Vehicular ad-hoc networks (VANETs) have become the predominant technology in the current era. Although VANETs have numerous benefits, they are prone to different types of attacks owing to their open nature. Therefore, security plays a crucial role in VANET systems. Ensuring a safe and dependable vehicular communication system is crucial when performing anonymous authentication and group key agreement. Many related works have been proposed based on signature aggregation and group key management; however, they suffer from high computational and communication costs. Hence, in this work, signature aggregation scheme is proposed in such a way that the computational overhead is significantly reduced. Moreover, an ECC-based group management scheme is proposed to secure group communication. In comparison to recent works, the proposed work generates and verifies signatures with an efficiency of 50.03% and 26.26%, respectively. Furthermore, 72.32% and 35.45% efficient in terms of transmission overhead and serving ratio when compared to recent works. To validate this work practically, printed antenna consisting of four elements arranged in a linear array is developed for the intended use. Security analysis is performed in formal and informal ways to prove the security strength of the proposed method. Finally, the performance is validated with similar works using the Cygwin platform with the PBC library.

As an indispensable component of the 6G-enabled intelligent transportation systems, the satellite-ground integrated vehicular networks (SGIVN) have attracted widespread attention in recent years for its ability to provide continuous and ubiquitous connectivity services. However, in view of a huge number of access requirements from vehicle terminals and the restricted contention resources, the conventional random access (RA) schemes will suffer from severe overload issues when applied to the emerging SGIVN. To address this challenge, we propose a novel deep learning (DL) assisted collision detection and load estimation scheme to efficiently support massive access in the SGIVN. Specifically, a reliable RA preamble based on cyclically shifted Zadoff-Chu sequences is first designed as the precondition of collision detection, which can achieve an optimal performance trade-off between interference mitigation and user identification. By making full use of the intrinsic properties of preamble correlation results and the relevance analysis capability of attention mechanism, we further present a correlation feature extraction based deep RA collision detection framework embedded with a lightweight transformer network, thereby enabling the global dependencies of the few and important features associated with collided loads to be thoroughly acquired from the local correlation results with low overhead. Extensive simulation results validate the feasibility of our scheme in high-dynamic non-terrestrial network scenarios involving large-scale RA collisions, and demonstrate that it can obtain remarkably enhanced detection performance with short computational time, in comparison with state-of-the-art DL-based schemes.

Mobile Edge Computing (MEC) effectively alleviates the pressure on limited in-vehicle computing resources and energy supply caused by computation-intensive vehicular applications. However, the uneven spatial distribution of users leads to load imbalance among adjacent MEC servers, significantly increase the latency and energy consumption costs for vehicles. Therefore, achieving optimal configuration of available computing resources in MEC servers to accomplish the goal of low-latency and low-energy task offloading has become a critical issue to address. To tackle this problem, this study proposes a Multi-RSU Load Balancing (MRLB) strategy based on multi-hop network technology. This strategy dynamically allocates computing tasks to neighboring RSU server clusters with available computing resources through task segmentation and computation offloading mechanisms. Meanwhile, adaptive resource allocation strategies are implemented based on task quantity and task scale characteristics. Specifically, this study designs a multi-RSU collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) to solve the optimal offloading decision. Additionally, by integrating the Lagrange multiplier method and Sequential Quadratic Programming (SQP) algorithm, the joint optimization of imbalanced task segmentation decisions and optimal CPU frequency allocation decisions for RSU servers is achieved. Experimental results demonstrate that the proposed method can achieve efficient multi-RSU resource allocation and ensure coordinated optimization of both system latency and energy consumption costs across diverse device conditions and varying network scenarios, particularly in load-imbalanced situations.

In the era of large-scale machine learning, largescale clusters are extensively used for data processing jobs. However, the state-of-the-art heuristic-based and Deep Rein-forcement Learning (DRL) based job scheduling mechanisms are facing challenges such as slow training speed and underexploitation of jobs' complex dependencies. We propose MPDA, a Massively Parallel learning and Dependency-Aware scheduling algorithm, consisting of a fast-training mechanism and a novel dependency-aware policy network, GATNetwork, to address these two challenges respectively. The fast-training mechanism is a two-level massively parallel training method that can significantly accelerate the training process and maximally utilize the resources of the cluster. Additionally, its decoupled learning and interacting design enables hybrid-workload training for MPDA, which guarantees the generalization and robustness of MPDA. The GATNetwork exploits the dependencies among stages/jobs using Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) networks to improve the performance of the scheduling policy. The experiments show that MPDA accelerates the training speed by one to two orders of magnitude and achieves better scheduling performance, i.e., lower average job completion time, compared with existing scheduling algorithms.

With the rapid evolution of wireless communication technology, smart homes have significantly improved the quality of peoples daily lives by taking advantage of the low latency and high transmission rates of 6G communication technology. Users can now conveniently manage the consumer electronics remotely. However, in the pratical smart home scenarios, the users and consumer electronics communicate with each other through an open public channels where charted and uncharted security risks and privacy vulnerabilities exist. To protect users confidential data from malicious interception and modification, diverse authentication protocols have been proposed so far. However, existing protocols often suffer from efficiency issues or vulnerabilities to known attacks. To address these challenges, this paper proposes a novel three-factor ECC-based anonymous authentication protocol. The protocols security properties can be rigorously proven using the formal analysis under the ROR model. Subsequently, the resistance to numerous types of attacks, including man-in-the-middle and replay attacks, can be demonstrated through informal analysis and the AVISPA verification process. Finally, the protocol is compared with the state-of-the-arts and the results show that the protocol strikes a good balance between security and efficiency and is well suited for smart home environments.

As a typical privacy-aware machine learning paradigm, federated learning (FL) provides facilities to individually train edge clients with their private data and aggregate the central global model. In this way, privacy leakage can be prevented. Massive communication overhead caused by exchanging updated weights between clients and the server is one of the main obstacles in this strategy. Prior work advocates compressing the weights by employing quantization, gradient sparsification, and knowledge distillation approaches. However, most of them cannot be readily applied to secure aggregation in privacy-aware FL. Some research has made great progress in directly utilizing benchmark secure aggregation protocols on top of the non-privacy-aware FL. Graph-based and gradient-based sparsification has been widely adopted in previous studies. However, the results of reducing communication costs are still unsatisfactory. In this paper, we present a novel communication-efficient privacy-ware FL algorithm from a distinct perspective. We design a new Two-Phase Sparsification with Secure Aggregation (TPSSA) algorithm. In the subnetwork phase, we identify sparse subnetworks by freezing the initial random weights in sufficiently overparametrized networks. All edge clients collaboratively train to discover their subnetwork inside a dense randomly weighted neural network. Then the server aggregates to compute the global model. In the gradient phase, for each pair of edge clients, we introduce pairwise multiplicative random masks to identify the sparsification pattern. Then updates from surviving clients can be correctly cancelled out during the aggregation process in the server. Theoretical analysis reveals convergence, privacy and performance guarantee. We show improvements in accuracy, communication, and computation over traditional and sparsified secure aggregation benchmarks on two real-world datasets.

Abstract Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.