Search Results (2,576)

Vehicular Ad Hoc Networks (VANETs) support safety-critical and traffic-optimization applications through low-latency, reliable V2X communication. However, securing integrity and auditability with blockchain is challenging because conventional BFT-style consensus incurs high message overhead and latency. We introduce CoCoChain , a concept-aware consensus mechanism tailored to VANETs. Instead of exchanging full payloads, CoCoChain trains a sparse autoencoder (SAE) offline on raw message payloads and encodes each message into a low-dimensional concept vector; only the top-k activations are broadcast during consensus. These compact semantic digests are integrated into a practical BFT workflow with per-phase semantic checks using a cosine-similarity threshold $\theta =0.85$ (calibrated on validation data to balance detection and false positives). We evaluate CoCoChain in OMNeT++/SUMO across urban, highway, and multi-hop broadcast under congestion scenarios, measuring latency, throughput, packet delivery ratio, and Age of Information (AoI), and including adversaries that inject semantically corrupted concepts as well as cross-layer stress (RF jamming and timing jitter). Results show CoCoChain reduces consensus message overhead by up to 25% and confirmation latency by 20% while maintaining integrity with up to 20% Byzantine participants and improving information freshness (AoI) under high channel load. This work focuses on OBU/RSU semantic-aware consensus (not 6G joint sensing or multi-base-station fusion). The code, configs, and an anonymized synthetic replica of the dataset will be released upon acceptance. Full article

To overcome the obstacles of maintaining covert transmissions in wireless networks employing collaborative wardens, we develop a reinforcement learning framework that jointly optimizes cooperative jamming strategies and relay selection mechanisms. The study focuses on a multi-relay-assisted two-hop network, where potential relays dynamically act as information relays or cooperative jammers to enhance covertness. A reinforcement learning-based relay selection scheme (RLRS) is employed to dynamically select optimal relays for signal forwarding and jamming; the framework simultaneously maximizes covert throughput and guarantees warden detection failure probability, subject to rigorous power budgets. Numerical simulations reveal that the developed reinforcement learning approach outperforms conventional random relay selection (RRS) across multiple performance metrics, achieving (i) higher peak covert transmission rates, (ii) lower outage probabilities, and (iii) superior adaptability to dynamic network parameters including relay density, power allocation variations, and additive white Gaussian noise (AWGN) fluctuations. These findings validate the effectiveness of reinforcement learning in optimizing relay and jammer selection for secure covert communications under colluding warden scenarios. Full article

The Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) standard facilitates large-scale connectivity among smart devices in utility networks and smart cities. Specifically designed for Low-Power and Lossy Networks (LLNs), Wi-SUN FAN supports the formation of multiple Personal Area Networks (PANs) and mesh topologies with multi-hop transmissions. However, the node association process, divided into five junction states, often results in prolonged connection times, particularly in multi-hop networks, thereby limiting network scalability and reliability. This study analyzes the factors affecting these delays, with a particular focus on Join State 1 (JS1), which relies on PAN Advertisement (PA) packets that use asynchronous communication and the trickle timer algorithm, frequently causing significant delays. To overcome this challenge in JS1, we propose the Parallel Rendezvous (PR) strategy, which forms synchronized clusters of unassociated nodes and leverages the standard’s PAN Advertisement Solicit (PAS) packets to rapidly disseminate network information. The proposed algorithm, PR Wi-SUN FAN, is evaluated through simulations in various network topologies, demonstrating notable improvements in linear, fully connected, and mesh scenarios. The most significant gains are observed in the linear topology, with reductions of up to 71.22% in association time and 59.56% in energy consumption during JS1. Full article

