With the rapid development of the Internet of Vehicles (IoV), the tight coupling between In-Vehicle Networks (IVN) and External Vehicle Networks (EVN) has made vehicular systems vulnerable to sophisticated cross-network attack chains. Existing Intrusion Detection Systems (IDS), however, typically operate in isolation on either IVN or EVN, and lack effective context-aware mechanisms for capturing inter-domain dependencies. To overcome this limitation, we propose CATwin-IDS, a context-aware intrusion detection framework that integrates digital twin technology with a lightweight Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) model. In our design, Conditional Mutual Information (CMI) and Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) are applied for feature optimization and data balancing, while Temporal Self-Attention (TSA) enhances the modeling of spatiotemporal dependencies across heterogeneous traffic. The digital twin provides real-time bidirectional synchronization and a simulation environment, enabling proactive adaptation to dynamic threats. Experimental results on benchmark datasets (Car-Hacking, CICIoV2024, CICIDS2018, CICIoT2023) demonstrate that CATwin-IDS achieves higher accuracy and real-time efficiency compared with state-of-the-art methods, providing a holistic solution for securing IoV against cross-network intrusions.

With the rapid advancement of the Internet of Vehicles (IoV), the scale of intelligent connected vehicles has expanded dramatically. Alongside this, the development of the low-altitude economy (LAE) has introduced new types of nodes, such as unmanned aerial vehicles (UAVs) operating in low-altitude airspace. This has led to an exponential increase in the demand for secure and efficient authentication between low-altitude UAVs and ground vehicles. The Chinese Remainder Theorem (CRT), characterized by its efficient computation, low storage requirements, high parallelism, and excellent real-time performance, CRT-based authentication schemes are highly compatible with the resource-constrained and low-latency requirements of IoV scenarios. However, existing CRT-based authentication schemes suffer from scalability bottlenecks. As the number of connected nodes increases, CRT-based authentication schemes exhibit a superlinear growth trend in computational overhead, posing significant challenges to their practical deployment scalability. In response to this challenge, we propose a Dynamic Group Management and Authentication Scheme based on CRT (DGMA-CRT). The proposed scheme employs dynamic grouping of vehicles to enable efficient intra-group domain key updates, avoiding the significant communication overhead caused by global domain key updates. The parameters for domain key updates depend solely on the group size, not the overall system scale, thus significantly enhancing efficiency while ensuring scalability. Furthermore, the scheme incorporates Shamir’s threshold mechanism to mitigate the risks of single-point attacks in key management and leverages elliptic curve cryptography (ECC) to establish a secure foundation for authentication. Simulation results show that DGMA-CRT, by utilizing grouped key updates and cryptographic mechanisms, significantly reduces computational overhead while maintaining robust security. Additionally, the scheme retains high efficiency as the system scales, demonstrating its suitability for large-scale IoV applications.

The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical characteristics of channels, such as K-factor, path loss, delay spread, etc. However, statistic-based channel identification methods cannot accurately differentiate implicit features induced by dynamic scatterers, thus performing very poorly in identifying similar channel scenarios. In this paper, we propose a novel channel scenario identification method, formulating the identification task as a maximum a posteriori (MAP) estimation. Furthermore, the MAP estimation is reformulated by a maximum likelihood estimation (MLE), which is then approximated and solved by the conditional generative diffusion model. Specifically, we leverage a transformer network to capture hidden channel features in multiple latent noise spaces within the reverse process of the conditional generative diffusion model. These detailed features, which directly affect likelihood functions in MLE, enable highly accurate scenario identification. Experimental results show that the proposed method outperforms traditional methods, including convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and random forest-based classifiers, improving the identification accuracy by more than 10%.

Rate splitting multiple access (RSMA) is a powerful multiple access technology that enables communication systems to achieve both reliable and fair data transmission by splitting and encoding user messages into common and private streams. This capability is particularly critical for space-air-ground-sea (SAGS) integrated networks, where heterogeneous nodes (e.g., satellites, UAVs, and underwater sensors) coexist with significant channel quality disparities. The common stream is formed by consolidating the diverse common messages that all users can decode, allowing the system to balance resource allocation and maintain reliable connectivity even for users with weaker channel conditions. Nevertheless, the performance of the common stream is often limited by the user with the weakest channel strength, a prevalent challenge in SAGS integrated networks with mixed near-far field communications and dynamic topology. To address this issue, this study proposes a generalized RSMA strategy to mitigate the rate limitation of common streams in RSMA systems, thereby enhancing system fairness and overall performance. The solution holds potential for crossdomain applications where strong and weak maritime/aerial users share spectrum resources. Furthermore, an algorithm is designed to optimize Max-min fairness (MMF) rate among all users. This is formulated as a non-convex optimization problem, which poses significant challenges for direct solution. To tackle this challenge, we design a low-complexity suboptimal iterative algorithm employing the successive convex approximation (SCA) method. Simulations demonstrate that the proposed generalized RSMA system outperforms traditional one-layer RSMA system and other existing counterparts, particularly in systems with weak users, by effectively enhancing the MMF rate and overall system fairness. This improvement suggests broader applicability for future integrated networks requiring unified management of heterogeneous links.

