The Internet of Things (IoT) and cyber–physical systems (CPS) are driving digital transformation and automation. An essential component of CPS is digital twin (DT) technology, which enables real-time synchronization between physical assets and their virtual counterparts. Battery management systems (BMS) in electric vehicles (EVs) face challenges in handling large volumes of sensor data, often leading to reduced accuracy in battery-state estimation. To address these challenges, DTs have been explored to aid real-time diagnosis and monitoring. One critical step toward the success of DTs is to have practical reference architectures. This paper presents proposes a novel six-layer DT architecture tailored for BMS, extending existing CPS/DT-BMS models by integrating high-fidelity electrochemical modeling, robust nonlinear state estimation, and interactive 3D visualization in a Metaverse environment. The architecture is designed with scalability in mind, supporting deployment on lightweight embedded platforms or via cloud-hosted rendering for resource-limited devices. We validate the approach using MATLAB to develop a thermally coupled SPMe-based DT of a lithium-ion NMC battery, synchronized with a virtual battery model in Unreal Engine for immersive visualization. Experimental results demonstrate accurate state-of-charge estimation (RMSE 0.23%) and low-latency real-time monitoring, highlighting the framework’s potential for deployment in large-scale EV BMS applications.

Securing Industrial Internet of Things (IIoT) systems presents critical challenges due to resource constraints, expanding attack surfaces, and the inadequacy of conventional security solutions against sophisticated cyber threats. While AI-driven detection and blockchain technologies offer promise, existing frameworks suffer from computational inefficiency, lack of real-time prevention, or insufficient auditability. This paper introduces a unified AI-PureChain Intrusion Prevention Framework that tightly integrates deep learning-based threat detection with an immutable PureChain ledger using Proof of Authority and Association (PoA2) consensus. The proposed architecture achieves high-fidelity intrusion detection through hybrid CNN-BiLSTM models, attaining 99.76% accuracy on IoTForge Pro, 98.33% on WUSTL-IIoT-2021, and 98.11% on X-IIoTID datasets, while maintaining low inference latency (0.0016s). The PureChain layer ensures tamper-proof audit trails with 24.56 TPS throughput and 68ms commit time, enabling verifiable prevention actions. Experimental results demonstrate complete attack mitigation (0% success rate) under high-traffic conditions while maintaining minimal resource consumption (14.49% CPU, 448 MB memory, 12.95W power). This work represents a significant advancement in IIoT security by delivering a tightly coupled framework that simultaneously addresses detection accuracy, prevention reliability, and forensic accountability, thereby bridging critical gaps in current industrial security paradigms.

The Industrial Internet of Things (IIoT) has transformed critical infrastructure but has also introduced severe security vulnerabilities, with breaches capable of causing catastrophic physical and operational damage. While blockchain technology offers a promising foundation for tamper-proof logging, existing platforms are often ill-suited for IIoT due to high latency, low throughput, and excessive energy consumption. Furthermore, most current research treats intrusion detection, secure logging, and system recovery as isolated components, lacking a unified framework for autonomous, verifiable resilience. To bridge this critical gap, this paper introduces PureChain, a holistic, secure, and resilient ecosystem. PureChain integrates a custom lightweight blockchain with a deep learning-based intrusion detection system and a novel verifiable recovery protocol, creating a closed-loop security model. The framework leverages a novel Proof of Authority and Association (PoA2) consensus mechanism, achieving high throughput (16.82 TPS), low latency (0.0594 s), and minimal energy consumption (12.43 W), demonstrating suitability for resource-constrained IIoT environments compared to general-purpose platforms like Ethereum and Hyperledger which are optimized for different use cases. Upon intrusion detection by optimized models like XGBoost (99.87% accuracy), immutable blockchain logs actively trigger and cryptographically attest to infrastructure-enforced recovery actions such as device isolation via SDN switches or state rollback through hardware management controllers. Extensive evaluation on benchmark IIoT datasets (IoT-CAD and IoTForge) demonstrates a detection-to-recovery success rate of up to 98.59% while maintaining 100% data integrity. PureChain establishes a new paradigm that unifies real-time threat intelligence, blockchain-based trust, and provable autonomous recovery for next-generation IIoT security.

The convergence of Non-Terrestrial Networks (NTNs) and Terrestrial Networks (TNs) in 6G presents a paradigm shift for the Internet of Things (IoT), enabling ubiquitous connectivity but introducing severe routing challenges across a dynamic, multi-domain topology. Traditional blockchain-based routing frameworks often fail to adapt to the mobility, heterogeneity, and energy constraints of IoT deployments. This paper proposes a Proof-of-Route (PoR) blockchain consensus protocol, augmented with PoA (Proof-of-Authority), with adaptive validator rotation for secure and scalable routing. The protocol integrates cryptographic route authentication, dynamic validator selection, and wallet-based key management. This ensures its resistance against black holes, Byzantine attacks, and Sybil attacks. Unlike prior work, our framework was evaluated under conditions that mirror real-world operational stress. Modular Docker-based containers were developed to represent devices in a 6G-IoT setting. They simulated intermittent link availability, varying latencies, node churn and handovers. Results revealed that the proposed protocol achieved up to an average latency of42.3 ms, a packet delivery ratio of98.7% with scalability verified up to over500 nodes. This paper offers a robust, scalable, and secure solution for the complex routing demands of 6G networks.

Federated learning (FL) in the Internet of Vehicles (IoV) networks is challenged by privacy vulnerabilities, model poisoning attacks, and the limitations of centralized aggregation, which threaten security and scalability. While homomorphic encryption (HE) provides an additional layer of privacy, existing schemes often suffer from cumulative noise growth, resulting in degraded model accuracy and increased inference latency, critical factors for real-time vehicular safety applications. This paper presents PureFL, a novel FL framework that combines adaptive Cheon-Kim-Kim-Song (CKKS) homomorphic encryption with a custom permissioned blockchain (PureChain), utilizing a proof of authority and association (PoA2) consensus mechanism. PureFL incorporates dynamic precision scaling to manage noise accumulation, ensuring computational efficiency without sacrificing privacy or accuracy. The framework also implements a trust-based aggregation mechanism based on cosine-similarity trust scoring, which dynamically weights client contributions according to historical reliability and anomaly detection, thereby enhancing resilience against model poisoning. Experimentation results on the CICIOV2024 and 5G-NIDD datasets demonstrate that PureFL achieves superior anomaly detection accuracy of 99.9%, reduces CKKS HE overhead via precision scaling, and that the proposed PoA2consensus outperforms other consensus mechanisms in terms of throughput and latency, making it suitable for real-time IoV operations.

