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In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC.

The Space-Air-Ground Integrated Network (SAGIN) offers a promising solution for seamless connectivity, high data rates, and wide-area coverage. However, its multi-segment architecture poses significant challenges in efficient resource management and Quality of Service assurance across diverse services. To address these challenges, we propose an Intent-Based Networking (IBN) system that streamlines network automation within the SAGIN ecosystem. Our system model integrates an IBN module with the SAGIN infrastructure, allowing network operators to express their service intents, such as Low-Latency Virtual Network Requests (LLVNRs) and High-Bandwidth Virtual Network Requests (HBVNRs), along with their respective QoS requirements. To tackle the inherent complexity, we employ a Deep Deterministic Policy Gradient (DDPG) based DRL-IBN framework. The DRL agent interacts with the SAGIN environment using a feature matrix extracted via Graph Neural Network (GNN), facilitating informed decision-making for resource allocation based on VNR acceptance. Through extensive simulations and numerical evaluations, we demonstrate the superiority of our proposed DRL-IBN algorithm over baseline approaches, such as LC-VNE and RW-MM-SP, in maximizing system utility, ensuring QoS satisfaction, and enabling efficient resource utilization in the dynamic and heterogeneous SAGIN environment. Our results indicate that the DRL-IBN framework achieves higher VNR acceptance ratios, better resource utilization, and more effective QoS assurance based on minimum QoS violation, proving its effectiveness and robustness in managing the complexities of the SAGIN network.

As network systems advance and become more sophisticated, the associated security challenges grow more complex. The zero trust model emerges as a new paradigm to address this, significantly emphasizing robust and continuous identity verification. Multi-factor authentication (MFA) methods have become crucial for enhancing authentication security within this framework. Additionally, the integration of blockchain technology is increasingly recognized for its potential to strengthen identity trustworthiness further, complementing the zero trust approach by providing a more secure and transparent identity verification process. However, privacy concerns remain, especially in public blockchain environments where personal data is vulnerable to inadvertent exposure. Also, using centralized servers for authentication, even in systems integrated with blockchain, presents the risk of creating single points of failure. This paper introduces a privacy-preserving MFA system that harnesses the decentralized capabilities of blockchain technology to enable a Distributed Authentication Mechanism (DAM) as a network of authenticators for enhancing the reliability of the authentication process. This system utilizes blockchain-based Zero-Knowledge Proofs (ZKP) as a privacy mechanism to prove the knowledge of a One-Time Password (OTP). This approach not only ensures the authenticity of the proof authenticity but also confirms the identity of the prover. In the final stage of the MFA process, non-transferable, non-fungible tokens (NFTs) are employed as authentication tokens for identity verification. Our experimental results and comparative security analyses suggest a relevant contribution to secure, private, and dependable MFA framework research.

Network slicing in 5G is a solution to accommodate a wide range of services. It also enables the network operators to establish multiple end-to-end (e2e) logically isolated and customized networks with shared or dedicated resources over the same infrastructure. Although, many tools and platforms have been developed to accomplish the management and orchestration (MANO) of e2e network slicing automatically, it is still challenging. Each of these platforms requires expertise and manual effort to define the requirements for the provisioning of the resources. The other issue is the generation of well-defined network slice configurations with lifecycle parameters. To this end, this paper proposes an efficient solution that automates the configuration process and performs the management and orchestration of network slices. This solution contains a one-touch Intent-based Networking (IBN) platform that effectively orchestrates and manages the lifecycle of multi-domain slice resources. IBN automates the process of slice configuration generation, service provisioning, service update, and service assurance by eliminating experts and manual effort. Furthermore, it has an intelligent Deep Learning (DL) based resource update and assurance mechanism which handles the run-time resource scalability and assurance.