Out-of-context misinformation (OOC) is a low-cost form of misinformation in news reports, which refers to place authentic images into out-of-context or fabricated image-text pairings. This problem has attracted significant attention from researchers in recent years. Current methods focus on assessing image-text consistency or generating explanations. However, these approaches assume that the training and test data are drawn from the same distribution. When encountering novel news domains, models tend to perform poorly due to the lack of prior knowledge. To address this challenge, we propose Variational Domain-Invariant Learning with Test-Time Training (VDT) framework to enhance the domain adaptation capability for OOC misinformation detection. Domain-Invariant Variational Align module is employed to jointly encodes source and target domain data to learn a separable distributional space and domain-invariant features. For preserving semantic integrity, we utilize domain consistency constraint module to reconstruct the source and target domain latent distribution. During testing phase, we adopt the test-time training strategy and confidence-variance filtering module to dynamically updating the VAE encoder and classifier, facilitating the model's adaptation to the target domain distribution. Extensive experiments conducted on the benchmark dataset NewsCLIPpings demonstrate that our method outperforms state-of-the-art baselines under most domain adaptation settings.

The proliferation of smart vehicles and resource-hungry applications has imposed challenges to on-board systems. By exploiting the clustered vehicles in neighbor-following network (NFN), the multi-hop vehicular network (MHVN) enables cooperative communication among multiple vehicles, making it promising for vehicular edge computing (VEC). Nonetheless, to the best of our knowledge, the joint task offloading and resource allocation strategies in MHVN remains open due to challenges arising from the prevalence of link disruptions, the substantial variability in channel states, and the limited computational resource. With the above considerations, in this work, the task offloading and resource allocation are jointly optimized in the MHVN to minimize the offloading cost and maximize the task completion rate (TCR). However, the optimization problem turns out to be a mixed integer nonlinear programming (MINLP) problem. To this end, the problem is decoupled into two subproblems, which are solved by graph theory and improved water-filling algorithm, respectively. Furthermore, an iterative optimization algorithm is designed to mitigate the impact of the relaxation of delay constraints. Simulations are conducted to confirm the effectiveness of the proposed scheme.

The growth of maritime activities has boosted the demand for efficient marine communications and computation offloading. Mobile edge computing (MEC)-powered space-air–ground-sea integrated network (SAGSIN) is a promising solution to satisfy these demands. To the best of our knowledge, the existing research on high altitude platforms (HAPs) in SAGSIN remains open. To exploit the HAPs’ advantages of facilitating communication at shorter distances and more stable computing services than terrestrial base stations, this work considers multi-HAP-assisted computation offloading in SAGSIN and formulates an optimization problem, which turns out to be a mixed integer nonlinear programming (MINLP) due to the joint optimization among task-HAP association, computational resource allocation of HAPs and task-satellite association. To this end, the problem is decoupled into three single-variable subproblems by relaxing the delay constraint, and the subproblems are solved by graph theory, the Lagrange multiplier method with Karush-Kuhn–Tucker (KKT) constraints, and the alternating optimization, respectively. Simulation results demonstrate the efficiency of the proposed scheme with superior performances compared to benchmark schemes.

Mobile edge caching (MEC) has grown substantially with the rapid development in scale and complexity of data traffic. By exploiting the expansive coverage of autonomous aerial vehicles (AAVs), MEC enables services for massive vehicle users (VUs) simultaneously, which is promising for enhancing network transmission efficiency. Nonetheless, due to challenges arising from the timeliness and freshness of content services caused by AAVs’ limited endurance and airborne capacity, caching strategy considering the real-time of content in large-scale dynamic Internet of Vehicles (IoV) environments remains open. With the above consideration, in this article, the cache refreshing cycle and content placement are jointly optimized in the cache-enabled AAV-assisted vehicular integrated networks (CAVINs) to minimize the content Age of Information (AoI) and energy consumption of the macro AAV. Since the joint optimization problem is variational coupled with nonconvex binary constraints, it is decoupled and solved by a double-iteration method. Specifically, the optimal cache refreshing cycle is derived in semi-closed form with the Karush-Kuhn-Tucker (KKT) conditions. The locally optimal solution of the content placement is obtained through successive convex approximation (SCA). Simulation results corroborate the effectiveness and superiority of the proposed scheme.

