramesh
The Blockchain-Enabled Decentralized Supply Chain System presents a secure and scalable product authentication and counterfeiting solution through Ethereum smart contracts and cryptographic methods. Written in the Truffle framework and tested on Ganache, the system uses Solidity-based contracts to enforce the traceability of product lifecycles through a finite state machine design. Role-Based Access Control (RBAC) algorithms control manufacturers, dispatchers, and transporters so that only permitted parties can conduct transactions. Product and transaction metadata are immutably locked with SHA-256 hashing, and backend communications are encrypted with AES. Python modules embedded within Web3.py enable real-time smart contract engagement and orchestrate state changes of the blockchain with rule-based decision logic. Vehicle tracking and receipt data are also validated end-to-end by the system with digital signatures for ultimate verifiability. Decentralized architecture enables tamper-proof auditability, cryptographic integrity, and strong integration with contemporary supply chain ecosystems.
The rapid growth of IoT devices raises challenges in identity management, security, traceability, and digital forensics. Traditional centralised registration methods face security and scalability issues. This paper proposes a Spidernetbased Multi-Blockchain architecture for registering IoT devices (DNAs) to enhance digital evidence acquisition. The multilayered model improves data distribution, redundancy, and fault tolerance. The Multi-Blockchain allows parallel processing and scalability for large IoT networks. A hybrid proof-of-stake and proof-of-activity consensus mechanism boosts security and energy efficiency. The Spidernet architecture outperforms traditional blockchains in transaction speed, latency, robustness, and digital evidence management, strengthening decentralised IoT ecosystems and digital forensics.
In supply chain finance (SCF), the long-standing issue of "difficult and expensive financing" has hindered SMEs' growth, with blockchain technology offering a novel solution.This study adopts a theoretical framework of supply chain internal and external financing to systematically analyze financing models: internal financing for upstream manufacturers, midstream distributors, and downstream e-commerce enterprises, and external bank financing via blockchain platforms.It compares decision-making differences between traditional and blockchain-enabled financing, revealing that blockchain technology reshapes the financing landscape through three core mechanisms: information sharing via distributed ledgers, credit transmission across supply chain tiers, and cost optimization through smart contracts.The study finds that blockchain reconstructs the trust system, optimizes banks' risk pricing, and alleviates financing constraints for end-tier enterprises.Additionally, platforms dominated by different entities (e.g., manufacturers, e-commerce companies, and banks) reshape supply chain pricing and profit distribution through differentiated governance rules.These findings provide theoretical support for integrating "blockchain + SCF" and guide supply chain members in optimizing financing decisions and technology adoption strategies.
The proposed research introduces blockchainbased trust system for Internet of Things (IoT) landscapes that overcomes three fundamental barriers of widely used IoT systems: security limitations and privacy requirements and requirements regarding scaling. The decentralized infrastructure of the blockchains and the properties which are unchangeable drive the proposed method which enhances authentication practices in devices and security in communication as well as the capabilities to assess trust. The system minimizes efficient and safe interactions between the IoT devices by virtue of incorporating Proof-of-Stake (PoS) consensus schemes as well as smart contracts that automate trust management whilst saving on computational costs. The methodology introduces decentralized identity management mechanisms also with the reputation-based trust models as well as off-chain storage options that enhance scalability and avoid network congestion. Intensive performance testing including the comparison of our proposed blockchain-based framework with existing centralized solutions and PoW-based current blockchain implementations take place. Greater transaction processing velocities coupled with reduced delays and reduced consumption of power makes the system more efficient thus proving its suitability for IoT hardware that operates in a resource-constrained environment. The proposed approach shows scalability ability as it can handle large number of devices without affecting the performance levels of the system.
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.