Vehicle trajectory prediction is a key technology in autonomous driving systems, playing a decisive role in ensuring autonomous driving safety and improving traffic efficiency. In complex traffic environments, the dynamic interactions between vehicles and pedestrians are extremely intricate, posing great challenges for accurate trajectory prediction. Traditional physical models exhibit obvious limitations when facing the nonlinear behaviors of traffic participants, while existing deep learning methods, although improved to some extent, still struggle to meet the practical application requirements in terms of computational efficiency and multimodal prediction capabilities. To address this, this paper proposes a novel vehicle trajectory prediction method based on Group-Rational KAN Enhanced Motion Transformer (GR-KAMT). This method combines the nonlinear modeling capability of the Kolmogorov-Arnold Network (KAN) with the attention mechanism of Transformer, effectively enhancing the trajectory prediction ability for complex scenes. Its uniqueness lies in the introduction of the efficient GR-KAN module, which demonstrates higher efficiency and accuracy in handling complex data relationships compared to traditional Multilayer Perceptrons (MLP), while supporting multimodal trajectory prediction to more comprehensively capture the behavioral patterns of traffic participants. Experiments on the NuScenes dataset demonstrate that GR-KAMT significantly outperforms existing methods in key performance indicators such as minimum average displacement error (minADE) and minimum final displacement error (minFDE), providing reliable support for safe decision-making in autonomous driving.

In space-air-ground-sea networks, the conventional data processing designs separately considering sensing, communication and computation processes lead to severe wastes of radio, energy, and computation resources. To overcome this drawback, an integrated sensing-communication-computation design is pro-posed in this paper, which aims at realizing energy efficient data processing by jointly determining the data offloading ratio together with the sensing and offloading rates according to the processor profiles of mobile devices and servers. It is proved that the data offloading ratio is determined by the server's processor profile, while the string-pulling algorithms are designed to obtain the optimal sensing and offloading rates. Simulations are conducted to verify the effectiveness of the proposed design.

Edge caching is crucial for enhancing the performance of vehicular networks primarily by reducing service latency and improving data availability. However, existing research typically focuses only on unidirectional vehicle movement, which limits its application in complex scenarios, such as those in urban areas. To address this, we propose two novel edge caching strategies specifically tailored for the intricate vehicle movements and traffic signal controls at urban intersections, taking into account temporal variability of content popularity, making them more practical in the real world. The first caching strategy, based on dynamic programming (DP), is suited for scenarios with low traffic flow, providing an optimal solution and serving as a benchmark for evaluating the performance of the second strategy. This benchmark assesses how closely the second strategy approaches the optimal solution. The second strategy employs deep reinforcement learning (DRL) and is suitable for high traffic scenarios. Its performance, when compared with the DP approach in low traffic scenarios, demonstrates results that are near-optimal. Simulation outcomes indicate that the DRL strategy effectively adapts to changes in content popularity, significantly optimizing service latency and hit rates.

In the era of big data, identity authentication in the Internet of Vehicles (IoV) is crucial for secure communications. With the diversification and expansion of IoV services, edge computing has emerged as a viable solution to effectively mitigate the high latency and scalability issues associated with traditional centralized identity authentication mechanisms. Despite the significant role that existing authentication schemes play in ensuring the security of IoV services, they still present relatively time-consuming issues during execution. To resolve this concern, we propose an efficient mutual authentication scheme, which combines the Edwards-curve Digital Signature Algorithm (EdDSA) with the Diffie-Hellman (DH) key exchange protocol, aiming to simplify the complex cryptographic computation process. To further reduce the identity authentication latency at the edge, we designed a rapid re-authentication process. Validated through simulation experiments, the proposed scheme has achieved approximately a 60.9% increase in authentication efficiency compared with the existing SEA scheme, demonstrating its effectiveness in enhancing the security and response.

