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.

With the rise in industries and population, electricity demand is increasing daily. Microgrids play a crucial role in providing green energy by utilizing renewable energy resources. Microgrids not only help meet the growing electricity demand but also reduce global warming and greenhouse effects. However, many homeowners are hesitant or reluctant to share their excess energy resources with other Microgrid or traditional electric grid users. In this article, we propose a hybrid deep learning and machine learning stacking model named GreenTrust. GreenTrust consists of three evaluation deep learning models at the base level and a single machine learning model at the meta-level. GreenTrust first establishes trust among home users using trust parameters. Once trust is buildup, a Microgrid can share its resources with other grid users. Results show that the hybrid model outperforms than other standalone machine learning schemes, such as Random Forest, XGBoost, and AdaBoost, in terms of accuracy, precision, recall, and F1 score.

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.

In the rapidly evolving domain of the Internet of Vehicles (IoV), ensuring robust trust management, privacy, and security presents significant challenges. This article proposes a novel approach integrating generative AI (GAI) and federated learning (FL) to address these challenges. FL allows distributed learning across vehicles without the need to share data, enhancing privacy compared to centralized methods. Our approach enhances trust management by raising the level of accuracy in detecting anomalies and preserving data privacy. As a result, the effectiveness of the proposed approach in practical real-world urban settings is illustrated by comprehensive evaluations using the CityPulse dataset. The results show a 20% improvement in trust scores under normal conditions, a 92% anomaly detection accuracy, and acceptable latency despite the added security measures. Additionally, 3-D visualizations illustrate the system’s robustness and scalability. This solution aligns with the objectives of 6G wireless communications, laying the groundwork for future intelligent, ultrareliable, and secure vehicular networks. Future research will focus on expanding the application of GAI and FL for real-time decision-making in large-scale IoV networks and optimizing cryptographic protocols.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

This article aims at analyzing and comparing an adaptive algorithm-based method for improving the performance of Internet of Things (IoT) systems through simulation studies. Concentrating on active and complex scenarios, the study presents new proposals for secure and smart learning of routes, activity forecasting for nodes, link stability estimation, and flexible resource management. These methods are benchmarked against conventional algorithms to evaluate the effectiveness of the proposed solution based on routing efficiency, traffic prediction, link, resource consumption, network response time, and energy requirements. The findings are encouraging, the adaptive algorithms do improve dramatically on the standard ones making the system slower and consuming much less power. From the findings of the study it can be concluded that using adaptive algorithms in IoT can have a high impact in terms of improvement in efficiency as well as sustainability. We conclude this work by providing some directions for further research and development in the IoT field.

As the Internet of Vehicles (IoV) continues to evolve, the imperative for advanced algorithms capable of managing increased network demands, ensuring data security, and boosting overall system efficiency becomes crucial. This article introduces a novel suite of algorithms designed to enhance IoV system performance across multiple metrics. Our comprehensive simulations contrast the proposed system with three contemporary approaches the two-layer computing resource management (TCRM) model, the federated edge learning (FEL) approach, and the blockchain-based trust-value management (BTVM) approach. We demonstrate significant improvements: a latency reduction to as low as 90 ms, compared to 118 ms in TCRM, 125 ms in FEL, and 120 ms in BTVM; reliability in packet delivery with an enhancement from an initial 98% to 99.9%, compared to 98.5% in TCRM, 97.8% in FEL, and 99.5% in BTVM; resource utilization efficiency that surpasses baseline models by maintaining rates up to 85%, compared to their 60–65% in TCRM and FEL, and 75% in BTVM; and swift network response times peaking at just 50 ms, against 60 ms in TCRM, 65 ms in FEL, and 50 ms in BTVM. Additionally, our algorithms maintain robust data security levels, consistently achieving 100% effectiveness, compared to 99.2% in TCRM, 98.9% in FEL, and 99.5% in BTVM. These results underscore the proposed system’s potential to significantly outperform existing solutions, paving the way for more resilient and efficient IoV architectures. The integration of these algorithms into real-world IoV applications can substantially contribute to the advancement of intelligent transportation systems.

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.

This study presents a federated learning (FL) framework tailored for uncrewed-aerial-vehicle (UAV)-enabled Internet of Things (IoT) networks, addressing challenges in efficiency, robustness, and scalability. The proposed system improves model learning with a 14.9 percentage point increase in accuracy (75.5%–90.4%) and a 69.2% reduction in loss over ten training epochs. It demonstrates resilience, limiting accuracy reduction to 7% under simulated attacks, and scalability with a linear increase in processing times as network size grows. High anomaly detection rates (92%) further enhance network security and reliability. These results validate the framework’s effectiveness in UAV networks and highlight its broader potential for IoT applications. Future work will explore further enhancements and diverse applications.