The Internet of Medical Things (IoMT) enables continuous health monitoring but still faces challenges in achieving personalized predictions and ensuring secure, tamper-proof data integrity. We presentRemoteCare, an AI-driven multi-modal framework that fuses synchronized physiological and network data for dual-task learning, simultaneously performing personalized health state classification (Normal, Warning, Critical) and cyberattack detection in IoMT traffic. Unlike conventional population-based thresholds,RemoteCaredynamically adapts alerts to each patient’s baseline, thereby minimizing false alarms and enhancing clinical reliability. A hybrid CNN–GRU–LSTM architecture jointly captures spatial and temporal dependencies across heterogeneous signals, while SHAP-based explainability provides transparent, patient-specific insights into the features influencing each prediction. To guarantee auditability, all predictions are immutably recorded on thePureChainblockchain integrated with IPFS, ensuring decentralized and tamper-proof storage. Evaluated on the WUSTL-EHMS-2020 dataset (Enhanced Healthcare Monitoring System),RemoteCareachieved 99.7% accuracy for health classification and 96.0% for intrusion detection, with negligible false alarms and efficient inference suitable for real-time deployment. By unifying multimodal prediction, personalization, interpretability, and secure logging,RemoteCareestablishes a trustworthy framework for early intervention, patient-specific risk assessment, and clinician-oriented decision support in remote healthcare.

Vehicular-to-Everything (V2X) communications promise unprecedented safety and efficiency gains but remain vulnerable to confidentiality breaches such as eavesdropping, traffic analysis, and man-in-the-middle attacks. We propose ConfidSPEC-V2X, a focused hybrid framework that integrates continuous-variable quantum key distribution (CV-QKD), a multi-agent deep reinforcement learning (DRL), and an Ethereum-based permissioned blockchainPureChainpublic-key infrastructure (PKI) to deliver information-theoretic secrecy, dynamic traffic obfuscation, and tamper-proof key management. In the quantum module, CV-QKD transceivers embedded in On-Board Units (OBUs) and Roadside Units (RSUs) establish symmetric keys resilient to passive interception and capable of immediate eavesdropping detection. The Artificial Intelligence (AI) module employs multi-agent DRL agents at RSUs to learn optimal dummy-traffic injection policies that obfuscate real V2X message patterns against statistical inference. The blockchain module leverages PureChain smart contracts to register, rotate, and timestamp vehicle public keys, ensuring that any man-in-the-middle attempt to forge or replay keys is invalidated. We implement and evaluate ConfidSPEC-V2X within an OMNeT++/Veins simulation under realistic urban mobility scenarios, measuring the quantum bit error rate, key generation throughput, obfuscation entropy, and key management latency. Results demonstrate that our framework achieves robust confidentiality protection with minimal performance overhead.

Abstract Land administration in Nigeria continues to face critical challenges, including document inconsistencies, opaque verification procedures, slow processing times, and persistent administrative malpractice associated with centralized and conventional, non-automated record-management systems. These systemic weaknesses create opportunities for double allocation, unauthorized alterations, and hidden registry manipulation, highlighting the need for more secure and transparent digital approaches. This paper proposes BERBLOM, a blockchain-enabled land ownership management system tailored to Nigerian land-registry workflows. The system tokenizes land parcels as ERC-4907 assets and stores supporting documents in the InterPlanetary File System (IPFS) to enhance data integrity and reduce single points of failure. BERBLOM supports practical use cases such as government plot allocation, peer-to-peer property transfers, and an on-chain leasing mechanism that automatically reverts ownership upon lease expiration. The framework operates on a permissioned Hyperledger Besu network using the Quorum Byzantine Fault Tolerance (QBFT) consensus protocol, selected for its Byzantine fault tolerance, predictable gas-free operation, and scalability under controlled conditions. Experimental evaluation indicates that QBFT maintains stable throughput and manageable latency as the number of validator nodes increases from 4 to 32. A Flutter-based decentralized application (DApp) enables real-time interaction through event-driven synchronization. While human-driven corruption and malpractice cannot be fully eliminated, the proposed prototype demonstrates how blockchain-based registries can reduce opportunities for hidden technical manipulation and improve on-chain accountability. It is important to note that the primary performance benefits of BERBLOM are realized after tokenization. Initial land registration remains dependent on legally mandated, human-driven governmental approvals; however, post-registration operations such as peer-to-peer transfers and on-chain leasing can be executed without intermediaries and complete within seconds. Consequently, the performance analysis focuses on the lifecycle stages where blockchain automation provides the greatest impact.

Robust object tracking in ISAC systems is challenging due to the inherent sparsity and severe impulsive noise of millimeter-wave (mmWave) radar signals. To address this, we propose a novel tracking framework that combines cross-modal supervision with a Spatio-Temporal Skip-Transformer (ST-Skipformer). An offline LiDAR–camera label generator produces high-fidelity ground-truth supervision, enabling the ST-Skipformer to learn precise spatial features from noisy radar inputs. Furthermore, the ST-Skipformer incorporates a Temporal Skip (TS) Block to effectively filter background clutter while recovering high-frequency spatial details via skip connections. Experimental results on the real-world DeepSense 6 G dataset demonstrate that the proposed method achieves an Average Displacement Error (ADE) of 0.0174 m, reducing localization error by approximately 98% compared to baselines, while attaining a near-perfect F1-score of 0.9967.

PureRx is an innovative blockchain-based prescription management system designed to overcome the limitations of traditional paper prescriptions, including medication errors, fraud, and inefficiencies in tracking patient records. By leveraging Non-Fungible Tokens (NFTs) and batch minting, PureRx enables efficient, secure, and patient-centric control of prescription data. Patients maintain ownership of their medical records, while healthcare providers and pharmacies benefit from transparent and immutable prescription workflows. Experimental evaluation shows that PureRx improves operational efficiency by 37.18% in initial cycles, increasing to 51% in subsequent cycles compared to existing systems, while reducing costs by 46.76% in high-volume scenarios. Security analysis confirmed that no critical vulnerabilities were found and that all prescription issuance and claiming events are tamper-resistant and auditable on-chain. By combining scalability, transparency, and patient empowerment, PureRx demonstrates the potential of blockchain to transform prescription management in modern healthcare systems.

