Fault detection in the Tennessee Eastman Process (TEP) is challenged by transient dynamics, post-injection regime transitions, and score drift that destabilize fixed-threshold detectors and increase false alarms. In the proposed Risk-Budgeted Conformal Anomaly Detection (RBC-AD) framework, a calibration-based anomaly detector integrates a causal forecasting backbone, frequency-aware scoring, and conformal decisioning. The forecaster is a dilated temporal convolutional network with causal self-attention, trained on fault-free runs to predict one-step-ahead Gaussian parameters by minimizing negative log-likelihood. Detection then scores each window using a fused nonconformity measure that combines the window-averaged predictive log-likelihood with an FFT-based frequency deviation term, using robust normalization fitted on a held-out calibration split and tuning the fusion weight under leakage-controlled splits. Conformal quantiles at miscoverage level α=0.05 define normal, uncertain, and anomaly states with finite-sample correction. Run-level partitioning over 500 runs per split and post-injection sampling (3,000 normal windows, 8,000 fault windows) achieves ROC-AUC = 1.000 and PR-AUC = 1.000 under the post-injection-only protocol; full-stream results provide the deployment-relevant assessment. Static conformal thresholds (θu=0.1977, θa=0.3033) yield F1 = 0.9989 and F1 = 0.9999. Deployment selects an uncertainty threshold to satisfy an empirical normal alarm-rate target of 0.05. Full-stream evaluation reports uncertainty rate and detection delay under k-consecutive alarm logic, with optional drift-triggered recalibration using normal-stream scores.

The expansion of 6G-enabled IoT networks demands integrated solutions for optimizing computational, communication, and energy resources. Existing methods often treat power efficiency and resource allocation independently, limiting overall performance gains. This study introduces FLARE, a federated learning–based framework for adaptive resource management and optimization in 6G-IoT. FLARE integrates an adaptive learning rate scheduler ηt= η0/(1+αt), gradient sparsification (Top-k) and uniform quantization to minimize communication overhead, and model pruning with weight quantization for energy reduction. A novel dynamic route optimization algorithm jointly minimizes delay and power consumption via continuous flow-based updates, supported by a virtualization-aware server activation model. An economic optimization module applies dynamic pricingPdynamic(t) and incentive mechanisms to balance demand and cost. NS-3 simulations on Edge-IIoTset, N-BaIoT, and IoT-23 show that FLARE improves resource utilization by up to 5%, power efficiency by 4%, and network performance by 3–5% compared to leading baselines under varied demand scenarios.

The increasing complexity of 6G-IoT networks presents challenges in ensuring real-time trust assessment, computational efficiency, and security against adversarial threats. Existing frameworks struggle to dynamically adapt to evolving threats and high-volume data streams, leading to compromised decision reliability. This study proposes TANF (Trustworthy Adaptive Neural Framework), an advanced deep learning-driven trust evaluation system incorporating hierarchical processing, multi-domain trust layers, and Holo-Recursive Memory (HRM) for adaptive optimization. TANF prioritizes high-trust data streams using sensory stream balancing, dynamically allocates resources through task-specific synergy layers, and enhances memory recall by integrating past, present, and predictive state representations. The simulation, conducted in Edge-IIoTset, IoT-23 and CICIDS2017, evaluated trust assessment, computational latency, scalability, and adversarial detection. TANF achieves a precision of 92. 8%, a latency reduction of 34. 5% and a adversarial detection rate of 95. 6%, outperforming ERAI, ROBUST-6G, and IMCS.

The widespread adoption of Internet of Things (IoT) devices increases the need for trust management systems that adapt to dynamic conditions and maintain reliability under diverse threats. This paper introduces StackTrust, a trust management framework designed for scalable and precise IoT security. The framework integrates decision trees, support vector machines, and random forests within a logistic regression meta-learner to enhance classification robustness. A central feature is the adaptive weighting mechanism, which periodically adjusts the influence of each base model according to current performance metrics. To further stabilize predictions, a logarithmic historical-trust function incorporates long-term behavioral evidence while reducing sensitivity to short-term fluctuations. The combined trust score converges to a stable equilibrium under bounded model outputs. StackTrust supports both centralized and decentralized architectures and is validated through NS-3 simulations across multiple datasets and attack scenarios. Results on 45,000 instances confirm precision, recall, and F1-scores of 0.99, with computational complexity ofO(N×T) andO(M×T) to ensure efficiency for resource-constrained IoT environments.