The convergence of the Internet of Things (IoT) and Software-Defined Networking (SDN) has paved the way for a new technological paradigm in Healthcare Industry 5.0. This integration addresses the complexity, heterogeneity, and dynamic nature of smart IoT devices within healthcare systems. However, it also increases the risk of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks, which pose significant threats to such critical infrastructure. While Deep Learning (DL)-based intrusion detection methods have demonstrated high accuracy in detecting these attacks, their opaque decision-making process often leads to their characterization as black-box models, limiting their practical use for security analysts. To overcome these challenges, this study proposes an explainable hybrid model for DDoS attack detection in SDN-IoT-based Healthcare Industry 5.0 environments. The model combines the strengths of Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in network traffic. Implemented using an SDN controller, the model accurately classifies DDoS and IoT attacks while providing transparency through the SHapley Additive exPlanation (SHAP) method, which identifies the most influential features in the model’s decision-making process. Simulation results on the CICDDoS2019 and IoT Healthcare Security datasets demonstrate the model’s effectiveness, achieving detection accuracy of 99.59% and 98.12%, respectively. These findings confirm the robustness of the proposed hybrid model compared to state-of-the-art methods for detecting potential attacks in Healthcare Industry 5.0 systems.

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.

This paper explores the transformative potential of integrating Augmented Intelligence with Internet of Things (IoT) in autonomous vehicles, a concept we term AIoT. We begin by examining the critical roles of IoT and augmented intelligence in automotive technology, delineating their evolution and synergistic benefits when unified. The crux of our investigation lies in the intricate fusion of these technologies, addressing key elements such as data acquisition, processing, and real-time decision-making, particularly in enhancing traffic coordination, vehicle safety, and energy efficiency. We place a strong emphasis on the practical applications of AIoT in autonomous vehicles, underscoring advancements in sensor data integration and vehicle-to-environment communication. Our discussion also navigates through the challenges and limitations currently faced, including data privacy, real-time data processing demands, and technological constraints. A case study is presented, offering a quantitative and algorithmic perspective on AIoT implementation in modern autonomous vehicles. Concluding, the paper casts a vision for the future of AIoT in the automotive sector, pinpointing areas for potential breakthroughs and further research. This study asserts the indispensable role of AIoT in revolutionizing autonomous vehicle technology, setting a new benchmark in automotive innovation.

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.

Trustworthy transmission is a beneficial step toward the success of the new era of telecommunication technologies and online social networks (OSNs). Many sensitive applications can benefit from OSNs, e.g., eHealth and medical services. However, OSNs have always been prey to Sybil attacks where numerous fake nodes are being generated and propagated in social networks to mimic like real nodes for the purpose of achieving malicious goals. Thus, for security reasons and for the sensitivity of data used in eHealth applications, such fake nodes have to be detected and deactivated immediately. The emerging field of the Internet of Medical Things (IoMT) promotes trust management (TM) among various IoMT devices to provide accurate and reliable communications, which is quite essential in critical diseases such as COVID-19. TM provides a secure platform to IoMT devices using different security protocols in the IoMT network. Generically, if a device is not comfortable to connect with additional devices in a network, the motive of the communication process is not succeeded and leads to disappointment for one device toward others. To handle these types of situations, a TM mechanism, named fuzzy-based TM mechanism for preventing Sybil attacks in the Internet of Medical Things (FTM-IoMT), is proposed. The FTM-IoMT provides TM for the users of eHealth systems using IoMT infrastructures. It is an intelligent mechanism to recognize Sybil or untrustworthy nodes in the system. The proposed mechanism helps IoMT nodes to collect authentic and credible information from their neighboring nodes as well as to neglect Sybil nodes. The trust value of a node is evaluated using fuzzy logic processing followed by the trust attributes, such as integrity, receptivity, and compatibility of a node. The FTM-IoMT provides a double evaluation check based on fuzzy logic processing and fuzzy filter. The proposed scheme shows superior results when compared to the state-of-the-art approaches.

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.

The smart grid emerges as a new era of the electronic power grid. It integrates advanced sensing technologies, communications, and controlling methods that tell how electricity travels from different generation points to consumers. In order to fulfill customers' satisfaction and two way communications, a huge number of smart meters are deployed in different countries for real-time consumption and presentation of the rigorous energy usage. The privacy of industrial ecosystems may require greater attention while considering minimum network load, lower computational resources, better energy efficiency, and accuracy of data. Different research works have been done to tackle customers' privacy, but at the cost of using more computational resources, communication overhead, and hiring of a trusting third party. In this article, we have proposed a symmetric encryption scheme for industrial ecosystems in the Internet of Thing (IoT) environment. The performance evaluation and security analysis demonstrate successful user privacy and integrity with lower computational resources and communication overhead.

Internet of Things has revolutionized the ways and means of use and management of electric grid systems. Now, the old mechanical grids are equipped with smart devices that not only automate the traditional grid but enable two way communications between the user and power suppliers called smart grid. Although, a lot of protocols have been developed to enable a secure communication between suppliers and consumers, cyber-physical systems (CPSs) are prone to privacy issues where adversaries may have access to particular users' information. In this article, an anonymous orthogonal code-based privacy preserving scheme, named ALPHA, is proposed for CPSs. The CPS is considered as a basic unit of the modern smart grid, which aggregates the power consumption from smart devices by keeping the user information confidential, anonymous, and untraceable. The proposed scheme, ALPHA, uses orthogonal bit codes and a systematized method to authenticate and manage the anonymity and untraceablity of user data along with low communication and computation overheads.

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices, wearables, sensors, and micro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions.