Search Results (582)

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks. Full article

Short-term passenger-flow forecasting at integrated transport hubs requires accurate forecasts with explicit target semantics. This study evaluates long short-term memory (LSTM) and temporal convolutional network (TCN) fusion models using two distinct 5 min targets at Shenzhen North Station: a 2023–2026 regional population-mobility crowd-density proxy and a 2025 automatic fare collection (AFC) exit-card throughput series. On the proxy task, chronological 70/15/15 splits, a 12-step purge and full-epoch neural training showed that the LSTM-only variant (M2) achieved the lowest neural mean absolute error (MAE) at 5 min, while the fused LSTM–TCN variant (M4) achieved the lowest neural MAE at 15–60 min. Ridge was the strongest conventional proxy reference at 5 min and Random Forest at 15–60 min. On AFC exits, Random Forest was strongest among the six conventional references at all four horizons, while M4 remained better than ridge at 5–60 min. The raw 5 min proxy–AFC Pearson correlation was 0.553; after weekday-by-clock de-seasonalization it was 0.505 (95% day-block confidence interval 0.457–0.538). These values support a shared-signal interpretation; calibration requires a separate mapping model. In the observed-arrival pressure test, recovery at 21:10 reduced cumulative queueing by 67.3%; delays of 10 and 15 min reduced the benefit to 63.4% and 61.7%. The workflow provides 5–60 min lead time for gate and queue preparation, staff rostering and feeder coordination. Full article

To address the challenges of relying on experience for vehicle allocation in concrete transportation for dam construction—as well as the difficulty in coordinating vehicle capacity, loading/unloading capabilities, and system queuing status—this study focuses on a closed-loop transportation system comprising “batching plant—transport route—pouring area” and develops a vehicle allocation model based on a BCMP (Baskett–Chandy–Muntz–Palacios) closed-loop queuing network. The loading process at the mixing plant and the unloading process at the silo yard are modeled as finite-service nodes, while the transportation of loaded vehicles and the return of empty vehicles are modeled as infinite-service nodes. The Buzen convolution algorithm is used to solve for the system’s steady-state performance. With the number of vehicles N and the silo yard diversion ratio p as joint decision variables, the feasible region for vehicle configuration and recommended solutions are determined subject to constraints on silo yard feed demand and node utilization. The results of the case study show that, using reference operating condition S0 as a benchmark, a decrease in the loading efficiency of the batching plant increased the recommended number of vehicles by 28.57%; a simultaneous increase in the unloading efficiency of the dual-bin system reduced the recommended number of vehicles by 42.86%; enhanced coordination between the loading and unloading systems reduced the recommended number of vehicles by 50.00% and decreased the expected queuing time per cycle by 97.26%; furthermore, the system bottleneck shifted as loading and unloading capacities changed. Further independent validation was conducted using discrete-event simulation (DES); the maximum relative error between BCMP and DES in terms of system throughput, expected queuing wait time per cycle, and maximum node utilization was less than 1.5%. Sensitivity analysis indicates that concrete pouring demand and unloading capacity at the bin area are key factors affecting vehicle allocation. When demand increases or unloading capacity decreases beyond the system’s capacity limits, simply adding more vehicles does not result in a feasible solution. The study demonstrates that the proposed method can quantitatively reveal the relationships among vehicle fleet size, traffic diversion at the bin area, loading and unloading capacity, and queueing conditions, thereby providing a decision-making basis for the coordinated allocation of vehicles and loading/unloading resources in concrete construction for dams. Full article

This paper presents a scalable edge-based multilingual speech processing system designed for secure communication environments with limited or intermittent network access. The proposed architecture integrates speech enhancement, language identification, streaming automatic speech recognition, confidence-aware re-ranking, and secure local output delivery within a unified on-premise framework. Experimental evaluation was conducted using a multilingual corpus containing clean speech, command phrases, and degraded radio-channel recordings in Kazakh, Russian, English, and Turkish. Results of the experiments indicate that practical deployment performance depends not only on recognition accuracy, but also on latency stability, thermal resilience, queue management, and robustness to mixed-language noisy speech. Full article

Immersive media services increasingly rely on edge-cloud rendering to deliver interactive visual content under dynamic network, computing, and mobility conditions. Rendering an entire scene as a single service couples interaction-sensitive foreground content with context-oriented background content, making it difficult to jointly control latency, quality, synchronization, and migration overhead. This paper studies sense-driven edge-cloud foreground–background split rendering for immersive media services. We formulate an online decision problem in which foreground and background rendering layers can be independently controlled under long-term system and migration cost budgets. The formulation turns structural scene separation into a coupled layer-state control problem by preserving asymmetric QoE roles and a common composition requirement. We propose SEELE, a Lyapunov-guided online control algorithm that represents accumulated budget pressure with two virtual queues and converts the long-term constrained problem into lightweight per-slot decisions. The resulting per-slot rule balances immediate QoE loss against queue-weighted system and migration costs. Under sustained resource and network stress, SEELE provides steady-state QoE statistically comparable to a pretrained PPO policy while significantly reducing synchronization violations and improving composition stability. It also improves steady-state QoE and system debt over deterministic and QoE-prioritized baselines. A prototype implementation and controlled characterization further validate split-stream deployment, runtime observability, practical control hooks, and the latency–capacity tradeoff of layered rendering. Full article