Vincenzo P. Di Perna, Marco Bernardo, Francesco Fabris, Sebastião Amaro · 6 authors
Since blockchains are increasingly adopted in real-world applications, it is of paramount importance to evaluate their performance across diverse scenarios.Although the network infrastructure plays a fundamental role, its impact on performance remains largely unexplored.Some studies evaluate blockchain in cloud environments, but this approach is costly and difficult to reproduce.We propose a cost-effective and reproducible environment that supports both cluster-based setups and emulation capabilities and allows the underlying network topology to be easily modified.We evaluate five industry-grade blockchains -Algorand, Diem, Ethereum, Quorum, and Solana -across five network topologies -fat-tree, full mesh, hypercube, scale-free, and torus -and different realistic workloads -smart contract requests and transfer transactions.Our benchmark framework, Lilith, shows that full mesh, hypercube, and torus topologies improve blockchain performance under heavy workloads.Algorand and Diem perform consistently across the considered topologies, while Ethereum remains robust but slower.
Unmanned Aerial Vehicles (UAVs) will play a vital role in the operation, management and service provisioning process of Next-Generation Networks (NGNs). Given that the Sixth Generation (6G) network is an AI-native cooperative ecosystem that relies on the resource and intelligent capabilities of every layer of the network, especially edge devices, UAVs are thus critical and a key player in 6G. This paper presents a UAVsupported framework for 6G that relies on a multi-tiered approach to guarantee autonomous and optimal network coverage and bandwidth. The UAV network adapts Federated Learning (FL) to maintain self-organization and support for real-time edge processing. The multi-tiered UAV network approach uses realtime adjustments in swarm membership and task allocation to enhance the network energy consumption. Blockchain is integrated into each swarm to maintain the integrity of the trained models, and provides a decentralized device authentication and swarm coordination mechanism. System evaluations reveal that the proposed framework provides high levels of task completion ratio and learning accuracy.
The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.
In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners' data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners' activities, and predicting learners' academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner's data on the blockchain through the interplanetary file system and reap the benefits of securing the learner's data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner's performance. The smart contract functions also play a role in enabling the university to issue learners' certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner's performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain's functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.
Blockchain technology has emerged as a transformative paradigm for secure, decentralized, and transparent data management. However, the rapid growth of decentralized applications (dApps), global transaction demands, and multi-chain ecosystems has exposed scalability bottlenecks in existing consensus mechanisms. Traditional models such as Proof of Work (PoW) and Proof of Stake (PoS), while effective in maintaining security, struggle with throughput, latency, and energy efficiency. Recent research highlights the potential of artificial intelligence (AI) to augment blockchain consensus by improving leader selection, optimizing validator participation, dynamically adjusting difficulty, and predicting network anomalies. This manuscript explores AI-assisted consensus mechanisms as a scalable alternative for next-generation blockchain systems. The paper conducts a comprehensive literature review of blockchain scalability challenges, outlines a methodology for integrating reinforcement learning (RL), deep learning, and predictive analytics into consensus protocols, and presents simulation-based results. Findings suggest that AI-enhanced consensus can achieve up to 70% improved throughput, reduce energy costs by 50%, and enhance fault tolerance by predicting malicious node behavior in advance. The study concludes that AI-assisted consensus mechanisms provide a sustainable path toward highly scalable, adaptive, and secure blockchain networks, with implications for finance, supply chains, IoT, and government applications.
In the Internet of Vehicles (IoV) era, connected vehicles are leveraging intelligent sensors to share sensitive and real-time information to enhance road safety, driving comfort, and traffic efficiency. Despite these advancements and benefits, security and privacy vulnerabilities remain a crucial challenge. This article proposes an enhanced trust-enabled Delegated Proof of Stake (DPoS) consensus-based consortium blockchain to address these challenges effectively for information sharing. The proposed consensus approach focuses on miner selection based on trust scores derived from vehicle transaction history analyses. The trust score calculation employs entropy information and binomial distribution, where the entropy measures the uncertainty in a vehicle's behavior, and the binomial distribution assesses the success rate of its transactions. This model ensures traceable and anonymous vehicle-to-vehicle information sharing, preventing unauthorized information transfer and improving overall system reliability. By leveraging the enhanced trust-based DPoS consensus mechanism of consortium blockchains, the proposed scheme leads to overcoming the challenges of vehicular ad hoc networks, along with fostering economic interest through enhanced traffic safety and energy conservation. The experimental results validate the proposed model, revealing significant improvements in terms of detection of malicious vehicles, communication latency, security, and efficiency of IoV applications.