The interconnected and diverse nature of Digital Twin (DT)-based industrial Artificial Internet of Things (AIoT) systems exposes them to potential cyber threats and malicious activities. This paper introduces a novel framework called BLIND-Twin, which leverages blockchain, DT, and Large Language Model (LLM) technologies to address critical security and scalability challenges in industrial AIoT networks. By integrating DT technology, BLIND-Twin continuously mirrors physical environments, enabling real-time monitoring and synthetic data generation to simulate diverse threat scenarios. Data from the DT undergoes feature extraction via a Long Short-Term Memory (LSTM) Autoencoder (LSTM-AE) to extract essential temporal patterns, while the LLM enables adaptive, contextaware intrusion detection without retraining. A permissioned blockchain layer ensures data integrity, privacy, and secure logging through smart contracts, supporting automated threat response with verifiable audit trails. The framework's decentralized architecture mitigates Single Points of Failure (SPoF), addressing scalability and privacy concerns. Performance evaluations utilizing datasets like 5G-NIDD and CICIoT2023 demonstrate BLINDTwin's capability in accurately detecting various cyber threats by achieving 99.63 % accuracy with minimal latency, showcasing its effectiveness for complex industrial AIoT environments.

The advancement of intelligent transport systems and the rise of autonomous vehicles offer significant potential for reducing road accidents. However, mountainous regions, such as South Korea, are particularly susceptible to the formation of black ice, which poses a serious risk to both human and autonomous drivers due to its near-invisibility and sudden formation. Traditional road condition monitoring methods often fail to promptly detect black ice, underscoring the need for more advanced sensing systems. This work presents a multimodal system called BlackIceNet, integrating visual, acoustic, and sensor data, including surface and ambient temperatures, to detect black ice. The system utilizes a convolutional neural network (CNN)-based framework for image and audio analysis, followed by data fusion techniques to combine the insights from each modality. The proposed algorithm includes the following steps: preprocessing and normalizing data, feature extraction from visual and acoustic data, multimodal fusion to combine vision, audio, and sensor data, and classification of road surface conditions using BlackIceNet. The dataset has been gathered over the years from various testbeds established across three distinct areas in South Korea, contributing to a comprehensive understanding of the area’s diverse conditions. The evaluation results demonstrate the efficacy of the proposed method, achieving a 97.54% accuracy rate in detecting black ice with a model size of 233.47 MB and a training time of 2293.92 s, offering a more compact and computationally efficient solution. This fusion-based approach overcomes the limitations of individual modalities, providing reliable and early warnings, thereby enhancing road safety in hazardous conditions.

Vulnerabilities in drone networks stem from the reliance on GPS and wireless communication technologies, combined with the lack of robust security mechanisms. This study proposes DroneGuard, a comprehensive cybersecurity framework leveraging supervised machine learning (ML) and explainable artificial intelligence (XAI) to detect intrusions and provide insights into the decision-making process of the security model. We explored various feature selection techniques to design a lightweight model suitable for the resource constraints of drones. Additionally, the synthetic minority oversampling technique (SMOTE) is employed to balance target class distribution and mitigate performance degradation, while randomized search cross-validation (RSCV) aids in selecting optimal hyperparameters for model training. Simulation experiments were conducted using a real-time GPS dataset for autonomous vehicles and a cybersecurity dataset containing variants of Denial of Service (DoS) attacks to evaluate the models’ performance. Comparison with four ML models using essential evaluation metrics validated the robust performance of the decision tree model, which detected spoofed GPS signals and DoS attacks with high accuracy, low-computational complexity, and minimal false alarm rates. Furthermore, the Shapley additive explanation (SHAP) provides intuitive visual explanations of important features contributing to the detection and classification of both GPS spoofing and DoS attacks. Therefore, DroneGuard offers effective and interpretable security solutions for enhanced drone application and adoption.

The rapid expansion of the Internet of Things (IoT) into edge networks, which are populated by resource-constrained devices, introduces significant security challenges. This article introduces a robust intrusion detection systems (IDS) for edge-IoT networks developed as a combination of blockchain technology and federated learning (FL) with a customized aggregation strategy, adaptive trimmed mean aggregation (ATMA). Our design leverages a permissioned blockchain to authenticate clients and immutably store the final global model, guaranteeing that only verified participants contribute to the training. ATMA strategy used in the FL departs from fixed-threshold schemes in other works by dynamically adjusting its trimming parameter according to the observed variance in client updates. This variance-aware trimming provides strong Byzantine resilience without sacrificing model accuracy, and its sorting-based implementation maintains an O(n log n) computational complexity. The proposed setup was evaluated under combined label-flipping and Gaussian-noise attacks at adversarial rates of 0%, 10%, 20%, 30%, 40%, 50%, and 60%, in both IID and non-IID data distributions. The results demonstrated that our blockchain-backed ATMA preserves high detection performance under severe attack scenarios and does so with minimal overhead, making it a scalable, secure solution for safeguarding Edge-IoT deployments.

Increased automation, connectivity, and data sharing enabled by 6G technology have heightened the vulnerability of Internet of Vehicles (IoV) networks. Addressing this challenge requires an intrusion detection system (IDS) capable of accurately identifying data falsification within IoV while adhering to real-time constraints. This paper presents a Blockchain-enhanced feature-engineered IDS to ensure precise attack detection and classification with minimal computational overhead in in-vehicle networks (IVNs). The proposed lightweight IDS utilizes a hybrid Pearson’s Correlation Coefficient (PCC) feature selection technique designed for deployment on the Telematics Control Unit (TCU). Furthermore, we propose a custom private blockchain network utilizing the proof of authority and association (PoA) consensus mechanism, deployable on the Roadside Unit (RSU), for the secure logging of vehicle Electronic Control Unit (ECU) information, detection results, and the automatic isolation of malicious ECUs via smart contracts. Experimentation analysis demonstrates that the proposed approach achieves notable performance, with a 99.9% detection accuracy and minimal computation times of 0.24s and 1.32s on the CICIoV2024 and CAN-Intrusion datasets. Furthermore, the system achieves high blockchain scalability, maintaining stable throughput of 16 tx/s and low transaction latency of 0.062s under increasing ECU density and RSU coverage.

