Search Results (24)

Reliable coordination of unmanned aerial vehicle (UAV) swarms is significantly challenged by random denial-of-service (R-DoS) attacks, which introduce stochastic packet loss, time-varying communication interruptions, and strong concealment. Existing fault-tolerant consensus approaches typically assume known attack information or treat attack detection, topology recovery, and control design as separate processes, resulting in limited resilience under dynamically evolving attack conditions. To address this issue, this paper proposes a detection-guided resilient consensus control framework for UAV swarms under R-DoS attacks. A dual-dimensional statistical detection method is developed by jointly modeling packet reception rate (PRR) and inter-arrival time (IAT), enabling real-time identification of attack-induced anomalies through spatio-temporal feature fusion. Based on the detection results, a distributed topology reconstruction strategy is designed, incorporating redundant node identification and cluster-based dynamic communication reconfiguration. The communication graph is adaptively updated via online adjustment of adjacency and Laplacian matrices, and robustness guarantees for the resulting consensus process are analytically established. Hardware-in-the-loop simulation experiments under both single-leader and multi-leader architectures demonstrate that the proposed method can accurately detect attacked nodes, effectively reconstruct the communication topology, and maintain stable formation coordination under severe R-DoS attacks. The position tracking error is constrained within 0.4 m, validating the effectiveness and robustness of the proposed framework. This study is limited to defensive cyber-resilience in a closed hardware-in-the-loop simulation environment and does not address reconnaissance payloads, weaponization, target selection, or operational attack execution. Unlike methods that assume known attack schedules or treat detection, topology recovery, and control separately, this study focuses on their online coupling under unknown random packet loss; its validation is limited to the stated closed HIL impairment model. Full article

Multi-agent reinforcement learning has emerged as a promising paradigm for cooperative unmanned aerial vehicle (UAV) swarm coordination. However, existing communication-aware MARL methods primarily focus on communication topology, message routing, and message aggregation, while the information content of the exchanged messages is often only implicitly controlled. In realistic UAV networks, inter-agent communication is constrained by limited bandwidth, communication range, energy consumption, and packet loss. It is therefore desirable for each UAV to transmit compact and task-relevant information rather than dense and redundant latent features. In this paper, we propose IB-CEMARL, an information-bottleneck-guided, communication-efficient multi-agent reinforcement learning framework for UAV swarms. We formulate inter-UAV communication as a minimal sufficient message-learning problem in which each UAV encodes its local observation into a stochastic bottleneck message before exchanging information with its neighbors. Cauchy–Schwarz divergence-based quadratic mutual information is adopted as a unified dependence measure to jointly regularize message compression, preserve decision-relevant information, and reduce statistical redundancy among neighboring UAV messages. Extensive experiments demonstrate that IB-CEMARL achieves superior cooperative performance, reduced message redundancy, and stronger robustness compared with representative communication-aware MARL baselines. In particular, IB-CEMARL improves the average return by $4.9\%$ and reduces inter-message dependence by $29.0\%$ compared with the KL-IB-MARL baseline while maintaining efficient communication under constrained bandwidth settings. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

This paper proposes a barrier-modulated probabilistic super-twisting guidance (BMPSTG) for robust three-dimensional maneuvering target interception under measurement uncertainty and packet loss. A stochastic barrier-adaptive mechanism inspired by tunneling dynamics is introduced to reshape the sliding variable evolution, enabling accelerated convergence and reduced chattering compared with conventional super-twisting schemes. To enhance disturbance reconstruction accuracy, a composite observer integrating a finite-time disturbance observer (FTDO) and a high-order extended state observer (HESO) is developed. The FTDO ensures fast transient estimation, while the HESO improves steady-state precision and robustness to noise. Simulation results demonstrate that the proposed FTDO–HESO structure outperforms both standalone FTDO and conventional FTDO–ESO configurations in terms of estimation accuracy and guidance performance. A probability-coupled gain adaptation strategy further adjusts control gains according to engagement states, improving robustness under aggressive target maneuvers. Monte Carlo simulations verify that the proposed method achieves high interception accuracy with stable convergence and reduced control effort in complex engagement scenarios. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution. Full article

While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding—an AI-assisted development and analysis methodology that allows for the ‘encoding’ and interpretation of the dynamic ‘vibe’ or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed–Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures. Full article

