Transactions on Emerging Telecommunications Technologies最新文献

Vehicular Ad Hoc Networks (VANETs) play an essential role in intelligent transportation systems. However, the dynamic mobile nature of VANETs makes them vulnerable to a wide range of anomalous behaviors. To address these issues, we propose X-ExTEN-ID, an explainable stacked ensemble framework for adaptive and intelligent anomaly detection and vehicular security enhancement in VANET environments. The proposed framework involves spatiotemporal data analysis and multiple ensemble learning techniques combined through a stacked meta-learning architecture to identify anomalous behaviors in real-time VANET environments accurately. Instead of traditional single-model approaches, our ensemble strategy combines multiple base learners to enhance detection accuracy and ensure protection against evolving attack strategies. To validate our suggested approach, we utilized the VeReMi dataset, which provides real-world urban vehicular mobility tasks with labeled position falsification and other attack types. Experiment results demonstrated that our framework achieves notable scores in detection performance, with the highest recall rate of 98.9%, an F1-score of 99%, and a notably low False Positive Rate (FPR) of 1.3% and False Negative Rate of 0.9%, compared to existing machine learning models.

Wireless networks offer significant advantages in disaster scenarios, enabling critical communication for rescue operations in emergencies like earthquakes, floods, and hurricanes. Technologies such as cognitive radios can address communication challenges in such high-stakes environments, with modulation recognition techniques enhancing reliability in disaster responses. This study focuses on deep learning for modulation recognition, a task complicated by the need to balance recognition accuracy and system complexity. A comprehensive dataset was developed, covering eight modulation schemes across varying signal-to-noise ratios (SNR) from −15 to 25 dB, represented as image data in both in-phase/quadrature (IQ) and radius-r/angle-θ ($$) domains. Using transfer learning with convolutional neural network (CNN)-based architectures like ResNetV2 models (50, 101, and 152 layers), which are pre-trained on ImageNet, the models were adapted for this specific task. Performance metrics, including accuracy, precision, recall, and F1 scores, show that as SNR exceeds 5 dB, these models achieve over 50% accuracy, nearing perfection at 20 dB in either IQ or $$ domains. However, in low SNR conditions, the $$ domain transformation demonstrates superior recognition advantage, with the models achieving up to 86% accuracy gain at −5 dB. Ultimately, the $$ transformation significantly enhances recognition performance, proving essential for reliable modulation recognition in complex communication scenarios.

To address issues in traditional vehicular network trust mechanisms such as the untrustworthiness of centralized reputation servers, threats to user privacy, and limited detection scope a blockchain-based vehicular network anomaly detection and reputation model with privacy protection is proposed. Leveraging blockchain technology, a distributed and trustworthy reputation update framework for vehicular networks is designed. The evaluation data is encrypted and computed using a multi-key fully homomorphic encryption technique, which reduces the danger of user privacy leakage. An adaptive adjustment approach is implemented for the retrospective time interval to improve anomaly detection. This strategy stops hostile cars from evading detection by exploiting reputation updates. According to the simulation results, which demonstrate accuracy with low false positive rates in identifying malicious cars, the suggested method effectively protects user privacy while attaining high anomaly detection rates. The detection rate for unusual vehicle behavior is increased by 38.56% as compared to conventional systems. This increased detection rate implies that the technique is better at differentiating between typical and anomalous processes, which improves network dependability and safety.

Implementing IP-based networking in vehicular communication scenarios is expected to facilitate the introduction of a wide range of security-enhanced roadway applications. Mobility management is one of the most challenging research areas, playing a vital role in enabling seamless mobility services within a secure vehicular environment. Mobile Internet Protocol version 6 (MIPv6) and Hierarchical Mobile Internet Protocol version 6 (HMIPv6) are two security-based mobility management solutions or protocols designed by IETF for IPv6 wireless communication networks. Utilizing these two schemes, this article presents a security enhanced novel Intelligent Mobility Management Support (IMMS) scheme that selects better alternative out of the two already presented relay vehicle selection schemes that is, Clustering based Optimal Relay Vehicle Selection (CORV) selection scheme and Adaptive Priority and Optimum Parameters (APOP) based optimal relay vehicle selection scheme, depending upon moving vehicle's mobility and its unique characteristics. In the proposed IMMS scheme, analytical modeling has been carried out for the performance evaluation of APOP and CORV selection schemes. Due to the similarity of the network architecture proposed for APOP and CORV with MIPv6 and HMIPv6, respectively, the performance comparison is done based on the Total Cost Function, ‘$$’ with security enhancement. A comparison of performance in terms of ‘$$’ is done against some key parameters. It is then demonstrated that the proposed IMMS scheme yields the minimum cost in terms of ‘$$’, and Packet Delivery Cost, ‘$$’, compared to the CORV and APOP selection schemes.

