Detecting blended Interest Flooding Attacks (IFA) and Collusive IFA (CIFA) in Named Data Networking (NDN) remains challenging due to the feature masking effect, where persistent IFA loads and transient CIFA pulses non-linearly overlap. Existing methods struggle to disentangle these superimposed signatures due to computational bottlenecks or limited temporal resolution. In this paper, we propose MST-Mamba, a unified detection framework built upon the Selective State Space Model (SSM). Our core innovations include a Multiscale Temporal Awareness (MTA) module to capture heterogeneous attack dynamics and a self-supervised pre-training strategy to eliminate topological interference. Experimental results demonstrate that MST-Mamba achieves over 99% accuracy with O(L) linear inference complexity. Even in complex blended scenarios, the model exhibits exceptional cross-topology generalization and low latency, providing a robust technical paradigm for securing NDN infrastructures.

Owing to the harsh underwater environment and limited energy replenishment, extending network lifetimes and achieving energy efficiency are critical challenges in underwater wireless sensor networks (UWSNs). In this paper, a novel multiobjective Arctic puffin optimization (MOAPO) method that is specifically tailored for clustering and routing in UWSNs is proposed. Within the proposed MOAPO framework, K-means++ clustering is first used to optimize the initial cluster head (CH) positions, thereby improving clustering performance and ensuring more effective coverage and resource utilization. A feedback-based mechanism is used to adaptively adjust the behavior conversion factor to balance global exploration and local exploitation, thereby avoiding premature convergence and improving the quality of the selected CHs. A fitness function that incorporates the node energy, communication distance, and CH selection frequency is constructed, with the weights dynamically tuned according to the current energy state of the network, which results in energy-efficient and energy-balanced CH selection. Based on the selected CHs, a multiobjective routing strategy in which the energy levels, delays, and packet loss rate are considered is applied to construct reliable data transmission paths. The simulation results confirm that compared with existing methods, the MOAPO method achieves lower energy consumption and a longer network lifetime, thus demonstrating superior robustness and adaptability.

To enhance the performance of stratospheric airships in Earth observation, the paper investigates the distributed cooperative tracking control problem for a heterogeneous stratospheric airship system under limited communication and computing resources. Initially, a distributed adaptive event-triggered consensus control is proposed to handle the limited communication and computing resources of the stratospheric airship system. Furthermore, to enhance the adaptability of the controller, the time-varying adaptive coupling weight is designed for each part of consensus error composition in both the controller and triggering function. Then, to improve the scalability and robustness of stratospheric airship clusters, a distributed event-triggered control is constructed that only uses state estimation and tracking errors of the neighbor airships. Additionally, the proposed distributed event-triggered control can realize the leader-following consensus for the cooperative tracking control of stratosphere airships, ensuring that each airship avoids the Zeno behavior. The necessary conditions and solid mathematical proof have been given to ensure the stability of the cooperative tracking stratosphere airship system. Finally, numerical simulations are given to illustrate the effectiveness of the proposed event-triggered cooperative tracking control of stratosphere airship for earth-observing.

Vehicular named data networking (VNDN), which integrates the principles of named data networks with vehicular ad hoc networks, represents a promising paradigm for future intelligent transportation systems. Nevertheless, VNDN faces significant hurdles, including broadcast storms from excessive interest packet flooding and reverse-path disruptions due to high vehicular mobility. To address these challenges, we introduce an adaptive forwarding with path optimization method. First, a dynamic caching algorithm is designed to optimize roadside unit storage efficiency and maximize cache hit rates. Second, a gated recurrent unit-based adaptive data forwarding mechanism is introduced to dynamically select optimal forwarders and preserve reverse paths via decentralized heartbeat detection and interface remapping, improving link reliability. Simulation outcomes demonstrate that the proposed approach significantly lowers data retrieval delays while curbing overall communication overhead.

3D Range-Gated Imaging technology is widely used for detection in complex environments (such as autonomous driving scenarios) due to its excellent anti-interference capabilities. However, its application faces the dual challenges of a lack of specialized datasets and the limited performance of traditional RGB models in low signal-to-noise ratio environments, which hinders the transfer and generalization of deep learning methods. To address these difficulties, this paper proposes a 3D imaging method based on multimodal feature fusion. Specifically, the model adopts a dual Vision Transformer (ViT) encoder, single-decoder architecture. On one hand, it performs pre-trained ViT encoding on geometrically re-projected RGB images. On the other hand, it applies an isomorphic ViT encoding to the range-gated images. Through layer-wise semantic recombination, it achieves efficient cross-modal feature fusion, not only does it enhance the robustness and accuracy of depth estimation, but it can also be easily deployed on edge devices. To overcome the problem of overfitting to LiDAR ground truth data, a spatially constrained window cropping data augmentation strategy is designed, significantly increasing the diversity of training samples and the model's generalization ability. To address the input resolution limitations of Transformers, an optimization scheme combining dynamic patch-based training and progressive up-sampling is further proposed, balancing high-resolution feature representation with efficient training. Experimental results show that the proposed method reduces the depth estimation RMSE on a public test set by more than 12% compared to mainstream baseline models, with particularly outstanding performance in low-texture and long-distance scenes. This research provides a systematic technical solution for cross-modal 3D perception and offers theoretical and engineering references for designing 3D imaging models for complex environments.