As the Metaverse progresses, its security measures must evolve to safeguard users from cyberattacks. To this end, Artificial Intelligence (AI)-powered Network Intrusion Detection Systems (NIDSs) have been implemented at the network layer to detect and respond to threats in real-time. Our survey -MetaWatch provides a comprehensive overview and analysis of relevant literature, focusing on studies that have explored NIDSs in the Metaverse. Unlike other surveys that have addressed Metaverse security issues more broadly, MetaWatch specifically provides a taxonomy of detection paradigms and defense mechanisms within Metaverse NIDS from 2021 to 2024. Additionally, MetaWatch identifies and discusses the key challenges and problems that impact the development of trustworthy, explainable, scalable, and robust Metaverse NIDS. Overall, the survey provides readers with a concise and informative summary of NIDS issues in the Metaverse and highlights open security problems worth exploring.

An essential aspect of Industrial Internet of Things (IIoT) systems lies in their reliability and resilience against failures. Fault detection serves as a crucial method for mitigating errors, leading to reduced downtime. Previous studies have predominantly focused on fault detection using centralized Artificial Intelligence (AI) approaches, wherein participant information is centralized and forwarded to a central server. However, Federated Learning (FL) offers a solution to these issues, enhancing the system’s reliability. In this study, we propose a Decentralized FL (DFL) approach for collaborative learning in bearing fault detection. DFL is preferred over centralized FL due to its elimination of a single point of failure. By leveraging the decentralized FL concept, the vulnerability of the collaborative framework to attacks can be minimized. Our proposed DFL integrates continual learning techniques to reduce communication overhead. The results demonstrate that decentralized collaborative learning achieves satisfactory performance, with an accuracy rate of 96.08% and a learning time reduction of up to 37.52%.

The distributed and heterogeneous connections in the digital twin (DT)-based industrial Internet of Things (IIoT) are vulnerable to cyber-attacks and malicious activities. This study proposes a permissioned blockchain-assisted collaborative and decentralized cyber threat detection for securing DT-based IIoT networks. A context-aware network intrusion detection system (C-NIDS) model was developed using factorized and grouped convolution structures to detect adversarial attacks in virtual and physical environments. A verifiable off-chain aggregation technique with a digital signature is implemented to provide a trustworthy and anti-tampering aggregated model with minimum transaction time. The results exhibit the robustness of the proposed model by achieving an attack detection accuracy of 99.50% using a lightweight model structure with trainable parameters of 4,634 and MFLOPs calculation of 0.0088. Moreover, the verifiable off-chain aggregation performs a total transaction time of 0.0244 seconds.

Abstract This paper proposes a whale optimization algorithm (WOA)‐based partial transmit sequence (PTS) scheme called WOA‐PTS to reduce the high peak‐to‐average power ratio (PAPR) for universal filtered multi‐carrier (UFMC) systems. High PAPR is a prevalent challenge encountered in multi‐carrier systems. In the conventional PTS technique, the optimization of phase rotation factors is achieved through multiplication with the sub‐blocks. In this undertaking, WOA optimization is integrated as a phase optimizer in the PTS‐based PAPR reduction scheme. The experimental results show that, when compared with PTS‐based UFMC signal, the UFMC signal with WOA‐PTS can achieve 4.15 dB PAPR reduction at the complementary cumulative distribution function value of 10 −3 , additionally power spectral density performance and bit error rate also improved.

The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-attacks. To prevent and mitigate malicious network activities in a distributed metaverse, conventional intrusion detection systems (IDS) have communication overhead and privacy concerns. Federated learning (FL) techniques are widely employed to develop IDS frameworks and enable privacy-preserving collaborative learning schemes in decentralized ecosystems. However, the vanilla FL system utilizes a centralized FL aggregation technique, which introduces a single point of failure (SPoF) and potential unauthorized aggregators, allowing malicious clients to inject false data parameters, known as poisoning attacks. Furthermore, low-quality clients in the FL system can result in degraded model performance and hinder convergence. This study proposes a secure and reliable blockchain-aided federated learning (BFL)-based IDS framework using a lightweight model for securing metaverse. An authorized federated IDS is proposed to establish a trustworthy decentralized aggregation mechanism, utilizing proof-of-authority (PoA) consensus. The proposed federated IDS implemented a hybrid client selection (HCS) technique, considering the accuracy and reputation of client histories, to select high-quality metaverse edge devices. Additionally, a fairness ERC-20 token-based incentive mechanism was developed to reward selected FL clients as a token of appreciation for their contribution to the FL training processes. According to the IDS framework measurements, the proposed model performs better than the existing approaches for detecting cyber-attacks in metaverse environments, achieving an accuracy of 99.28% with trainable parameters of 1.8K and mega floating-point operations (MFLOPs) of 0.0016.

The accelerated industrial transformation has witnessed the supervisory control and data acquisition (SCADA) transit from monolithic to the Internet of Things (IoT-SCADA). The development also transformed conventional specialized serial-based to transmission control protocol/internet protocol reliant standard communication protocols, such as IEC-60870-5-104 (IEC-104), thereby increasing vulnerability to attacks and intrusions. Maintaining the reliability and availability of IoT-SCADA demands versatile and robust monitoring of network traffic. This study proposes a monitoring technique to detect and characterize the IEC-104 IoT-SCADA network traffic. The proposed trees bootstrap aggregation monitoring technique of GridSearchCV() hyperparameter tuning of 11 n-estimator, 20 max-depth, and 5-k cross-validation achieved early detection and characterization. Experimental results demonstrate its sensitivity and precision in detecting and classifying various network traffic and application types at a minimal execution time while reducing false alarm rates, which is vital for mitigating intrusions in heterogeneous IoT-SCADA networks.

The immersive metaverse environment offers distinct social interactions and opportunities, yet it also presents significant challenges in securely managing misbehavior, including hate speech, bullying, and harassment. Existing solutions primarily focus on detecting such behavior through artificial intelligence but lack robust mechanisms for management and governance. This gap is critical as the metaverse continues to mirror complex real-world interactions and centralized authority systems prove vulnerable to compromise. Our research introduces a novel framework, Meta-Governance, which not only detects but also effectively manages and governs user behavior through smart contracts, ensuring a secure, fair, and transparent metaverse environment. The system incorporates behavior monitoring to identify and condemn inappropriate behavior, specifically targeting problems such as hate speech and cyberbullying. Occurrences of misbehavior are permanently preserved on the blockchain to ensure the capacity to trace and bear accountability. In this article, we deploy a Natural Language Processing (NLP) model and a smart contract-based framework to address unusual behavior monitoring, access control, and credit scoring. Deep learning models are used to identify and classify linguistic patterns that may be considered hazardous. Blockchain technology addresses virtual misconduct using smart contracts, while a distinctive credit scoring mechanism ensures that users are held responsible for making disrespectful statements. The efficacy of the proposed smart contract is comprehensively evaluated within the context of a private Hyperledger Besu system. The integration of AI and blockchain may greatly improve the security and inclusiveness of the metaverse, highlighting the crucial role of these technologies in combating hate speech and enhancing user engagement.

The pervasiveness in the industrial internet of things (IIoT) due to the application of supervisory control and data acquisition (SCADA) has led to the growth of heterogeneous sensor data, thereby increasing the risk of intrusions and attacks. The existence and effect of intruders and their innovative attack techniques are on the rise. Existing intrusion detection systems (IDS) tend to be computationally expensive with this form of data due to the presence of noise. In real-time domains, available methods lag, necessitating additional research into effective feature extraction schemes, which is fundamental in machine learning (ML) for time exigency. This study, in a comparative analysis of some feature selection techniques (FS), proposes a combination of an efficient ML classifier and an agnostic feature selection (FS) scheme for attack detection and classification in a real-time SCADA network. The flexibility and interoperability of the proposed approach resolve the computational complexity of vulnerability detection schemes while reducing false alarm rates (FAR) and overall model execution time. With the view of an online preprocessing, the proposed technique is phased thus: (i) data preparatory consisting of data cleansing and normalization followed by (ii) the combination of a pre-pruned decision tree (DT) algorithm and an agnostic Chi-square FS approach built to obtain an optimal subset of data features for efficient IDS. (iii) Evaluation of proposed agnostic DT-CH and other FS candidates for anomaly detection.

The intelligence and integrity of a real-time cyber–physical system depend on how trustworthy the data’s legitimacy, appropriation, and authorization are during end-to-end communication between the participating nodes in its network. With the recurrent repugnant global violations of the airspace by drones and their derivatives, there is an urgent need to empirically evaluate the underlying security architectures that govern drone usage operations for priority logistics. This review examines the significant contribution of artificial intelligence models and blockchain to the development of trustworthy and reliable intelligent and secure autonomous systems by integrating cyberspace, intelligence space, and airspace security. PRISMA-SPIDER methodology was adopted for the systematic review of 133 articles based on the inclusion criteria consisting of 91 (68.4%) quantitative studies, 19 qualitative studies (14.2%), and 23 (17.3%) mixed method studies to balance article selection sensitivity and specificity. The review outcome shows a significant disconnect between model proposals and actual implementation. Through the incorporation of zero-trust architecture into the existing blockchain technology and the convergence of newer AI models, dynamic security issues like drone ownership authentication, drone package delivery verification, drone operation authorization, and drone jurisdiction accountability, can be achieved seamlessly for secure smart mobility via drone transportation systems.

Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet of Military Things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a blockchain-integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN analyzed the radio-frequency (RF) signal sent by the antenna array element to determine the Direction of Arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to process the received signal array through IoMT-Net. Moreover, the proposed CNN model is designed with two different functional modules, such as the resource accuracy tradeoff (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depthwise and grouped convolution layers. These sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the subunits of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10-dB SNR) and outperforms other state-of-the-art models.

The dynamic nature of the vehicular space exposes it to distributed malicious attacks irrespective of the integration of enabling technologies. The software-defined network (SDN) represents one of these enabling technologies, providing an integrated improvement over the traditional vehicular ad-hoc network (VANET). Due to the centralized characteristics of SDN, they are vulnerable to attacks that may result in life-threatening situations. Securing SDN-based VANETs is vital and requires incorporating artificial intelligence (AI) techniques. Hence, this work proposed an intrusion detection model (IDM) to identify Distributed Denial-of-Service (DDoS) attacks in the vehicular space. The proposed solution employs the radial basis function (RBF) kernel of the support vector machine (SVM) classifier and an exhaustive parameter search technique called grid search cross-validation (GSCV). In this framework, the proposed architecture can be deployed on the onboard units (OBUs) of each vehicle, which receive the vehicular data and run intrusion detection tasks to classify a message sequence as a DDoS attack or benign. The performance of the proposed algorithm compared to other ML algorithms using key performance metrics. The proposed framework is validated through experimental simulations to demonstrate its effectiveness in detecting DDoS intrusion. Using the GridSearchCV, optimal values of the RBF-SVM kernel parameters “C” and “gamma”$(\gamma)$of 100 and 0.1, respectively, gave the optimal performance. The proposed scheme showed an overall accuracy of 99.33%, a detection rate of 99.22%, and an average squared error of 0.007, outperforming existing benchmarks.

The goal of this study is to offer a novel fog data prediction and recovery (FDPR) algorithm that uses deep learning (DL) to forecast and recover missing sensor data in an Internet of Things (IoT) network. Because of the fog layer’s unique qualities compared to other IoT environment layers, the FDPR algorithm is employed in this layer. The most recent studies generally concentrate on data recovery or prediction, with few assessment metrics. In this work, an algorithm that can handle both data prediction and recovery is provided. With the proposed FDPR approach, data prediction and recovery are dealt with by an effective DL network, namely, a deep concatenated multilayer perceptron (DC-MLP). The algorithm consists of a prediction function that forecasts future sensor data for a specified round of data transmission and a recovery function that recover one or two missing data points. The evaluation of the proposed algorithm is performed with simulation and experimental works. Initially, a data set is collected, preprocessed, and fed to various DL models using$K$-fold cross-validation. These DL models are then converted and embedded into a fog layer in the experimental work with nine edge devices. In both simulation and experimental evaluation, the FDPR with DC-MLP can predict future data and recover missing data with an average accuracy of 99.89% while slightly increasing network delay by 2.5 ms compared to traditional IoT. Aside from a slightly increased delay, a 121% improvement in IoT device lifetime is achieved using the FDPR algorithm due to data transmission reduction.