The convergence of Internet of Things (IoT) technologies with Vehicular Ad-hoc Networks (VANETs) serves as a critical infrastructure for intelligent transportation systems. It enables vehicle-to-vehicle and vehicle-to-infrastructure communications. However, the dynamic and heterogeneous nature of VANETs creates significant security challenges. Particularly, in detecting novel intrusion patterns when labeled data is limited. Traditional intrusion detection systems struggle with rapid adaptation to emerging attack vectors, often requiring extensive retraining and substantial labeled datasets. We present MAFSID (Multi-Agent Few-Shot Intrusion Detection), a collaborative learning framework utilizing multiple specialized detection agents through few-shot learning paradigms. Our system employs five distinct agents, i.e., Network Traffic Analyzer, Anomaly Detector, Behavior Analyzer, Protocol Analyzer, and Threat Classifier. These agents communicate and share knowledge to collectively identify intrusions with minimal training samples. Comprehensive evaluations on public datasets such as NSL-KDD, CICIDS2017, and In-Vehicle datasets demonstrate MAFSID's superior performance. Furthermore, robustness analysis reveals high resilience against adversarial attacks, especially communication jamming, showing minimal impact on performance. Notably, the collaborative agent framework enables rapid adaptation to new attack patterns while maintaining robust performance across diverse network conditions.

Federated learning (FL), as an advanced distributed learning paradigm, faces significant challenges, particularly in terms of slow convergence and instability, which are exacerbated by heterogeneous data distributions. A critical issue in this context is data heterogeneity, which increases gradient estimation variance and drives the model toward local minima that are distant from the global optimum. Previous studies have primarily focused on using Control Variates (CVs) to reduce gradient estimate variance without introducing bias. In this work, we propose a novel distributed gradient optimization framework, FedNCV, aimed at effectively reducing gradient variance. Central to this approach is the use of the REINFORCE Leave-One-Out (RLOO), a CV-based technology, which serves as the core gradient estimator for FedNCV at both the client and server levels. We have developed an algorithm based on FedNCV and provided three theoretical results. Experimental evaluations demonstrate that the proposed method enhances performance. The dual structure of FedNCV equips it to address the challenges of data heterogeneity and scalability in federated networks, offering a promising solution for applications in heterogeneous FL environments. Additionally, the efficacy of FedNCV was validated across four diverse datasets under a Dirichlet distribution with $$, setting new performance benchmarks when compared to six leading methods.

In the evolving landscape of the Space Air Ground-Integrated Network (SAGIN), addressing the challenges of limited labeled data and the need for adaptable models is crucial for effective data processing across diverse and heterogeneous sources. Weakly supervised few-shot semantic segmentation (WSFSS), aims to segment the unseen targets by solely relying on support images with class-level annotations, offering a robust solution to the complexities inherent in SAGIN environments. Existing works usually generate pseudo masks for training images, and then convert WSFSS to the plain few-shot semantic segmentation task. However, these rough pseudo masks, almost always, contain background noise due to imprecise object localization during mask generation, which thus leads to undesirable segmentation results. To mitigate the above challenge, we inject implicit text supervision into WSFSS, and propose an efficient text-guided hierarchical attention (ETHA) framework to explicitly alleviate the mask noise issue. Specifically, we first propose a cross-modal interaction attention module to capture comprehensive object guidance from text embeddings and refocus the model's attention toward the area of the focused object. In addition, we propose a lightweight dual visual cross-attention module to efficiently aggregate the contextual information among each branch and common object clues from both branches, which provides enhanced visual features to facilitate the cross-modal information interaction. Based on single-scale features, ETHA has established new state-of-the-art results on the golden WSFSS datasets, that is, PASCAL-$$ and COCO-$$. These results highlight ETHA's potential for improving accessibility and efficiency for robust applications in SAGIN environment.