Rate-splitting multiple access (RSMA), space division multiple access (SDMA), and non-orthogonal multiple access (NOMA) have gained significant popularity and are extensively utilized across various domains. However, it is still unclear whether hybridRSMA-SDMA-NOMA (HybridRDN) would seamlessly combine the advantages of RSMA, SDMA, and NOMA to contribute to the computation offloading of autonomous vehicle systems. To address the above issue, this paper introduces a novel HybridRDN-assisted computation offloading fleet (COF) scheme tailored for autonomous vehicle systems. First, we propose a stochastic-geometry-aided method to model the offloading framework. Afterwards, the task vehicles (TVs) ingeniously employ the proposed HybridRDN scheme to offload tasks to the resource vehicles (RVs) in each COF to relieve their computational burden. Diverging from the sole optimization of the task segmentation ratio or the transmission rate, a joint optimization problem involving the transmission weighting factor, the HybridRDN precoding matrix, the common rate, and the task segmentation ratio, is formulated, which aims to minimize the average delay of the COF system while approaching the rate performance of the ideal HybridRDN. Furthermore, a delay-optimal alternating optimization algorithm (DOAOA) is developed to obtain the solution for the optimization problem. Experimental results validate the plausibility and superiority of the proposed framework compared to the state-of-the-art schemes.

Decentralized and open features of federated learning provides opportunities for malicious participants to inject stealthy trojan functionality into deep learning models collusively. A successful trojan attack is desired to be effective, precise and imperceptible, which generally requires priori knowledge such as aggregation rules, tight cooperation between attackers, e.g. sharing data distributions, and the use of inconspicuous triggers. However, in realistic, attackers are typically lack of the knowledge and hardly to fully cooperate (for privacy and efficiency reasons), and out of scope triggers are easy to be detected by scanners. We propose FedTrojan, a zero-knowledge federated trojan attack. Each attacker independently trains a quasi-trojaned local model with a self-select trigger. The model behaves normally on both regular and trojaned inputs. When local models are aggregated on the server side, the corresponding quasi-trojans will be assembled into a complete trojan which can be activated by the global trigger. We choose existing benign features rather than artificial patches as hidden local triggers to guarantee imperceptibility, and introduce catalytic features to eliminate the impact of local trojan triggers on behaviors of local/global models. Extensive experiments show that the performance of FedTrojan is significantly better than that of existing trojan attacks under both the classic FedAvg and Byzantine-robust aggregation rules.

In this paper, we develop a non-coherent differential chaos shift keying based index modulation by using initial value index (IVI-DCSK) to convey additional information for wireless communications. In the proposed scheme,mcmapped bits are carried by 2mcchaotic sequences by exploiting the quasi-orthogonality of different chaotic signals, while the modulated bit is carried by DCSK. To diminish the multiuser interference, the references allocated to different users are sent in individual time slots, while the information-bearing sequences for the mapped bits of users are sent simultaneously. We then derive the bit error rate (BER) expression of multi-user IVI-DCSK over multipath Rayleigh fading channels. The theoretical and consistent simulation results show that the proposed IVI-DCSK achieves significant gains over the conventional chaotic-based index modulations, i.e., permutation index DCSK (PI-DCSK) and code index modulation DCSK (CIM-DCSK). This gain can be more than 4 dB in fading channels with high multipath delay. In addition, it achieves higher energy and spectral efficiencies over the latter ones. The superiority of the proposed scheme is further verified in practical ultra-wideband (UWB) communications. Thus, this proposed scheme is efficient and promising for chaotic-based low-complexity communications, such as in wireless local area network (WLAN) and indoor applications.

Vehicular fog computing (VFC) that supports inter-vehicular task offloading emerges as a promising complement to handle the explosive growth of computation-intensive tasks in Intelligent Transportation Systems (ITS). Nonetheless, as the fog access points (F-APs) in crowed areas are often overloaded, the conventional single F-AP VFC may become incompetent and energy-inefficient. To tackle the issue, a novel scheme of non-orthogonal multiple access (NOMA)-enabled multi-F-AP VFC with partial offloading is proposed in this work. However, the corresponding energy minimization turns out to be a highly non-trivial non-linear mixed-integer programming problem. To this end, the optimal power allocation is derived by exploiting monotonicity while good task splitting ratio and user association are found through successive convex approximation (SCA)-based interior-point method and game theoretic approach, respectively. Extensive simulations based on MATLAB show that, in the considered scenarios, the proposed scheme can fulfill a more balanced offloading and better exploit the available computing resources, thereby leading to an approximately 30% energy consumption reduction compared to the baselines.

With the advent of the Internet of vehicles (IoV), the explosive growth of data from vehicular sensors places a heavy computing burden on green-enabled intelligent transportation systems. In this study, a paradigm of vehicular fog computing (VFC) is introduced, which is able to offload computation tasks to the nearby fog nodes. In addition, considering the advantage of non-orthogonal multiple access (NOMA), a NOMA-enabled VFC is proposed, in which a task vehicle, a main fog access point (F-AP), an idle vehicle, and other cooperative F-APs are involved to process the task. By jointly optimizing NOMA power allocation, task allocation ratio, bandwidth allocation and time slot allocation, the offloading data maximization problem is investigated. After simplifying the problem through mathematical analysis, the offloading data maximization problem is solved by the proposed interior-point method based on successive convex approximation (SCA). Simulation results show that the proposed offloading scheme performs better than other existing schemes in terms of offloading data.