We experimentally investigate the structural, magnetic, transport, and electronic properties of two d⁵ iridate double perovskite materials La₂BIrO₆ (B = Mg, Zn). Notably, despite similar crystallographic structure, the two compounds show distinctly different magnetic behaviors. The M = Mg compound shows an antiferromagnetic-like linear field-dependent isothermal magnetization below its transition temperature, whereas the M = Zn counterpart displays a clear hysteresis loop followed by a noticeable coercive field, indicative of ferromagnetic components arising from a non-collinear Ir spin arrangement. The local structure studies authenticate perceptible M/Ir antisite disorder in both systems, which complicates the magnetic exchange interaction scenario by introducing Ir-O-Ir superexchange pathways in addition to the nominal Ir-O-B-O-Ir super-superexchange interactions expected for an ideally ordered structure. While spin–orbit coupling (SOC) plays a crucial role in establishing insulating behavior for both these compounds, the rotational and tilting distortions of the IrO₆ (and MO₆) octahedral units further lift the ideal cubic symmetry. Finally, by measuring the Ir-L₃ edge resonant inelastic X-ray scattering (RIXS) spectra for both the compounds, giving evidence of spin–orbit-derived low-energy inter-J-state (intra t_2g) transitions (below ~1 eV), the charge transfer (O 2p → Ir 5d), and the crystal field (Ir t_2g → e_g) excitations, we put forward a qualitative argument for the interplay among effective SOC, non-cubic crystal field, and intersite hopping in these two compounds. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Background/Objectives: Dance, particularly hip-hop, offers a dynamic means of fostering physical activity (PA) and encouraging movement in health-related initiatives among children and youth in educational environments. Hip-hop offers benefits across motor, physical, social, and mental domains. Given the importance of PA in early development, and the preschool period as a sensitive phase for acquiring motor skills, this study aimed to examine the effects of the “Grow+” hip-hop program on motor competence (MC), perceived motor coordination (PMCoor), and PA levels in preschoolers. Methods: A quasi-experimental within-subjects design was used, including 37 children aged 3 to 4 (M = 4.29 ± 0.58). The intervention included two 4-week hip-hop periods, separated by a 4-week break. Four assessments were conducted using the MCA battery (MC), PA’s pictorial scales, and questionnaires completed by caregivers and educators (PMCoor). Data were analyzed using repeated measures ANOVA and Spearman correlations. Results: MC and PA levels showed a nonsignificant but positive trend across the study. Significant improvements in MC were observed during intervention periods, while no significant changes occurred during the break. Educators’ perceptions of PMCoor remained unchanged, despite improvements in MC. Conclusions: The findings suggest that the “Grow+” hip-hop program contributed meaningfully to improvements in MC and PA levels among children in early childhood. These findings accentuate the potential efficacy of structured rhythmic movement interventions in promoting motor development throughout early childhood, thereby supporting their integration into early childhood education curricula. Full article

This study aimed to test the reliability of seven functional performance tests in amateur trail runners, including ankle mobility, balance, hopping, and countermovement jump (CMJ) tests. The sample consisted of 35 runners who were evaluated in two sessions separated by 7 to 14 days, which varied due to participants’ scheduling constraints. Relative reliability was assessed using the Intraclass Correlation Coefficient (ICC, which indicates consistency between repeated measures), the Standard Error of Measurement (SEM, which reflects measurement precision), and the Minimal Detectable Change (MDC, which represents the smallest real change beyond measurement error). The results show high reliability in almost all tests. The Lunge Test obtained an ICC of 0.990 and 0.983 for distance, and 0.941 and 0.958 for angular measurements in both legs. The Hop Tests showed moderate reliability with ICC above 0.7 In contrast, the Y Balance Test demonstrated lower reliability, with ICC values ranging from 0.554 to 0.732. The CMJ test showed good reliability, with an ICC ranging from 0.753 to 0.894, an SEM between 5.79% and 11.3%, and an MDC ranging from 15.54% to 31.44%, making it useful for assessing lower limb explosive strength. Both tests presented comparatively higher error values, which should be considered when interpreting individual changes. These findings support the use of these tests as valid and reliable tools for evaluating ankle dorsiflexion, balance, functional symmetry, and lower limb explosive strength in amateur trail runners, prior to training programs or injury prevention strategies, provided that standardized protocols and validated measuring instruments are used. Full article

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

As a subclass of Mobile Ad hoc Networks (MANETs), Vehicle Ad hoc Networks (VANETs) possess multi-hop relay communication and dynamic topology reconstruction capabilities and are widely applied in various social activities. When they are used as clusters to perform various disaster search and rescue operations or communication relay, reliable, secure, and timely communication connectivity becomes particularly important. This paper focuses on the research of connectivity reliability in VANETs, emphasizing the impact of node movement characteristics and various failure modes on the connectivity reliability of VANETs: As a cluster, the nodes in VANETs have interactive relationships and no longer follow a random movement model, exhibiting regular movements of the network as a whole; the failure modes of nodes in VANETs include vehicular hardware/software failure, energy consumption failure, intentional attack, and isolation failure. Additionally, to optimize node communication energy consumption, the paper proposes a routing path identification algorithm. Finally, the paper presents a simulation algorithm for solving the connectivity reliability of VANETs. Through MATLAB simulation experiments, the effectiveness and correctness of the proposed algorithm are verified, and it is found that the attraction distance between nodes has a certain impact on the isolation failure mode and connectivity reliability. Full article

LoRa is a long-range, low-power wireless communication technology widely used in Internet of Things (IoT) applications. However, its conventional implementation through Long Range Wide Area Network (LoRaWAN) presents operational constraints due to its centralized topology and reliance on gateways. To overcome these limitations, this work designs and validates a gateway-free mesh communication system that operates directly on commercially available commodity microcontrollers, implementing lightweight self-healing mechanisms suitable for resource-constrained devices. The system, based on ESP32 microcontrollers and LoRa modulation, adopts a mesh topology with custom mechanisms including neighbor-based routing, hop-by-hop acknowledgments (ACKs), and controlled retransmissions. Reliability is achieved through hop-by-hop acknowledgments, listen-before-talk (LBT) channel access, and duplicate suppression using alternate link triggering (ALT). A modular prototype was developed and tested under three scenarios such as ideal conditions, intermediate node failure, and extended urban deployment. Results showed robust performance, achieving a Packet Delivery Ratio (PDR), the percentage of successfully delivered DATA packets over those sent, of up to 95% in controlled environments and 75% under urban conditions. In the failure scenario, an average Packet Recovery Ratio (PRR), the proportion of lost packets successfully recovered through retransmissions, of 88.33% was achieved, validating the system’s self-healing capabilities. Each scenario was executed in five independent runs, with values calculated for both traffic directions and averaged. These findings confirm that a compact and fault-tolerant LoRa mesh network, operating without gateways, can be effectively implemented on commodity ESP32-S3 + SX1262 hardware. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

Background: Neuromuscular training is widely implemented in professional football to enhance performance and reduce injury risk. Although unstable surfaces are commonly used for proprioceptive and rehabilitation purposes, limited evidence supports their effectiveness in improving sport-specific force production and stability in elite athletes. This study aimed to compare the effects of multicomponent neuromuscular training performed on stable versus unstable surfaces on unilateral force production, mobility, and agility in elite male soccer players. Methods: Twenty-seven professional male soccer players from the Spanish first division were randomly assigned to either a stable surface group (SSG; n = 14) or an unstable surface group (USG; n = 13). Both groups completed a 10-week intervention in addition to their regular training routines. Pre- and post-intervention assessments included dorsiflexion range of motion (DFt), Y-Balance Test (YBT), single-leg countermovement jump (SLCMJ), single-leg hop for distance (SLH), side-hop (SH), Speedy Jump (SpJ), Agility T-test (TT), and the Lower Extremity Functional Test (LEFT). A two-way repeated-measures ANOVA and Hedges’ g effect sizes were used for statistical analysis. Results: The SSG showed significant improvements in most performance variables, including DFt, YBT, SLH, SH, SpJ, TT, and LEFT (percent change range: 1.6% to 9.8%; Hedges’ g ranging from 0.52 to 2.57). The USG showed limited improvements, with significant changes only in LEFT (percent change = 1.18%; Hedges’ g = 0.53). Notably, the stable surface group demonstrated enhanced force production and agility, particularly in the non-dominant limb. Conclusions: Multicomponent neuromuscular training on stable surfaces appears more effective than training on unstable surfaces for improving unilateral strength, mobility, and agility in elite soccer players. These findings suggest that stable surface training may provide superior performance benefits and should be considered a priority in high-performance environments. Full article

This paper investigates physical layer security (PHY-security) for multi-hop transmission in underlay cognitive radio networks under various eavesdropping attacks. To enhance secrecy performance, we propose two opportunistic scheduling schemes. The first scheme, called the minimal node selection (MNS) scheme, selects the node in each cluster that minimizes the eavesdropper’s channel capacity. The second scheme, named the optimal node selection (ONS) scheme, chooses the node that maximizes secrecy capacity by using both the main and eavesdropper channel information. To reveal the relationship between network parameters and secrecy performance, we derive closed-form expressions for the secrecy outage probability (SOP) under different scheduling schemes and eavesdropping scenarios. Numerical results show that the ONS scheme provides the most robust secrecy performance among the considered schemes. Furthermore, we analyze the impact of key network parameters on secrecy performance. In detail, although the proposed ONS scheme requires more channel information than the MNS scheme, under a 20 dB interference threshold, the secrecy performance of the ONS scheme is 15% more robust than that of the MNS scheme. Full article

Hops essential oil (HEO), a by-product of the brewing industry, has known antibacterial and antifungal properties, but its antiparasitic effects remain underexplored. This study evaluated the cytotoxicity of HEO and its predominant monoterpene, myrcene, in intestinal cells and assessed their ability to reduce Cryptosporidium parvum infection in vitro. The cytotoxicity (IC₅₀) of HEO and myrcene was determined in HCT-8 intestinal cells using flow cytometry and propidium iodide staining after 24 and 48 h of exposure. The anti-Cryptosporidium activity of HEO and myrcene was assessed by infecting confluent HCT-8 cells with C. parvum sporozoites (1 × 10⁴ sporozoites/mL) and treating them with bioactives below their IC₅₀ values. Two treatment modalities were tested: (1) immediate treatment during infection (invasion) and (2) treatment initiated 2 h after infection (growth). Parasite growth was quantified using an immunofluorescence assay with a fluorescence-conjugated anti-Cryptosporidium antibody. HEO exhibited low cytotoxicity (IC₅₀ = 382.7 µg/mL), while myrcene showed higher cytotoxicity (IC₅₀ = 240.6 µg/mL). HEO reduced C. parvum growth in a dose-dependent manner, with IC₅₀ values of 45.8 and 58.7 µg/mL under either modality, respectively. Myrcene alone demonstrated greater anti-Cryptosporidium activity, with IC₅₀ values lower under the invasion modality (17.7 µg/mL) than the growth modality (28.1 µg/mL) on average for both food-grade and analytical standards. HEO and myrcene exhibited significant in vitro anti-Cryptosporidium activity, highlighting their potential as novel therapeutic agents against cryptosporidiosis. Full article