The seamless integration of big data technology with the Internet of Vehicles (IoVs) has ushered in unprecedented opportunities in the fields of intelligent transportation systems, autonomous driving technology, urban planning, and personalized user services. These applications rely on vast amounts of data for training and optimization. However, traditional methods of data uploading may be subject to communication resource constraints and personal privacy security risks. In this context, Federated Learning (FL), as a distributed learning technology, demonstrates its unique advantages in protecting privacy and reducing communication burdens. Nevertheless, applying FL technology to IoVs faces numerous challenges, such as the complexity of IoV communication environments, data distribution imbalances, and potential malicious user attacks. To address these issues, we designed an IoV aggregation model framework to safeguard the security and robustness of model data, by employing model filtering strategies to counter potential malicious user attacks and utilizing model aggregation algorithms to improve the accuracy of the global model. Experimental results demonstrate that our proposed algorithm performs well in both vertical and horizontal FL, effectively mitigating various backdoor attacks, and significantly improving the accuracy and reliability of the global model.

IoT-based wireless sensor networks (WSNs) face significant energy constraints, which can be alleviated by wireless power transfer (WPT) technology. Integrating WPT with WSNs creates wireless rechargeable sensor networks (WRSNs), where optimizing charging efficiency and scheduling is critical. This paper introduces an ISAC-facilitated optimal on-demand mobile charging scheme for IoT-based WRSNs (IOMSN) with three key components. First, it presents an ISAC-assisted prioritized charging queue, incorporating four attributes with probability distributions: residual energy, traffic load, MCV travel time, and direction angle. Second, it provides ISAC-driven estimations of MCV distance, speed, and location to enhance prioritization, thereby optimizing the charging route and potentially reducing travel costs. Third, a time-allocated partial charging model improves charging efficiency. Numerical results show that the proposed protocol outperforms cutting-edge protocols in energy usage efficiency, travel distance, charging delay, and service time.

In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant variation in channel parameters and inconsistent channel offsets among different wordlines. As a result, the process of estimating channel parameters for each individual wordline typically requires a considerable amount of computational resources, resulting in the high latency of system, which becomes a new challenge. To tackle this problem, two unsupervised learning algorithms are proposed to estimate the channel parameters, based on analyzing the error distribution in 3-D flash memory. To address the longer read latency introduced by the unsupervised learning algorithm for the channel estimation, we further propose a low-latency detection algorithm, which first detects whether the current channel needs to be updated. In the event that an update is required, the algorithm only periodically estimates the channel parameters during system idle times, resulting in a more efficient and streamlined process. Compared to the existing methods, the proposed algorithms can efficiently estimate channel parameters with lower-computational complexity. Moreover, combining with the search algorithm, a correction scheme is proposed to minimize the error between the estimated read reference voltage (RRV) and the optimal RRV in the actual device. Theoretical analysis and simulation results demonstrate that the proposed method can improve the lifetime of flash memory and reduces the number of read retries.

Low-density parity-check (LDPC) codes have been widely adopted to guarantee data reliability in 3-D NAND flash memory. However, the iterative LDPC decoding algorithm leads to high-decoding latency due to the iterative message transfer mechanism. Using a field-programmable gate array (FPGA) testbed, we first present the binary channel in NAND flash and analyze the single-symbol log-likelihood ratio (LLR) variation with the decoding iterations. Subsequently, we investigate the raw bit error ratio (RBER) characteristics of intrapage frames. To reduce the number of iterative decoding, we propose a frame feedback information aware decoding algorithm (FFIA-DA), combined with the single-symbol LLR variation and the similar error characteristics among intrapage frames. The proposed method uses the decoding feedback information of one frame to decrease the number of decoding iterations of other frames with similar RBER. Experiments show that the proposed approach can improve the decoding performance of LDPC and speed up decoding convergence.

Geosynchronous Earth Orbit (GEO) satellites, which can relay image data for Low Earth Orbit (LEO) satellites, play an important role in remote sensing. With the development of satellite technologies, the significantly improved computation capabilities of GEO satellites have enabled space service computing, through which GEO satellites can provide data processing services before forwarding to reduce the quantity of transmitted data. In the presence of multiple LEO satellites, how to make effective use of limited communication and computation resources in GEO satellites has become crucial. At present, the research on satellite resource management typically focuses on either communication or computation resources. Existing resource management algorithms are usually of slow convergence speed, which limits their applicability in real-time remote sensing scenarios. Therefore, we propose an aggregated resource management method for remote sensing applications. We first propose models for transmission tasks and processing tasks of remote sensing images. Then we formulate the aggregated resource management for satellite edge computing as a hybrid Stackelberg game and simplify the problem to speed up its convergence speed. Then we propose a distributed resource management algorithm to determine the optimal strategies. Simulation results show that the proposed method can quickly obtain the optimal resource allocation strategy and outperforms typical dynamic iterative algorithms in terms of service quantity and throughput.