Microservice architecture provides innovative solutions for delay-sensitive applications and is widely used in Mobile Edge Computing (MEC). Deploying microservices and implementing request routing within large-scale networks are confronted with numerous challenges, primarily due to intricate dependencies between microservices, frequent data communications between microservices, and the dynamic fluctuations of user request traffic. Given that the user requests are time-varying, the orchestration scheme must enable automatic scaling of microservice instances to ensure service quality. Yet, existing research mainly focuses on static microservice instance deployment, inadequately achieving the intelligent autoscaling of microservices and addressing the time-varying nature of user requests. To address these challenges in dynamic MEC networks, this paper introduces a joint optimization strategy for microservice autoscaling and routing, which adapts to the dynamic fluctuations of user request traffic. Initially, we utilize open Jackson queuing network theory to construct the model, analyzing service request queuing, communication, and processing delays along routing paths, and formulate the problem aiming to minimize deployment costs while ensuring service processing delays are not beyond the delay range that the users can accept. To address the problem, we propose a multi-stage, fine-grained dynamic scaling and routing algorithm. Extensive simulation results indicate that our approach substantially reduces network costs while maintaining network delays within a reasonable range, compared to the other state-of-the-art methods.

Ambient backscatter communication (ABC) enables low-cost and energy-efficient connectivity for Internet of Things (IoT) devices by leveraging ambient radio-frequency (RF) signals. However, the passive nature and open wireless medium of ABC systems make them vulnerable to detection by unauthorized receivers (wardens). To mitigate this risk, covert communication, which conceals transmissions by embedding them within noise, offers a promising security enhancement for ABC systems. This paper proposes a jammer-assisted reflection coefficient optimization framework to enhance the covertness and reliability of ABC systems with an endogenous warden and an external jammer. Specifically, we consider two distinct jamming patterns: uniformly distributed and truncated exponentially distributed artificial noise power. We derive closed-form expressions for both the outage probability of the backscatter link and the minimum detection error rate at the warden under these jamming patterns. Based on these expressions, we determine the optimal reflection coefficients that maximize the effective covert rate while satisfying a predefined covertness constraint. Additionally, we introduce the concept of jamming cost to evaluate the efficiency and applicability of different jamming patterns in terms of the required jamming power to achieve a desired level of covertness. Numerical results validate the effectiveness of the proposed optimization framework and reveal that while uniform jamming provides stronger covertness and lower jamming cost, truncated exponential jamming achieves a lower outage probability. These findings provide key insights for designing secure and efficient ABC systems across diverse IoT deployment scenarios.

Microservice architecture, as an emerging network architecture, has gained widespread adoption in latency-sensitive applications within the realm of mobile edge computing (MEC). In MEC networks, these latency-sensitive applications necessitate the concurrent processing of numerous service requests, which are composed of microservices. The complex dependencies between microservices and frequent data communication between servers contribute to the intricacy of deploying and routing microservice instances within the network. Moreover, the dynamic and unpredictable nature of service request traffic significantly complicates the timeliness and efficiency of service deployment and request routing strategies. However, existing research predominantly focuses on static network environments and neglects the time-varying characteristics of service request traffic in realistic scenarios. Consequently, we address the joint optimization problem of service deployment and request routing in the presence of dynamic service request traffic. To model the inherent data dependencies and analyze service request response latency, we employ the open Jackson queuing network. We propose a fine-grained microservice dynamic scaling (FMDS) algorithm to capture the dynamic fluctuations in service request traffic within the network. This algorithm scales microservice instances based on the principle of equal proportional change, obtaining a service deployment scheme that minimizes costs while satisfying latency constraints. Furthermore, we introduce a recursive path search algorithm that explores the service deployment scheme to determine the node forwarding probability for the entire network, adhering to the principles of fair routing. Simulation results show that the proposed method effectively improves network latency stability by 75% and enhances the timeliness of the service deployment strategy.