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. This paper presents a Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model. The UAV acts as an aerial decode-and-forward relay between IoT devices and a Base Station (BS), with a Deep Q-Network (DQN) making decisions based on queue backlogs, channel conditions, UAV position, and remaining battery. The reward function balances Energy Efficiency (EE), queue stability, fairness, and battery longevity. We benchmark the DQN against six baselines; Round Robin (RR), Random Allocation (RA), the Single-to-Noise Ratio (Max-SNR), Proportional Fair (PF), a Lyapunov heuristic, and a GreedyEE scheme; across a range of device counts, traffic loads, battery budgets, and flight altitudes. Simulations consistently show that the DQN outperforms all baselines, including a RA baseline with equal access to UAV mobility; in EE, throughput, delay, and fairness, confirming that the gain stems from the learned joint control policy rather than from UAV mobility being available. Full article

This study investigates how coordinated wireless and fast charging services reshape electric vehicle departure time and path–charging choices when a trip-level charging requirement must be completed before arrival. A multi-class dynamic user equilibrium model is formulated for road networks containing wireless charging lanes and fast charging stations. An energy-aware dynamic network loading model propagates traffic and battery states, transfers upstream wireless energy into the residual station workload, and determines endogenous waiting. The equilibrium is expressed as a finite-dimensional variational inequality and solved by an energy-aware inertial fixed-point framework with safeguarded route swapping and independent verification. Experiments on the Nguyen–Dupuis and Sioux Falls networks show that low-state-of-charge users depart 6.91 min earlier on average, while exposure-informed wireless-charging placement can substantially reduce downstream station waiting and exhibits saturation once all behaviorally exposed links are active. Under compound demand and low-state-of-charge pressure, roadway queues activate more sharply than station waiting. In a common Sioux Falls algorithm benchmark, the inertial method reaches stable acceptance in 776.2 s compared with 1562.9 s for its non-inertial counterpart. The method of successive averages crosses the practical gap threshold earlier but does not satisfy the common flow-stability criterion within 3000 updates and 9018.1 s. Across 30 final Sioux Falls scenarios, all solutions satisfy the practical verified gap and physical feasibility gates, with 11 difficult cases requiring explicit route-swap continuation. The results clarify the complementary operational roles of corridor and station charging while delimiting the numerical and behavioral assumptions of the framework. Full article

Frame aggregation is central to modern IEEE 802.11 networks, yet the existing performance models fail to capture how it behaves under usual unsaturated traffic. Some rely on a predefined service-time distribution; others cover only narrow unsaturated cases, such as stations withholding transmission until K packets accumulate or stations being modeled as if they always have a packet queued. This paper develops a performance model for 802.11 networks with frame aggregation under unsaturated traffic in which the aggregation size and service time emerge dynamically from the offered traffic load, the random backoff process, and the number of stations rather than from any of these simplifying assumptions. Beyond throughput, the model derives closed-form estimates of the average aggregation size, service time, and per-station queue length directly from the steady-state distribution of a three-dimensional Markov chain. Performance evaluations across two physical-layer rates (867 and 150 Mbps), two queue capacities, and different numbers of stations show that the proposed model produces throughput and aggregation-size estimates that closely match an event-driven simulator, while the service time and queue-length estimates reflect the model’s own assumption. Full article

Improving sample efficiency and achieving balanced multi-task performance across different tasks remain important research challenges in reinforcement learning. Yie Ar Kung-Fu is formulated as a multi-task reinforcement learning environment. A task-specific neural network architecture, simplified state and action encoding schemes, a reward mechanism, and a dynamic learning-rate schedule are designed for the game environment. Based on Proximal Policy Optimization (PPO), a parallel training method is further proposed. The method aggregates episode samples generated from different opponent environments into a unified sample queue in the order of episode completion and dynamically adjusts the weights of samples from different opponents in the loss function based on their win rates. Experiments were conducted on the AutoDL platform using an NVIDIA RTX 4090 GPU. The proposed method enables joint training across multiple opponent environments. After approximately 15,000 training episodes, the trained agent achieved win rates exceeding 90% against all opponents and attained a level-clear rate of 79%, demonstrating strong game-playing performance and balanced performance across multiple opponent tasks. Furthermore, interdisciplinary analogies are used to interpret how parallel training improves sample efficiency and balanced performance across tasks. The proposed method is simple to implement, stable during training, and readily applicable to other complex multi-task environments. Full article

Renewable energy and energy storage stations require frequent updates of monitoring and control logic across heterogeneous devices and changing operating strategies. This paper proposes a rule graph-based reference architecture that combines low-code logic configuration, graph–model semantic binding, and microservice-oriented functional decomposition. A component status matrix separates the target architecture from the implemented subset. The runnable subset comprises a minimal FastAPI backend, REST/WebSocket telemetry interfaces, an in-process queue, and stateful rule evaluators; gateway, authentication, external message bus, time-series database, visual editor, and industrial protocol services remain design-level elements. Beyond the original single-rule example, a priority-ordered multi-device rule is implemented for cooperative BESS dispatch, communication/topology blocking, low-SOC protection, frequency-based load shedding, backup request, and five-sample recovery release. Existing local network benchmarks are complemented by a 600-step software-in-the-loop trace with scripted telemetry fluctuations and communication quality faults and by 500 in-process ASGI timing samples at each of the four point levels. The trace produced no safety dispatch or protected device violations. P99 application path latency ranged from 1.1962 to 5.5287 ms, but one 75.3065 ms outlier exceeded a 50 ms reference deadline, demonstrating that the Windows/FastAPI path is not deterministic. No industrial controller, hardware-in-the-loop facility, field data, or engineer usability study was used. Accordingly, the paper makes no claim of industrial real-time readiness or measured development effort reduction. Full article

Marine cross-medium acoustic–radio collaborative networks must route traffic across heterogeneous underwater acoustic and radio links while coping with energy imbalance, congestion, and mobility-induced link instability. This paper proposes Q-Learning AODV, an AODV extension that integrates distributed Q-value updating and multipath route maintenance into existing RREQ, RREP, and HELLO procedures. The routing reward combines normalized residual energy, queue availability, inter-node distance/link stability, relative velocity, and, for air–sea links, elevation-angle information. The protocol maintains multiple node-disjoint candidate paths and forwards data through the currently highest-valued path. NS-3 simulations are reported for underwater-to-underwater, underwater-to-air, and air-to-underwater communication scenarios. Relative to conventional AODV, Q-Learning AODV increases packet delivery ratio from 55.8% to 88.3%, from 68.3% to 76.1%, and from 86.9% to 91.9%, corresponding to relative improvements of 58.2%, 11.4%, and 5.8%, respectively. The results indicate improved delivery reliability and communication-subsystem energy balancing at the cost of additional state exchange, Q-table storage, and route-selection computation. Full article

Work zones reduce roadway capacity and create unstable merging, queue spillback, and stop-and-go conditions that degrade traffic operations while elevating crash risk. Conventional fixed-time, actuated, and adaptive controllers are poorly suited to these non-stationary conditions, and most reinforcement learning approaches optimize mobility while treating safety only as a post hoc evaluation measure. This study develops a safety-aware Deep Q-Network framework for adaptive signal control at intersections operating near work zone activity areas. Merge conflict risk, upstream spillback propagation, and stop-and-go instability are embedded directly into both the state representation and the reward formulation, alongside operational objectives. A merge-conflict model based on relative spacing, relative speed, and acceleration characterizes unsafe interactions in the merge region, and a Pareto-based procedure samples reward-weight vectors to identify non-dominated policies. The framework was evaluated in a SUMO microscopic simulation of a signalized intersection under lane closure. Relative to default fixed-time control, the selected policy increased throughput by 24.6–37.3% across vehicle classes (p < 0.001; Cohen’s d = 0.53–1.29), with the largest gains for trucks and buses, and reduced maximum queue length by 39.1% and spillback distance by 45.8%. The findings show that a single controller trained with surrogate safety indicators as learning objectives can improve operational performance while reducing safety-critical instability in work zones. Full article

The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions. Full article

Integrated Modular Avionics (IMA) integrates safety-critical functions on shared computing and network resources, creating coupling channels through which a local fault may escalate into a catastrophic system-level scenario. This study develops a graph-based fractional-order model for cascade escalation in IMA architectures with communication delays and reconfiguration failures. The architecture is represented as a weighted directed graph of core processing modules, network switches, and remote data concentrators, where each node carries functional degradation and queue-backlog states. The proposed delayed Caputo fractional-order dynamics incorporate degradation propagation, backlog spillover, mixed-criticality priority conflict, and a state-dependent reconfiguration-failure mechanism. We establish well-posedness and positive invariance of the feasible state domain, derive a sufficient cascade threshold that separates a delay-independent, globally Mittag–Leffler stable nominal regime from a supercritical regime in which bistability and catastrophic attractors may occur, and characterize delay-induced oscillatory instability together with a memory-stabilization effect. Numerical experiments on a synthetic 22-node IMA configuration show fault absorption below the threshold, reconfiguration-contained cascades under sufficient supervisory capacity, and global escalation when reconfiguration collapses under load. The results indicate that backlog growth is an early warning signal and that maintaining the cascade threshold below unity while provisioning reconfiguration capacity above the tipping point can support safer reconfiguration-policy design in certifiable avionics. Full article