Information exchange between distant nodes is the basis for all functionalities of a wireless ad hoc network. To effectively transmit a packet from one node to other distant nodes, the key is messenger discovery. It is important for a sender to find sufficient but not excessive direct neighboring nodes as messengers. On one hand, the messengers should be distant from the sender but not too far away. On the other hand, the messengers should be far from each other. However, the nature of the wireless ad hoc networks makes this mission a great challenge. In this paper, considering limited energy resource and the nature of ad hoc networks, we review the previous protocols and propose a new protocol, exploiting Erasure Coding for messenger discovery. Our proposed protocol is stateless and works in a distributed manner. In each messenger discovery process, the sender encodes a message into multiple packets with erasure coding and broadcasts the packets until it finds enough messengers activated. The receivers decide whether they should act as messengers or not, based on the percentage of received packets and the number of activated messengers. Mathematical analysis and simulations verifies the viability of our protocol, and shows the superiorities of our protocol.

Wireless Sensor Networks are changing our lives by making all objects around us “smart” and thus creating an Internet-of-Things (IoT). Location information is indispensable for all these things to be really “smart”. GPS and some other hardware based techniques can provide good location information but GPS signal availability, cost, and energy consumption limit their adoption. Location information can also be estimated accurately without hardware support if accurate distance information between nodes is available. Many distance measurement techniques are being investigated. In this paper, a transitional region based distance estimation method for neighboring nodes is proposed. Inside the sender's transitional region, the receiver receives packets reversible to the distance to the sender. Intuitively, the further away, the lower chance to receive. Our mathematical analysis shows that the distance from sender is close to linearly related to receivers' packet reception ratio for most of the transitional region. Our simulation shows that our proposed approach is viable and is superior to RSSI based approach.

Radio frequency identification (RFID) is a technology that incorporates the use of electromagnetic fields to uniquely identity objects. Among different types of RFID tags, passive tags have some salient features such as light weight, low cost, small size, etc. However, the downside of passive RFID systems is very limited reading range due to lacking their own energy sources (passive RFID tags communicate solely by backscattering the reader's power). A novel concept of passive RFID tag-to-tag (T2T) communication has been recently proposed, via which passive tags in proximity (at centimeter level) can directly communicate with each other with the existence of an external energy source. Utilizing this concept, we proposed a Network of Tags (NeTa) that passive tags can connect with each other through multiple hops, using a the novel concept of turbo backscattering operation. This significantly enhances the scalability of such a T2T network. However, to implement the proposed NeTa architecture, one of the main issues is the inter-tag interference, which brings challenge to the routing protocol design. In our previous work [1], we introduced protocol design for both tag-to-reader routing and tag-to-tag routing, considering basic hardware capability of tags, i.e., tags cannot measure the strength of received signals. In this paper, we extend upon the results in [1] and focus on tag-to-tag routing for two distinct types of tags with different hardware capabilities - tags can measure and attenuate the received signal before backscattering. These functions can greatly reduce the inter-tag interference and therefore enhance the network throughput. The protocol design is based on solutions of two mixed integer nonlinear programming (MINLP) problems, respectively. The performances of the proposed protocols are analyzed and the impacts of several network factors (e.g., tag density, the transmit power of the reader, etc.) are investigated.

This paper provides a tractable framework to understand the performance of Device-to-device (D2D) underlay cellular networks. D2D communication can improve range, data rates and energy efficiency of cellular networks. However, analyzing performance of D2D techniques is non-trivial. In this paper, ergodic capacity of D2D underlay cellular networks is investigated using Poisson point process (PPP) models. Line-of-sight (LoS) components in D2D direct links are characterized via a Rician fading model. In addition, locations of D2D receiving users (DRUs) are assumed to be random with varying distribution. Specifically, we investigate two different cases of DRUs: (1) distance between a D2D user (DU) pair follows a uniform distribution and (2) a DRU is distributed uniformly in the circular area around its serving D2D transmit user (DTU). We derive, for the first time, closed-form results for ergodic capacity of D2D communications for a practical case with path loss exponent of 4. We demonstrate that our new analytical results better approximate the actual capacity relative to prior approaches.

Simultaneous information and energy transfer (SIET) is attracting much attention as an effective method to provide green energy supply for mobiles. However, low power level of harvested energy from RF spectrum limits application of this technique. Thanks to improvement of sensitivity and efficiency of RF energy harvesting circuit as well as dense deployment of small cell base stations, SIET becomes more practical. In this paper, we propose a unified receiver model for SIET in LTE-A small cell base station networks, formulate a feasibility problem with Poisson point process model and analyze the feasibility for a special and practical scenario. The results show that it is feasible for mobiles to charge the secondary battery with harvested energy from BSs, but it is still impractical to directly charge the primary battery or operate without any battery at all.