Integrated vehicular and low-altitude Internet of Things networks require stable trust regulation under dynamic mobility, interference, and adversarial conditions. In this study, a unified edge intelligence approach is proposed to address the trustworthiness issue in modern edge intelligence. A validated constraint-consistent state information is preserved instead of raw data from multiple information sources to guide accurate information procurement and reserve-aware decision making. Vehicles, UAVs, roadside units, and edge nodes in the system are organized into shadow states corresponding to their real counterparts in a shared state space. Processed multi-source state information is represented as bounded and non-invertible representations that are structurally consistent and can be used for decision making without reconstructing the corresponding raw data. A cross-domain admissibility region is first determined by physical constraints, communication constraints, and mission constraints in the constraint domain. The geometric domain state evaluation stage then determines the scores of time and space geometric admissibility using multiple types of aerial, vehicle, and infrastructure heterogeneous witness information. Simulation results show that CODE achieves a Trustworthiness Index of 0.927, a maximum Coherence Consistency Score of 0.962, decision latency within 12.3–25.8 ms, and stability of 0.826 under high-density and multi-attack conditions.

The growing use of Internet of Medical Things (IoMT) systems demands accurate sentiment analysis, nuanced emotion tracking, and seamless integration of multimodal data. Current models often struggle when handling heterogeneous sources and evolving emotional patterns. To address these issues, this study proposes QED-Net—a quantum-inspired deep learning architecture designed specifically for IoMT environments. It introduces four modular components. The quantum-driven sentiment amplification (QSA) enhances contextual sentiment interpretation. The temporal emotion evolution graph (TEEG) captures the shifting nature of emotional states over time. The hyperdimensional quantum tensor fusion (HD-QTF) supports synchronized integration of diverse modalities. Finally, the emotion-to-medical ontology encoder (EMOE) translates emotional cues into actionable clinical signals. These components operate both independently and in synergy, allowing for flexible deployment in real-world scenarios. Simulations conducted on benchmark datasets confirm the model’s effectiveness, with QED-Net achieving 93.2% precision in sentiment detection, 92.3% in emotion tracking, and 91.6% robustness in multimodal fusion.

Sentiment analysis, or more specifically, the integration of IoMT into healthcare systems, requires frameworks that must adapt at runtime with high precision. Most existing methods have several limitations of either latency or accuracy issues, and therefore perform less effectively in dynamic scenarios. This study aims to address these challenges by proposing EmoSyn, a novel framework that incorporates Emotion Wave Modulation (EWM), Neuro-Cognitive Language Dynamics (NCLD), and the Sentient IoMT Interaction Protocol (SIP). EWM generates dynamic emotional waveforms using high-dimensional feature vectors and kernelized mappings, while NCLD employs synthetic neural mappings and adaptive linguistic modeling to capture semantic transitions. SIP facilitates real-time IoMT recalibration through bidirectional sentiment-driven feedback. Implemented using mathematical frameworks and a custom Emotion-Aware Predictive Synthesis Algorithm (EAPSA), EmoSyn ensures precise sentiment interpretation and efficient IoMT interactions. The framework was evaluated using MOSEI and MIMIC-III datasets in a Python-based simulation environment. The results showed EmoSyn achieving 91% precision for MOSEI and 86% for MIMIC-III, with average latencies of 29 ms and 27 ms.

Vehicular networks face increasing challenges to ensure security, privacy, and efficiency in dynamic communication environments.Current solutions often lack adaptability to evolving threats and efficient mechanisms for preserving privacy and reducing computational overhead.This study proposes PrivNet, a framework that integrates generative AI with advanced cryptographic and trust mechanisms to address these limitations.The framework comprises the Quantum-Augmented Holographic Cryptographic System (QAHCS) for dynamic and secure key generation, the Neural Overlap Privacy System (NOPS) for adaptive pseudonym morphing and entropy-driven identity obfuscation, and the Self-Supervised Generative Anomaly Detection (SS-GAI) module for real-time threat modeling and counter-anomaly injection.The system also incorporates Hyperledger Mesh for energy-efficient and secure transaction validation.Simulations were performed using NSL-KDD, CICIDS2017, and Car-Hacking / VeReMi datasets for 300 minutes.The results demonstrate a 12% improvement in detection accuracy, a 23% improvement in energy efficiency, and a 22% reduction in resource utilization.