With the high penetration of distributed energy resources (DERs), which are characterized by stochasticity and intermittency, traditional centralized optimization methods face challenges such as communication packet loss, low reliability, and poor scalability in large-scale DC microgrids. Therefore, distributed optimization methods have attracted attention due to their robustness and scalability. This paper extends our previous conference work by proposing a convex-relaxation-based distributed control strategy for DC microgrids with constant power loads (CPLs) and maximum power point tracking (MPPT)-controlled distributed generations (MPPT-DGs). Furthermore, a control strategy based on distributed observers is designed to achieve global optimal control under sparse communication networks. First, an exact convex relaxation method is applied to transform the original non-convex optimal power flow (OPF) problem into a convex problem, with theoretical guarantees of exactness. Then, the Karush–Kuhn–Tucker (KKT) conditions are equivalently transformed into a consensus-based optimality condition and integrated into the distributed control framework. Next, small-signal stability analysis is performed to verify the system’s robustness. To reduce communication costs, a distributed observer-based control strategy is proposed, which can achieve optimal control under sparse communication networks. The impact of communication delays on system stability is also investigated. Finally, the simulation results verify the accuracy of convex relaxation, the effectiveness of the proposed control strategy, and its performance under communication delay. Full article

Modern TCP/IP networks are increasingly exposed to unpredictable conditions, both from the physical transmission medium and from malicious cyber threats. Traditional stochastic models often fail to capture the non-linear and highly sensitive nature of these disturbances. This work introduces a formal mathematical framework combining classical network modeling with chaos theory to describe perturbations in latency and packet loss, alongside adversarial processes such as denial-of-service, packet injection, or routing attacks. By structuring the problem into four scenarios (quiescent, perturbed, attacked, perturbed-attacked), the model enables a systematic exploration of resilience and emergent dynamics. The integration of artificial intelligence techniques further enhances this approach, allowing automated detection of chaotic patterns, anomaly classification, and predictive analytics. Machine learning models trained on simulation outputs can identify subtle signatures distinguishing chaotic perturbations from cyber attacks, supporting proactive defense and adaptive traffic engineering. This combination of formal modeling, chaos theory, and AI-driven analysis provides network engineers and security specialists with a powerful toolkit to understand, predict, and mitigate complex threats that go beyond conventional probabilistic assumptions. The result is a more robust methodology for safeguarding critical infrastructures in highly dynamic and adversarial environments. Full article

This paper investigates an optimal decision-making and optimization framework for networked systems operating under the coupled effects of stochastic transmission delays, packet dropouts, and input delays, which is a critical unresolved challenge in data-driven intelligent systems deployed over shared communication networks. Such uncertainty-aware optimization problems exhibit strong similarities to modern recommender and decision support systems, where multiple performance criteria must be balanced under dynamic and resource-constrained environments while addressing the disruptive impact of coupled network-induced uncertainties. By explicitly modeling stochastic transmission delays and packet losses in the sensor to controller channel, together with input delays in the actuation loop, the problem is formulated as a stochastic optimal control task with multi-stage decision coupling that captures the interdependency of communication uncertainties and system performance. An optimal feedback policy is derived based on a discrete time Riccati recursion explicitly quantifying and mitigating the cumulative impact of network-induced uncertainties on the expected performance cost, which is a capability lacking in existing frameworks that treat uncertainties separately. Numerical simulations using realistic traffic models validate the effectiveness of the proposed framework. The results demonstrate that the proposed decision optimization approach offers a principled foundation for uncertainty-aware optimization with potential applicability to data-driven recommender and intelligent decision systems where coupled uncertainties and multi-criteria trade-offs are pervasive. Full article

The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article

We study the throughput and losses of a buffer with stochastically dependent service times. Such dependence occurs not only in packet buffers within TCP/IP networks but also in many other queuing systems. We conduct a comprehensive, time-dependent analysis, which includes deriving formulae for the count of packets processed and lost over an arbitrary period, the temporary intensity of output traffic, the temporary intensity of packet losses, buffer throughput, and loss probability. The model considered enables mimicking any packet interarrival time distribution, service time distribution, and correlation between service times. The analytical findings are accompanied by numerical computations that demonstrate the influence of various factors on buffer throughput and losses. These results are also verified through simulations. Full article

Natural disasters can severely disrupt conventional communication systems, hampering relief efforts. High-altitude tethered balloon base stations (HATBBSs) are a promising solution to communication disruptions, providing wide communication coverage in disaster-stricken areas. However, a single HATBBS is insufficient for large disaster zones, and limited resources may restrict the number and energy capacity of available base stations. To address these challenges, this study proposes a cluster deployment of tethered balloons to form flying ad hoc networks (FANETs) as a backbone for post-disaster communications. A meta-heuristic-based multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize the placement of balloons and power control to maximize target coverage and system energy efficiency. Comparative analysis with a stochastic algorithm (SA) demonstrates that MOPSO converges faster, with significant advantages in determining optimal balloon placement. The simulation results show that MOPSO effectively improves network throughput while reducing average delay and packet loss rate. Full article

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations. Full article

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attacks and packet loss. Firstly, the stochastic characteristics of DoS attacks and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of the systems. Then, by constructing a general common Lyapunov functional and combining it with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through two examples. Full article