Device-to-device (D2D) communication is one of the most promising technologies for relieving the pressure of demands in the 5G mobile networks. However, due to randomness of user request, limitation of storage space and transmission capacity, it is still a challenge for channel allocation to optimize the successful delivery ratio of contents. Thus, in this article, we first consider the link selection problem for mobile users in multi-D2D (MD2D)-based content delivery networks. We establish a content delivery utility that combines physical and social aspects, taking into account energy consumption, user mobility, and trust relationships. Second, we model the content delivery link selection as the maximum weighted matching problem. For this NP-hard problem, by relaxing the integer constraints, we propose a BnB-CDLS algorithm based on the branch-and-bound method for the proposed content delivery scheme. Furthermore, we prove that the problem can be computationally reduced to a monotone submodular problem subject to matroid and knapsack constraints, which can be solved by a greedy GA-CDLS algorithm. Numerical results show that as compared with the existing schemes, the proposed scheme can significantly improve the successful delivery ratio of contents.

As one of the most promising solutions, edge caching is emerging to reduce the content retrieval latency and relieve the huge burden on the backhaul links in the 6th generation (6G) millimeter wave (mmWave) intelligent vehicular networks. In this paper, we first formulate the optimization problem in terms of request hit probability and investigate two crucial factors, which impact the effectiveness of cache placement in device-to-device (D2D) enabled mmWave vehicular networks. Then, the general mathematical expressions for optimization of the request hit probability with the constraints of caching capacity are derived. Moreover, a group caching scheme (GCS) is proposed to obtain the sub-optimal results by utilizing relaxation method and Karush-Kuhn-Tucker (KKT) conditions. Inspired from the Matern hard-core processes and by exploiting the spatially correlated characteristics of users distribution, hard-cored based caching algorithm (HCCA) is proposed to avoid the simultaneous caching for a particular file. In addition, a joint caching and scheduling strategy is investigated which maximizes the number of concurrent transmissions by D2D enabled vehicle pairs matching and antennas adjustment. Comprehensive simulations validate our theoretical analysis and demonstrate that the proposed scheme can achieve higher performance in terms of request hit probability and the number of concurrent transmissions compared to the existing methods. Consequently, the proposed caching scheme has great potential in future intelligent vehicular applications.

Federated learning is inherently vulnerable to poisoning attacks, since no training samples will be released to and checked by trustworthy authority. Poisoning attacks are widely investigated in centralized learning paradigm, however distributed poisoning attacks, in which more than one attacker colludes with each other, and injects malicious training samples into local models of their own, may result in a greater catastrophe in federated learning intuitively. In this paper, through real implementation of a federated learning system and distributed poisoning attacks, we obtain several observations about the relations between the number of poisoned training samples, attackers, and attack success rate. Moreover, we propose a scheme, Sniper, to eliminate poisoned local models from malicious participants during training. Sniper identifies benign local models by solving a maximum clique problem, and suspected (poisoned) local models will be ignored during global model updating. Experimental results demonstrate the efficacy of Sniper. The attack success rates are reduced to around 2% even a third of participants are attackers.

Device-to-device (D2D) communication, which is defined as a direct communication between two mobile users without traversing the Base Station (BS) or the core network to offload the increasing traffic to the user equipments, is one of the key technologies in the fifth generation (5G) of wireless communication systems. Discovery and communication are the basic two features to fulfill the need for the D2D communication. However, Most of existing D2D studies focused on the communication issues always assume that the discovery is completed. In this paper, we propose two strategies of device discovery and access scheme for the 5G cellular networks. Then the performance analysis based on two dimensional discrete time Markov process model is provided. In addition, we present numerical simulation on the Matlab platform. The simulation results demonstrate the viability of the proposed scheme.

Abstract A large number of new data‐consuming applications are emerging, and many of them involve mobile users. In the next generation of wireless communication systems, device‐to‐device (D2D) communication is introduced as a new paradigm to offload the increasing traffic to the user equipment. Before the traffic transmission, D2D discovery and access procedure is the first important step which needs to be completed. In this paper, our goal is to design a device discovery and access scheme for the fifth generation cellular networks. We first present two types of device discovery and access procedures. Then we provide performance analysis based on the Markov process model. In addition, we present numerical simulation on the Vienna Matlab platform. The simulation results demonstrate the viability of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.