Mobile-edge computing (MEC), pushing the centralized cloud computing, storage, and communication capability to the edge close to vehicular terminals, is proposed as a promising solution to support computation-intensive and delay-sensitive services. This article proposes a cooperative MEC-enabled task offloading framework where the computational task of each vehicle is divided and computed by multiple collaborative MECs located on the roadside. However, existing MEC-enabled offloading research is based on offline settings or static networks and fails to address the dynamic communication environments. These dynamic environments involve variations in temporality (real-time channel state) and spatiality (uncertain data-queue backlogs as vehicles pass through different coverage areas of MECs). In the dynamic vehicular networks, the degradation of utility energy efficiency (UEE) and time delay is inevitable and significantly impacted. To tackle this issue, we propose an online dynamic scheme to solve the problem of maximizing UEE while meeting time-delay constraints. We then introduce a novel online dynamic optimization algorithm based on Lyapunov optimization theory to adaptively create strategies for task offloading and communication resource allocation in parallel. Numerical simulations demonstrate that the proposed algorithm achieves a balance between UEE and delay, striking a flexible tradeoff by tuning the control parameter$V$. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in terms of real-time communication and transmission capability.

The integration of Ambient Backscatter Communication (ABC) with covert communication is expected to support emerging Internet of Things (IoT) applications (e.g., Radio Frequency (RF)-powered networks) due to the need for low-cost connectivity and confidential transmission. In general, the purpose of covert communication is to hide the existence of the RF-powered wireless link to ensure the information security of the ABC link. However, the ABC link may have a high rate requirement, thus inevitably increasing the risk of information leakage. Hence, this paper considers jammer-aided endogenous covert communication, where an RF tag sends information covertly to an ABC receiver and exploits the jammer's Artificial Noise (AN) under the supervision of a warden-like legacy receiver. To obtain the maximum data rate of the backscatter link without being detected, we derive the minimum detection error rate of the warden and the outage probability of the backscatter link under random channel fading and the jammer's AN, respectively. Then, we optimize the tag's reflection coefficient to maximize its effective covert rate under the covert constraint based on the warden's mean detection error rate. Since the optimal reflection coefficient cannot be solved directly, monotonicity analyses of the objective and the constraint with respect to the reflection coefficient are adopted to achieve an efficient solution. Numerical results demonstrate the effectiveness of the optimized reflection coefficient for the jammer-aided system.

Unmanned aerial vehicles (UAVs) have attracted a lot of research attention in serving as aerial base stations (BSs). To protect the data privacy without being detected by a warden, we investigate a jammer-aided UAV covert communication system, aiming to maximize the user's covert rate with optimized transmit and jamming power. By considering the general composite fading and shadowing channel models, we derive the closed-form expressions for detection error probability and covert rate. The covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced. To solve the NBS, we propose a particle swarm optimization-based power allocation algorithm. The numerical results are presented to verify the theoretical analysis.

This article investigates the robust secure transmission problem in two-tier B5G heterogeneous networks with multiple noncollusive eavesdroppers and users, where two types of imperfect channel state information (CSI) scenarios, i.e., instantaneous and statistic CSI scenarios, are considered. Given the two-sidedness of co-channel interference in physical-layer security and the selfishness of femtocell base stations (FBSs), an imperfect-CSI-based noncooperative game framework is proposed to maximize the profits of the macro base station (MBS) and FBSs, while guaranteeing user’s Quality-of-Service (QoS) requirement in terms of outage probability. Specifically, based on the involved two CSI scenarios, the original game where the MBS and FBSs act as players is elaborated as two robust game problems. To address channel uncertainties in the objective function, the worst-case and the mean value of the channel gains are used separately. Besides, the remaining channel uncertainties embodied in the intractable outage probability constraints are treated in a unified way, i.e., the extended Bernstein approximation. The existence and uniqueness of the Nash equilibrium (NE) are analyzed, and the sufficient condition on the uniqueness of the NE is derived. Then, two robust iterative algorithms are given to approach the robust game equilibrium. Finally, numerical results are presented to verify the theoretical analysis and show the robustness and effectiveness of the proposed algorithms.

Unmanned aerial vehicles (UAVs) have attracted a lot of research attention because of their high mobility and low cost in serving as temporary aerial base stations (BSs) and providing high data rates for next-generation communication networks. To protect user privacy while avoiding detection by a warden, we investigate a jammer-aided UAV covert communication system, which aims to maximize the user’s covert rate with optimized transmit and jamming power. The UAV is equipped with multi-antennas to serve multi-users simultaneously and enhance the Quality of Service. By considering the general composite fading and shadowing channel models, we derive the exact probability density (PDF) and cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR). The obtained PDF and CDF are used to derive the closed-form expressions for detection error probability and covert rate. Furthermore, the covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced to investigate the negotiation among users. To solve the NBS, we propose two algorithms, i.e., particle swarm optimization-based and joint two-stage power allocation algorithms, to achieve covertness and high data rates under the warden’s optimal detection threshold. All formulated problems are proven to be convex, and the complexity is analyzed. The numerical results are presented to verify the theoretical performance analysis and show the effectiveness and success of achieving the covert communication of our algorithms.