Bassam W. Aboshosha, M.A. Zayed, Hany S. Khalifa, Rabie Α. Ramadan
Abstract Background The rapid expansion of Internet of Things applications in healthcare has created new opportunities for improving patient care through real-time monitoring and data sharing. However, this growth also introduces significant challenges related to data security, privacy, and system efficiency, especially for devices with limited processing power and energy resources. To address these issues, this study introduces a blockchain-based lightweight hashing system specifically designed for healthcare environments with resource-constrained devices. The goal is to ensure secure, efficient, and scalable handling of sensitive medical data without overwhelming the capabilities of connected devices. Results The proposed system combines a collision-resistant, lightweight hash function with blockchain technology to enhance data integrity, authentication, and privacy. The hash function minimizes computational demands, making it ideal for wearable and embedded healthcare devices. Blockchain integration enables decentralized data management, preventing unauthorized access and tampering. The system generates unique, immutable patient identifiers and protects electronic health information from common security threats, including collision attacks, Sybil attacks, and cryptographic analysis. Simulation results show improved computational efficiency, lower latency, and effective handling of high transaction volumes with minimal resource usage. Conclusions This research presents a secure and efficient framework for managing medical data in healthcare Internet of Things applications. By leveraging lightweight cryptographic techniques and decentralized data structures, the system addresses key limitations in current solutions while supporting scalability and real-world deployment. Potential applications include secure patient monitoring, real-time sharing of health data, and decentralized management of medical records. The proposed approach provides a foundation for future advancements in digital healthcare systems, particularly in remote care, emergency response, and wearable health technologies.
The use of Electric Vehicles (EV) will promote urban sustainability, decrease air pollution, and reduce noise pollution. In this landscape a new mobility concept termed shared electric mobility-as-a-service (eMaaS) has emerged over the years. Shared eMaaS comprises the seamless integration of various forms of electric transport services available via one single digital platform. Although, the current shared eMaaS solutions are based mostly on fragmented and siloed systems which has resulted to issues related to the exchange of data and services from different eMaaS providers. Therefore, there is need for integrators and enablers to achieve an inter-operable and intra-operable seamless shared eMaaS. To this end, Distributed Ledger Technologies (DLT) is proposed in this study to enable new business models for shared electric mobility solutions. As compared to conventional approaches DLT offers a transparent, cost-efficient, and decentralized services both for managing the supply and demand sides of shared eMaaS to improve public transportation. Accordingly, this article presents a DLT based business models grounded on the literature to decentralize shared eMaaS. Qualitative data is collected from Scopus and Web of Science database, and descriptive analysis is employed to analyze the collected data. Findings from this study presents use case scenarios of how IOTA tangle as a DLT using smart contracts and IOTA wallet/tokens are deployed to design novel business models for managing seamless travel experience for electric car sharing and leasing to improve public transportation.
The rapid proliferation of Internet of Things (IoT) devices in modern smart cities has led to the creation of myriad ICT-enabled services ranging from automated healthcare and building monitoring to smart energy management. Traditional centralized systems, however, face significant challenges such as single points of failure, vulnerability to hacking, and data tampering. In this paper, we propose a decentralized framework that leverages Ethereumbased smart contracts to enable secure and autonomous automated trading systems for smart cities. Our approach replaces centralized intermediaries with self-executing contracts, which not only guarantee immutable transaction records and real-time execution but also support dynamic pricing mechanisms that balance supply and demand in real time. We demonstrate our framework through two key use cases-smart healthcare and smart building monitoring-illustrating how smart contracts can automate multi-step workflows while preventing issues such as double spending through robust cryptographic measures. Experimental results obtained from a testbed implementation (utilizing Node.js, web3.js, and Ethereum Virtual Machines on Raspberry Pis) highlight the system's fault tolerance, improved transaction speeds, and scalability. Additionally, simulations reveal that as transaction values increase, the risk of double spending can be mitigated by adjusting the hashing power, ensuring a secure and reliable environment for automated trading. This work contributes to the state of the art in blockchain-based automation by providing a viable, cost-effective alternative to traditional, centralized control systems in smart city applications.