Intent-Based Networking (IBN) is emerging as a powerful paradigm for managing complex, adaptive systems by translating high-level user policies into automated infrastructure behavior. To meet these intents effectively, especially in dynamic and data-driven environments, IBN increasingly depends on Artificial Intelligence (AI) for intelligent decision-making and task automation. While AI enhances the responsiveness of IBN, it introduces new challenges in data privacy and regulatory compliance—particularly in environments where sensitive personal data is continuously collected and learned. A central issue arises from the Right to Be Forgotten (RTBF) under the General Data Protection Regulation (GDPR), which requires that user data—and its learned influence—be fully removed upon request. However, conventional unlearning methods that rely on full model retraining are resource-intensive and impractical for real-time systems. To address this, this paper proposes an intent-driven machine unlearning framework that integrates meta learning, redactable blockchain, and Secure Multi-Party Computation (MPC), all coordinated through IBN. In this framework, unlearning is formulated as a targeted removal of a data point’s influence from the model without retraining, using implicit gradients. The redactable blockchain ensures compliant and auditable logging, while MPC supports secure, decentralized redaction. IBN orchestrates the process by aligning unlearning actions with privacy intents. Experimental results demonstrate that the proposed framework achieves up to 2.2% higher accuracy than baseline meta-learning approaches and 2.78 times greater unlearning efficiency compared to retraining, while maintaining strong resistance to membership inference attacks. This demonstrates its suitability for scalable, regulation-compliant AI unlearning.

Artificial Intelligence of Things (AIoT) systems are increasingly deployed in smart cities to enable automation, resource optimization, and real-time decision-making. How-ever, large-scale deployments face significant challenges, in-cluding device-level resource limitations, communication over-head, synchronization inefficiencies, and security threats such as data and model poisoning. To address these issues, a digi-tal twin-assisted collaborative learning framework is proposed. Resource-constrained devices are virtualized at home edge servers to offload computationally intensive training, while Multi- access Edge Computing (MEC) nodes equipped with Zero-Touch Networking (ZTN) autonomously orchestrate training policies. Snake learning is adopted to reduce synchronization delays and communication costs compared with federated and split learning, and Harris Hawks Optimization is applied to select participants based on trust, resources, and latency. Robustness against ad-versarial updates is ensured through a trust-weighted Adaptive Multi-Krum aggregation mechanism, while a permissioned blockchain provides tamper-proof auditability and accountability. Experimental results on a smart home intrusion detection dataset demonstrate a 50-65% reduction in communication, 30-45% reduction in computation and energy consumption, and Fl- scores above 95 % even under 40 % adversarial participation.

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges.

This study proposes an intelligent approach to identifying an injured soldier on blockchain-integrated Internet-of-Battlefield Things (IoBT) employing unmanned aerial vehicles (UAVs). The intelligent approach combines a unique deep learning (DL) model with a smartwatch-based heart-rate (HR) data collection technique. Different activation functions (i.e., MISH and Leaky rectified linear unit) are used in the proposed DL model to enhance the identification task by extracting the in-depth features from the images. Furthermore, a smart-watch-based HR data analyzing technique is introduced to confirm the injury of a soldier. However, due to the UAV’s low battery capacity, the identification task is offloaded to the neighboring edge computing server to improve system performance. Moreover, to restrict the access of registered IoT devices (e.g., UAV, smartwatch, etc.) and protect the sensitive data leakage on IoBT, a blockchain-integrated access control (ACL) mechanism is utilized. Detailed experimental results are provided for the proposed DL model that outperforms existing DL models. Besides, implementing a smartwatch-based HR data analysis technique for the soldiers improves the outcome of the proposed DL model. To provide a fine-grained data protection mechanism in the proposed system, a private blockchain-based ACL management policy is constructed utilizing hyperledger, and various assessment metrics have been scrutinized.

This article presents a deep learning (DL) model integrated automated and secure garbage management scheme using unmanned any vehicle (UxV) to minimize the human effort in terms of the traditional garbage management system. Different kinds of UxV (unmanned aerial vehicles, automated guided vehicles, unmanned surface vehicles, unmanned underwater vehicles, etc.) are utilized to establish an automated garbage management scheme to collect and place the garbage both from the ground and sea surfaces. However, due to the limited battery capacity and inadequate resources of different UxV, a lightweight DL model is developed to detect the garbage successfully with a higher accuracy rate. The proposed lightweight DL model uses two activation functions named MISH and rectified linear unit to enhance the feature extraction and detect the garbage. Moreover, a multiaccess edge computing (MEC) server is allocated in the proposed scheme to improve the Quality of Service (QoS) (i.e., reduce latency and improve security). Furthermore, a blockchain-based secure hazardous garbage (e.g., infectious, toxic, or radioactive materials) tracking technique is concluded in this scheme to identify the individual and reduce the potential harm to the environment. Experimental results demonstrate that the UxV can successfully detect the garbage using the proposed lightweight DL model within a minimum time frame and the obtained accuracy is higher than the other existing DL models. Besides, QoS has been investigated to verify the efficacy of the proposed scheme. Finally, a private blockchain network is established to demonstrate the performance of the proposed hazardous garbage tracking technique.