The widespread deployment of Internet of Things (IoT) devices has intensified the demand for scalable and secure trust management IoT systems. Existing GNN-based approaches often neglect real-time adaptability and contextual trust in dynamic, heterogeneous networks. This study introduces TrustAware-GNN, a trust-aware graph neural network framework designed to robustly evaluate device trustworthiness in IoT environments. The model integrates a multi-dimensional trust mechanism encompassing direct, indirect, temporal, and contextual trust, computed using localized device parameters: reliability, capability, security posture, reputation, and location awareness. Trust values modulate edge weights within the graph, enabling trust-adaptive message propagation. A trust-threshold-based edge formation mechanism filters unreliable links, while attention-based aggregation refines node embeddings. The model continuously adapts to behavioral shifts via time-decayed trust updates and contextual similarity matching. Simulation was conducted across IoT-23, EDGE-IIoTSET, AutoTrust, and ToN-IoT datasets. TrustAware-GNN achieved 94.83% accuracy on EDGE-IIoTSET and 93.25% on IoT-23, outperforming MGNN, STAR-GCN, and SEGC-PP in both accuracy and adaptability under dynamic trust scenarios.

Quantum computing offers exceptional computational capabilities, but achieving optimal performance and resource efficiency in practical applications remains challenging. Addressing the gap between theoretical quantum algorithms and their real-world implementation, this study introduces QuantGAN, a novel approach designed to enhance sustainability and security in Internet of Things (IoT) and consumer electronics manufacturing. QuantGAN combines state-of-the-art quantum algorithms and generative adversarial networks (GANs) over a multilayered Digital Twin framework. This enables explicit sustainability risk assessment with quantum computing and latent process optimization via GANs. The Digital Twin, foreseen as an interactive metaverse interface, enables a real time touch-and-go framework. Central modules within GENESIS include a multilayered Digital Twin, quantum risk assessment algorithms, and an AI-driven continuous feedback loop orchestrated by GANs. The simulation environment uses Qiskit on Intel Core i7-10700K CPU with 32 GB RAM using Ubuntu 20.04 LTS. Our experimental results show that QuantGan effectively out performs the existing methods achieving 96.4% accuracy in detecting risk.

The prompt expansion of Internet of Things (IoT) devices necessitates advanced security frameworks to protect data integrity, confidentiality, and availability in resource-constrained environments. Traditional security solutions are often resource-intensive for IoT devices with limited computational power and energy resources. This study addresses these inadequacies by proposing a novel approach formulated to such constraints. This study propose the trust-enhanced lightweight security framework (TELSF), integrating two novel components: 1) the adaptive lightweight encryption algorithm (ALEA) and 2) the trust-aware data protection model (TADPM). ALEA employs dynamic key generation through a lightweight hash function, ensuring unique and regularly updated encryption keys based on device context and behavior. TADPM enhances this framework by continuously assessing device trustworthiness through direct interactions, aggregated feedback from neighboring devices, and contextual parameters, such as location and device capabilities. Performance evaluations demonstrate that TELSF significantly enhances security and operational efficiency, reducing computational overhead by 18%, improving energy efficiency by 20%, and increasing data transmission security by 10% compared to existing solutions.

Ultrasound imaging continues to play a critical role in prenatal diagnostics, but accurate interpretation remains hindered by limited labeled data, inconsistent pseudo label quality, and real-time processing constraints in Internet of Medical Things (IoMT) environments. Existing semi-supervised learning (SSL) frameworks fail to maintain reliable segmentation under these dynamic and resource-constrained conditions. This study proposes ADLIFT, a real-time SSL-based ultrasound processing framework designed to optimize diagnostic accuracy and computational efficiency. The approach integrates an Adaptive Dual-Layer Perception (ADLP) mechanism combining macro-level anatomical recognition with micro-level feature refinement, and a Dynamic Label Generation (DLG) module that iteratively improves pseudolabel reliability using confidence-driven feedback. Efficient Sparse Feature Extraction (ESFE) minimizes computational overhead by isolating high-activation regions, while the Temporal Contextualization Framework (TCF) ensures inter-frame consistency. Blockchain-enhanced edge computing supports secure and scalable IoMT deployment. Evaluations in HC18, FetalPlane18 and Kvasir-Segment datasets demonstrate precision of 93. 7%, decision stability of 92. 8%, interpretability index of 91. 5%, uncertainty handling efficiency of 89. 7%, trust reliability score of 95. 3%, and processing latency of 28.1 ms per frame.

The significant increase in the number of Internet of Things (IoT) devices in various domains requires robust and adaptive authentication mechanisms. Existing methods often fail to address the dynamic and heterogeneous nature of the IoT ecosystem, resulting in significant security vulnerabilities. This paper presents a context-aware LLM-enhanced authentication framework (CLAF-IoT) that dynamically adjusts authentication protocols based on real-time environmental and user-specific contexts. Using the advanced contextual understanding and generation capabilities of Large Language Models (LLMs), the proposed framework enhances both security and usability in highly dynamic IoT environments. Key components include environmental context sensing, user behavior analysis, adaptive authentication protocols, real-time threat detection, and federated learning integration for continuous improvement and privacy preservation. Experimental evaluations demonstrate that CLAF-IoT achieves higher authentication accuracy in different scenarios, 11.11% false acceptance rate and 9.09% false rejection rate.

Connected Autonomous Vehicles (CAVs) need a reliable communication structure which enables the complete evolution of transportation systems. Our proposed trust management strategy implements Quantum Key Distribution (QKD) and blockchain technology for solving CAV network security and coherence problems. The system applies QKD to produce unbreakable quantum keys which defend vehicle communication networks and blockchain systems strengthen governing networks by decentralizing operations and ensuring transparency and credibility. This framework employs QKD together with blockchain technology through a structure that demonstrates adaptability to changing networking conditions and cyber dangers within CAV environments. The independent operational mindset allows for automatic security rule updates that result in steady improvements to the encryption standards and system protocols. The framework demonstrates its functionality through simulations performed on multiple traffic conditions featuring different speeds together with density levels. The analysis included evaluation of energy efficiency together with overhead ratio performance as well as QKD success rates and blockchain verification duration. The testing results demonstrate that this system performs effectively under different operational scenarios while demonstrating strong defense capabilities against Sybil and Wormhole security attacks. Under dense traffic scenarios, Quantum-Secured Trust Management Framework (QSTMF) delivered improved network throughput reaching 15% above current models while high-speed traffic circumstances led to 20% diminished blockchain verification times. Attack detection rate performance of the framework exceeded 90% throughout multiple attack simulations indicating its robust capabilities for vehicle communication protection.

The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of the Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in Industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

Authentication receives enormous consideration from the research community and is proven to be an interesting field in today’s era. User authentication is the major concern because people have their private data on devices. To strengthen user authentication, passwords have been introduced. In the past, the text-based password was the traditional way of authentication, but this method has particular shortcomings. The graphical password has been introduced as an alternative, which uses a picture or a set of pictures to generate a password. In the future, it is a requirement of such approaches to maintain robustness and consume fewer energy resources to become suitable for the Green Internet of Things (IoT). Similarly, diverse graphical password authentication mechanisms have been used to provide users with better security and usability. In this article, we conduct an extensive survey on the existing approaches of graphical password authentication to highlight the challenges required to be addressed for Green IoT. In comparison to other existing surveys, the objective is to consolidate the graphical password technique and to identify the problem associated with it. Besides, this survey will also identify the vulnerabilities of the graphical password against several potential attacks. We have also examined the strengths and weaknesses of each technique along with the future research directions. This study also evaluates the usability of each approach by considering learnability, memorability, and so forth and also presents a comparative analysis with security.

Internet of Medical Things (IoMT) provides a diverse platform for healthcare to enhance the accuracy, reliability, and efficiency. In addition, it utilizes the productivity of available equipment to improve patients’ health. IoMT also provides distinct ways by which healthcare will be revolutionized as it provides numerous opportunities to handle operations with precision. However, numerous advantages have raised several security challenges, such as trust, data integrity, network constraints, and real-time processing among others. There is a requirement for a robust approach to maintain data integrity along with the behavior detection of nodes to completely maintain a secure environment. In the proposed approach, the mechanism is capable of maintaining a robust network by predicting and eliminating malicious nodes. The proposed NeuroTrust approach utilizes the trust parameters to evaluate the degree of trust that include reliability, compatibility, and packet delivery. This approach also lightens the two-way computation burden and uses a lightweight encryption mechanism to further enhance the security and integrity during data dissemination, which is required for the digital revolution in delivering efficient high quality healthcare. The performance of the proposed approach has been extensively evaluated against the absolute trust formulation, accuracy of trust computation, energy consumption, and several potential attacks. The simulation results show the effective performance to identify malicious and compromised nodes, and maintain resilience against various attacks.