Software-defined networking (SDN)-based Industrial Internet of Things (IIoT) networks have a centralized controller that is a single attractive target for unauthorized users to attack. Cybersecurity in IIoT networks is becoming the most significant challenge, especially from increasingly sophisticated Distributed Denial-of-Service (DDoS) attacks. This situation necessitates efficient approaches to mitigate recent attacks following the incompetence of existing techniques that focus more on DDoS detection. Most existing DDoS detection capabilities are computationally complex and are no longer efficient enough to protect against DDoS attacks. Thus, the need for a low-cost approach for DDoS attack classification. This study presents a competent feature selection method extreme gradient boosting (XGBoost) for determining the most relevant data features with a hybrid convolutional neural network and long short-term memory (CNN-LSTM) for DDoS attack classification. The proposed model evaluated the CICDDoS2019 data set with improved accuracy and low-complexity capability for low latency IIoT requirements. Performance results show that the proposed model achieves a high accuracy of 99.50% with a time cost of 0.179 ms.

Falsification detection is a critical advance in ensuring that real-time information about vehicles and their movement states is certified on the Internet of Vehicles (IoV). Thus, detecting nodes that are propagating inaccurate information is a requirement for the successful deployment of IoV services although only a few research studies have been carried out on Basic Safety Message (BSM) falsification. As such, this paper proposes a Randomized Search Optimization Ensemble-based Falsification Detection Scheme (RSO-FDS). The RSO technique was used to construct the proposed Ensemble-based Random Forest (RF) model. The evaluation was performed on three different datasets developed to evaluate falsification in IoV. In addition, the six most popular supervised learning (SL) algorithms were investigated to evaluate the capability of the proposed RSO-FDS, which had the best performance across all datasets. The performance metrics considered are computational efficiency in terms of prediction time, validation accuracy for overall attack classification, precision, recall, and F1 scores. For validation, the performance of the proposed RSO-FDS was further compared with results from recent works. Furthermore, the irrelevance of data balancing was illustrated for real-life IoV scenarios. The result shows that the proposed model outperformed state-of-the-art algorithms implemented in this work and related works.

Preventive measures to stop copyright infringement are yet to be implemented on current decentralized music-sharing platforms. There is no mechanism to reject modified audio before they go online, so some decentralized music platforms become places full of pirated audio files. To address this problem, a perceptual hash-based audio detection method for copyright protection in decentralized music sharing was proposed. Chromaprint, an open-source audio fingerprint program, generates a hash value to detect copyright infringement. To assess the perceptual hash technique’s robustness, Chromaprint generates a hash value from an audio file that can be modified with several signal processing attacks. The results of the detection system show that Chromaprint is very effective at spotting copyright infringement, with an average match rate of 92.64%. Deployed using a public Ethereum blockchain test network, the execution time from hashing to uploading to the IPFS distributed storage is only 616.3 ms.

Unauthorized users may attack centralized controllers as an attractive target in software-defined networking (SDN)-based industrial cyber-physical systems (CPS). Managing high-complexity deep learning (DL)-based intrusion classification to recognize and prevent attacks in the industrial Internet of Things (IIoT) networks with low-latency requirements is challenging. Moreover, a centralized DL-based intrusion detection system (IDS) leads to privacy concerns and communication overhead issues during data uploading to a cloud server for training processes in IIoT environments. This study proposes federated learning (FL)-based low-complexity intrusion detection and classification in SDN-enabled industrial CPS. This framework utilizes Chi-square and Pearson correlation coefficient (PCC) feature selection methods to select potential features, which help reduce the model’s complexity and boost performance. The proposed model evaluated the SDN and IIoT-related InSDN and Edge-IIoTset datasets. The model measurement shows that the proposed model achieves high accuracy, low computational cost, and a low-complexity model architecture compared with state-of-the-art approaches.

The constant development and deployment of the supervisory control and data acquisition (SCADA) in the industrial internet of things (IIoT) have enabled vast communication leading to the generation of large volumes of sensor data. This phenomenon has increased SCADA’s susceptibility to vulnerability and attacks which calls for attack detection mechanisms. Existing systems only aim at detection accuracy without considering the effect of false alarm rates in large sensor data. To resolve this issue, we propose a Rational Quadratic Gaussian Process Regression (RQGPR) for the effective reduction of false alarm rate and improved prediction precision. In this algorithm, a Gaussian process regression model is trained with recourse to kernel functions to precisely predict attacks and reduce false alarms. The RQGPR outperforms all other kernels in the reduction of false alarm rates. Through simulations, we show that the proposed model reduces the false alarm rate up to 71.73% higher than other kernels. This result was validated by evaluating the CIRA-CIC-DoHBrw-2020 datasets, which also had a reduction rate of 67.61%. In addition, it also showed superior performance when compared with other state-of-the-art models.

Achieving communication intelligence with a low computational cost is necessary for wireless sensor networks for the drone transportation system. This work proposed a novel localization system with low computational and time efficiency that uses an unmanned aerial vehicle (UAV) as anchor node and lightweight neural networks to evaluate the UAV position information. The scheme takes advantage of the capabilities of artificial intelligence models. Simulation results indicate that the proposed scheme displayed a good performance with the least localization error of 1.75, least training time of 0.0032s, and testing time of 0.00039s without requiring GPS when compared to other algorithms and previous schemes.

The proliferation and wide usage of the Internet of Things (IoT) and related information and communication technologies (ICT) have led to the emergence of smart cities which comprises ubiquitous sensors, and heterogeneous network architectures. These cities are capable of relaying real-time information about the world which can then be used to improve the Qualify of Life (QoL). However, due to the unprecedented access to the city and personal data by smart city applications, there is an increase in both security and privacy threat. In this study, we propose a stacked generalization machine learning algorithm for the detection of cyberattacks in a smart city. The algorithm was tested using datasets from various smart city infrastructures. Simulation results show a high detection accuracy.

This work proposes the use of blockchain for offline voting. Using the Smart contract feature of the Ethereum blockchain network. The voter’s registration is made. An offline token is generated which is used for offline voting. The offline token and the voting information are sent to the vote counters Smart contact. This is where the votes are counted and results extracted. However if a voter can share the cast vote to another voter who will send the new token to the vote counters. In a situation a vote is submitted by multiple voters, the counter has the ability to identify and count such votes only once.

Internet of Things (IoT) devices generates massive amounts of data continuously, making it difficult to combine this data at the edge node or central edge server for AI techniques to be applied, especially on low-bandwidth networks. Traditional computation offloading involves extensive resource management, queuing, and high bandwidth usage to send data from an edge device to a server. To mitigate this challenge, federated learning is applied in this paper, to offload the computation of the edge server. Simulation results show the efficiency of the proposed method over the centralized method.

Software-defined networks (SDN)-based industrial cyber-physical systems (CPS) enable customizing development opportunities with integrated network interconnection to perform monitoring, measurement, control system, and security tasks. The extensive connectivity and the vast amount of data exchange in the SDN-based industrial CPS environment make it vulnerable to cyberattacks. Furthermore, an SDN controller is a single attractive target for an attack. It is challenging when the SDN controller manages DL-based high-complexity intrusion detection in an IIoT network with low latency requirements to identify and prevent attacks. This study proposes a lightweight intrusion detection model in an SDN-based industrial CPS environment. The proposed model was evaluated using a recent publicly SDN-related cyber-security InSDN dataset. The experimental results show that the proposed model outperforms the state-of-the-art by achieving 98.95% accuracy, 99.00% precision, 98.91% recall, and a 0.164 ms time cost when using the LightGBM feature selection technique.

As unmanned aerial vehicles (UAVs) are progressively deployed for logistics purposes, there is need for paradigm shift from mere drone detection to proactive identification of the conveyed objects and proper risk assessment from a far distance. This paper proposed a timely, efficient, accurate, and situation-aware (TEAS) mission critical operation approach for detecting, localizing, and neutralizing UAVs under 3 scenarios (sunny, cloudy, and evening) and different altitudes using vision- based deep learning model. Two manually generated datasets consisting of 7200 samples from 6 UAV models and 3600 samples from 9 conveyed objects were used for simulation purposes. The proposed model was compared with 7 state-of-the-art models based on selected performance metrics. The results shows that the proposed model achieved superior mean average precision of 99.5%, 100% sensitivity, 11.2% specificity, 21.5% G-mean, and 99.8% F1-score with a latency of 0.021s, and throughput of 16.4 Gbps, which is better than other models. The model also exhibited high efficiency with cost noise at 0.037, and high reliability with minimal detection error which makes it suitable for mission critical operation of proactive and situation-aware countering of drones.

Abstract This work proposes smart auto mining (SAM) for resource‐efficient mining in a blockchain network. The SAM algorithm stops the miners when there is zero pending transaction and starts the miner when there is at least one transaction sent into the network. The miner listens to the network to identify when a transaction has been made by a node. The model does not need any instruction to start mining when there is a pending transaction. The results show that a private Ethereum network produced over 300% more blocks in a 12‐h period with 599,950 transactions compared to when SAM is applied. The proposed algorithm is also able to reduce the storage used by the chaindata by 14%. The overhead of mining is decreased by reducing the production of empty blocks in the network which saves energy, storage space, network bandwidth and computational complexity.

Solar irradiance prediction is an indispensable area of the photovoltaic (PV) power management system. However, PV management may be subject to severe penalties due to the unsteadiness pattern of PV output power that depends on solar radiation. A high-precision long short-term memory (LSTM)-based neural network model named SIPNet to predict solar irradiance in a short time interval is proposed to overcome this problem. Solar radiation depends on the environmental sensing of meteorological information such as temperature, pressure, humidity, wind speed, and direction, which are different dimensions in measurement. LSTM neural network can concurrently learn the spatiotemporal of multivariate input features via various logistic gates. Moreover, SIPNet can estimate the future solar irradiance given the historical observation of the meteorological information and the radiation data. The SIPNet model is simulated and compared with the actual and predicted data series and evaluated by the mean absolute error (MAE), mean square error (MSE), and root MSE. The empirical results show that the value of MAE, MSE, and root mean square error of SIPNet is 0.0413, 0.0033, and 0.057, respectively, which demonstrate the effectiveness of SIPNet and outperforms other existing models.

Ensuring secured and reliable dissemination of information for a mission-critical system such as the Internet of Vehicle (IoV) in real-time is of utmost importance. In this work, a False Location Detection System (FLDS) based on an optimized Ensemble Random Forest (Ens.RF) was proposed. The performance of the Ens.RF was compared with four other Machine Learning (ML) algorithms, using the Veremi dataset where five (5) different location falsification categories and one benign category were modeled. To validate the idea in this work, a performance comparison with recent work was presented. The result shows that the proposed Ens.RF outperformed other algorithms modeled in this work as well as related works with an accuracy of 99.92%

With the expanding diversity of Internet of Things (IoT), more IoT networks are now being attacked easily by aggressors without being easily distinguished as they adapt network traffic characteristics similar to the victim's network. The consequences are the opportunities to gain control of the secure communication antagonistically, influencing internal information and prompting harms to the physical components of the IoT framework without being distinguished quickly. Anomaly-based Identification (AID) has enabled the possibility of solving this problem by helping to detect novel attacks by skimming and authenticating the IoT network designs, with higher accuracy using machine learning (ML) classification. In this work, a stacked ensemble (SE) machine learning classification approach is utilized to investigate IoT network traffic characteristics for improving novel detection of malware and noxious exercise in gadgets. The SE classifier was composed of base and meta-classifiers. The base level consisted of four classifiers; decision tree (DT), gaussian support vector machine (GSVM), ensemble DT, and naive bayes (NB). This classifiers are applied to IoT Network Intrusion Dataset from HCRLab, Korea University and sent trained data to meta classifier which was further employed to detect binary classification on test data. The experimental results shows that SE classifier achieved 99.8% accuracy with lower loss value 0.0020861 and 0.9994 precision. This application can be promising insights into the AID-based security of IoT for effective industry and/or smart factory management.

This paper proposes a system to reduce sensor energy consumption by predicting the next sensor value. The current implementation of the smart factory utilizes wireless sensor network nodes to monitor the environmental condition in real-time. Instead of periodically exploiting those nodes, a deep learning prediction-based algorithm is proposed in the cluster head to reduce sensing times and increase sensor lifetime. The cluster head can learn the behavior of each sensor nodes based on its previous value. The proposed scenario can be combined with existing solutions in sensor failure detection and recovery to provide a robust solution in the industrial environment.

In this paper, an algorithm to recover failure sensor data in real-time is proposed. In the current factory automation trends, each device senses the information directly, where humans are no longer involved in huge part of the task. The ability of the system to detect the failure in the device is one of the crucial issues as well as the ability to recover the data of the device when failure happens. The deep learning technique is proposed to learn the behavior of the sensor and to provide exact data as a recovery when the failure occurred. This scenario can be applied along with a faulty detection technique to provide seamless solutions in the current industrial environment.

Recent emergent technologies with the advent of 5G use cases have made it imperative for high demands in broadband radio spectrum as compared to the current 4G technologies and other outdated ones. This study introduces an efficient spectrum management based on localisation of the primary user (PU) position in interweave cognitive radio networks (CRNs) towards 5G. In a typical network consisting of licensed system with PUs; and unlicensed system with secondary users (SUs), the SU opportunistically exploits the unused frequency bands of the licensed network provided there is no harmful interference to the PUs. Since the network is dynamic and the PUs are moving targets, the unused bands become unstable; hence, the SUs meet with limitations of detecting white spaces; finally resulting the interference. To this end, both Kriging interpolation and Kalman filter (KF) techniques for tracking and estimating the PUs position are proposed. The performance work was evaluated with respect to received signal strength indicator (RSSI) scheme. Both KF and Kriging interpolation outperformed RSSI. Moreover, the KF leads to the best performance compared to the other two as per localisation error prediction rate and system throughput; which makes it as the preferable technique to tackle mentioned challenges encountered by the SUs in such environment.

Industrial image processing and computer vision play significant role in factory automation since industries now employ human-robot interaction for the monitoring of products in areas considered risky and dangerous for humans. The challenge however, is to ensure reliability in image processing such that image sizes and image similarity index are expected to be perfect representation of actual objects. This paper investigated the statistical relationship between the image ratio size and similarity index after compression. Using correlation analysis, a statistical relationship was established between the image size ratio and similarity index of selected images under review. It was observed that an inverse and high correlation existed between the image similarity index and image size ratio of compressed images. The result of the validation shows that the proposed regression model has predictability or good fit of 95%.

This paper proposes a bio-inspired service provisioning scheme for fog-based industrial Internet of Things (IIoT). We use wild-goats algorithm to address the adaptive fog service provisioning problem. Specifically, the master node is configured to monitor the network condition of all fog nodes inside of its domain. Then, the master node will distribute a service to the most suitable fog node based on network latency, computation cost, and storage cost. Therefore, the service can be accomplished in a real-time. The proposed method also considers a probability of node failure during the service provisioning process due to the harsh condition of IIoT environment. The results show that the proposed scheme can achieve low latency compared to the traditional architectures. The results also show that the node failure can escalate the latency of fog-based IIoT network. Thus, the adaptive service provisioning scheme is essential to maintain the network performance of fog-based IIoT.

This paper proposes a load balancing technique in which data traffic is balanced by sinks in a dynamic manner. It is directed to solve the problem of overload on one sink which result in high energy consumption and poor quality of experience in that area of the network. Sinks share responsibility of data aggregation equally throughout the network, by considering energy consumption and cluster size. The simulation works show how proposed routing protocol performance is close to ideal in ensuring all sink do equal work in the network and how network life in this approach varies with allowed time threshold.

This work proposes a framework for distributed data analytic for precision agriculture based on seamless computing paradigm named SeamFarm. Generally, heterogeneous nodes deployed for precision agriculture where these nodes generated an extensive amount of data. Then machine learning can be used to analyze this data for precision agriculture. However, most of the IoT devices are resource-constrained devices, which results in poor performance while conducting a machine learning task. Thus, in SeamFarm, we consider distributing the data as well as the task to all available nodes. The results show that SeamFarm can meet all of the functional and non-functional requirements of distributed data analytic for precision agriculture. Moreover, it can obtain faster data analytic results.

This paper proposes a framework to reduce overhead of controller-switch communication for industrial SDN-based Data Center Networks (DCN). The proposal focuses on OpenFlow (OF) to reduce the number of control messages, consisting of both PACKET_IN and PACKET_OUT messages handled by the OF controller during rule installation on OF switches' flow tables and to selectively choose switches for rule installation. Extensive simulation shows significant results on reduced controller workload. The performance provides a mutual trade-off by considerably improving rule matching rate in the presence of slightly enhanced number of flow entries, conserving resources on both OF controller and OF switches for SDN-based DCN operation. As a consequence, network latency is reduced while throughput is enhanced, which offers a great promise in the future deployment of industrial DCN.

This paper studies the performance of six transmission schemes, namely single-hop, multihop, selective decode-and-forward, amplify-and-forward, incremental decode-and-forward (IDF), and incremental amplify-and-forward (IAF) schemes. For a fair comparison, these schemes are constrained in terms of maximum outage probability and end-to-end throughput. In this paper, we investigate the impact of propagation channel conditions including both line-of-sight and non-line-of-sight environments by exploiting Nakagami-m fading channel. Our simulation results show that, by utilizing a return channel, the IDF and IAF cooperative schemes achieve better performance than the other schemes in most cases.

This paper investigates a real-time water quality monitoring system by using a proposed brokerless publisher-subscriber (pub/sub) architecture framework. On the system, sensors sense the water measurement metrics, including temperature, pH, and dissolved oxygen level. All collected data are stored in a database and computed stochastically for further analysis on water quality. A complementary experiment compares the proposed pub/sub architecture and MQTT, a lightweight protocol on which IoT mostly uses, to show better performance of the proposed architecture in case of network latency and throughput for diverse message payload size, thus suggesting the future IoT implementation of the system. To complete the experiment, the relationship among temperature, pH, and dissolved oxygen is analyzed, and the experiment summarizes that water temperature is inversely proportional to pH and dissolved oxygen value.