The operation of Unmanned Aerial Vehicles (UAVs) in low-altitude airspace is a key component in the Space-Air-Ground Integrated Network (SAGIN). Efficient and rational path planning is essential for UAV operations. Existing path-planning algorithms typically rely on uniform-grid models based on Cartesian coordinate systems, which seldom account for the unique characteristics of UAV terminal airspaces. UAV terminal airspaces are often defined as cylindrical volumes where multiple UAVs converge at vertiports, allowing for higher operational densities compared to en-route airspaces. While a fine-grained grid model is essential for UAV terminal airspace, it is inefficient for en-route airspace due to excessive computational costs. This paper presents a path-planning approach based on a variable grid model to find the optimal path while effectively utilizing airspace resources and minimizing computational overhead. Specifically, for the UAV terminal airspace, we propose the Grid-Optimized A* Path-Planning (GO-APP) algorithm, which establishes a sector-grid model based on a cylindrical coordinate system to find the optimal path. Extending to the en-route airspace, the Variable-Grid A* Path-Planning (VG-APP) algorithm integrates the GO-APP and A* algorithms to search the optimal path by stages. Simulations indicate that as the obstacle density in terminal airspace increases from 10% to 40%, GO-APP demonstrates a path length improvement ranging from 0.78% to 24.46% relative to A*. In generating a path from en-route airspace to terminal airspace, VG-APP significantly outperforms A*, reducing path length by up to 15.39% along with improving computational efficiency by 30.54%. Additionally, experiments in the real-world city of Hangzhou validate the effectiveness of the proposed approach.

Despite presenting significant safety concerns, the proliferation of Cyber-Physical Systems (CPS) in industries such as electricity distribution and medicine has spurred innovative applications. The integration of 5G technology, Software-Defined Networking (SDN), and Vehicular Ad Hoc Networks (VANET) within Industrial CPS (ICPS) enables dynamic, real-time interactions but also exposes these systems to vulnerabilities and potential malicious activities. Addressing these risks requires robust security strategies that safeguard both the physical and digital components of CPS. In this context, the Strengthening the Safety of 5G-Enabled CPS using Key Agreement Method (SSKAM) framework is proposed. It incorporates a triple-element user identification technique leveraging user passwords, mobile devices, and unique biometrics. This approach ensures two-way authentication and facilitates the establishment of secure session keys for encrypted communication between registered users, CPS smart devices, and VANETs. The study also explores advanced key agreement methods tailored for 5G-enabled CPS, focusing on enhancing identification protocols while maintaining a balance between computational efficiency, communication overhead, and resilience to emerging threats. By integrating SDN, the framework enforces dynamic security measures at the network level, ensuring real-time adaptability to potential threats. Comprehensive evaluations demonstrate the efficacy of the proposed SSKAM framework in mitigating risks such as replay attacks, impersonation, and man-in-the-middle assaults. The results highlight its viability in safeguarding the integrity and confidentiality of CPS, offering a scalable, efficient, and practical solution to address the evolving security challenges in 5G-enabled CPS integrated with IoT ecosystems and VANETs.

The traditional Dynamic Window Approach (DWA) for local path planning of unmanned aerial vehicles (UAVs) exhibits limitations in flexibility and robustness. Specifically, its fixed evaluation function weights, velocity sampling resolution, and dynamic window ranges fail to adapt to changing environmental conditions, resulting in reduced adaptability of the velocity search space. To address this issue, this study proposes an improved DWA based on the Sparrow Search Algorithm (SSA). First, the proposed algorithm adaptively adjusts dynamic window parameters according to the complexity of the obstacle environment, thereby optimizing the UAV's velocity search space. Second, a velocity sampling resolution strategy is introduced to achieve an effective trade-off between the quality and quantity of predicted trajectories based on the density of dynamic obstacles. Third, by leveraging the strong global search capability and rapid convergence properties of the SSA, the weights of the evaluation function are adaptively optimized to enhance global optimality. Experimental results show that, compared with DWA in a dense multiple dynamic-static obstacles scenario, the proposed algorithm achieves improvements of 8.8%, 66.7%, and 18% in path length, safety distance, and number of iterations, respectively. These enhancements contribute to improved planning efficiency, safety, and overall optimality in UAV operations.