Search Results (1,022)

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article

Fibrin clots with strain-hardening characteristics exhibit pronounced material nonlinearity and acoustic dispersion under ultrasound, leading to waveform distortion and shock formation during finite-amplitude wave propagation. However, peak-shock stress is limited by viscoelastic dissipation and dispersion, constraining the efficiency of ultrasound in applications such as thrombus ablation. To overcome this limitation, a shock wave amplification method using designed multi-wave-packet sequences is proposed. Based on a power-law model from quasi-static compression tests, shock generation under a single sinusoidal pulse was first simulated. The dual-wave-packet chasing strategy was then developed, in which the amplitude, frequency, and time delay of the second packet were tuned to achieve effective superposition with the precursor. The waveform superposition factor (WSF) was introduced for quantitative evaluation. Numerical results demonstrate that this strategy can significantly increase the peak-shock-wave stress, with a maximum gain of 22.7%. Parametric analysis further identified amplitude as the dominant factor influencing wavefront steepness and amplification effectiveness. This study provides a novel method and theoretical support for developing efficient and controllable ultrasonic sequences for thrombolysis. Full article

Wireless sensor networks (WSNs) contend with the critical challenge of balancing energy conservation against data transmission delay, a trade-off that protocols such as PEGASIS—while being strong in energy efficiency—fail to manage optimally due to resulting high latency, unbalanced load distribution, and suboptimal cluster formation. To address these limitations, this paper introduces the Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) protocol, which is designed and implemented using a dual-phase machine learning strategy. This multi-objective approach works in two stages. First, it utilises K-means clustering to achieve robust spatial partitioning of the network. Second, it employs K-Nearest Neighbours (K-NN) classification to enable adaptive and intelligent routing. The simulation was performed using MATLAB R2025a, and the results show that EMO-PEGASIS addresses this multi-objective optimisation problem. The proposed EMO-PEGASIS protocol achieves a 45% reduction in average energy consumption, a 38% decrease in end-to-end delay, and a 67% increase in network lifetime compared to the original PEGASIS protocol. Additionally, EMO-PEGASIS demonstrates enhanced stability and effective load balancing under heterogeneous network configurations, while maintaining an excellent packet delivery ratio of 96.8%. These findings underscore the effectiveness of integrating machine learning techniques, which ultimately yield enhanced performance and enable reliable multi-objective optimisation within energy- and delay-constrained WSN environments. Full article

This study presents a comparative analysis of the novel guided-wave-based imaging method that integrates variational Bayesian principal component analysis with time-delay strategy for detecting internal and external defects in plate-like structures. The performance of the conventional delay-and-sum imaging method deteriorates when the signal-to-noise ratio of signals is low or when other wave packets overlap with the defect scattering signal. The imaging method based on variational Bayesian principal component analysis analyzes the principal components and corresponding singular values of the time-delayed signal array, and the maximum singular value represents the contribution of the most principal component, serving as an indicator of the coherent defect-related wave packets. Thus, the defect can be highlighted by accounting for the effect of noise and wave packet interference on the time-delayed signal array. However, when defects are located outside the sensor network, the limited information available may reduce the imaging performance. Numerical simulations and experimental studies conducted on plate-like structures demonstrate the proposed method achieves higher imaging clarity and localization accuracy for the internal defect compared with the external defect, with the former exhibiting mm-level absolute localization errors. 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

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

Vehicular ad hoc networks (VANETs) must simultaneously satisfy stringent reliability, latency, and sustainability targets under highly dynamic urban and highway mobility. Existing solutions typically optimise one or two dimensions (link stability, clustering, or energy) but lack an integrated, adaptive mechanism that fuses heterogeneous metrics while remaining lightweight and deployable. This paper introduces a VANET routing protocol named SYMPHONY (Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield) that operates in three coordinated layers: (i) a compact neighbourhood filtering stage that reduces forwarding scope and eliminates transient relays, (ii) a cluster layer that elects resilient cluster heads using fuzzy energy-aware metrics and backup leadership, and (iii) a global inter-cluster optimizer that blends a GA-reseeded swarm metaheuristic with a stability-aware pheromone scheme to produce multi-objective routes. Crucially, SYMPHONY employs an ultra-lightweight online weight-adaptation module (contextual linear bandit) to tune metric fusion weights in response to observed rewards (packet delivery ratio, end-to-end delay, and Green Performance Index). We evaluated the proposed routing protocol SYMPHONY versus strong modern baselines across urban and highway scenarios with varying density and resource constraints. The results demonstrate that SYMPHONY improves packet delivery ratio by up to 12–18%, reduces latency by 20–35%, and increases the Green Performance Index by 22–45% relative to the best baseline, while keeping control overhead and per-node computation within practical bounds. Full article

In ad hoc networks employing Autonomous Aerial Vehicles (AAVs), the importance of real-time applications and edge computing is steadily increasing. However, existing routing protocols still fail to meet the strict performance requirements under the unique conditions of AAV networks, where the network topology changes continuously, and nodes move at high speed. This paper presents SHARP-AODV (Stability Heuristic Adaptive Routing Protocol—AODV), an enhanced routing protocol specifically developed for AAV networks. SHARP-AODV introduces two key innovations: (1) an intelligent RREQ (Route Request) dissemination mechanism that combines neighbor density control with a multi-parameter probabilistic model, and (2) a multi-criteria path selection mechanism that jointly considers hop count, link quality, and resource state. Simulation results in NS-3 across four distinct mobility models and various numbers of AAV nodes show that SHARP-AODV significantly outperforms standard AODV, improving packet delivery ratio (PDR) by up to 23.9%, increasing throughput by up to 61%, while reducing end-to-end delay by up to 87.8% and jitter by up to 90.6%. The proposed protocol is especially suitable for AAV-enabled applications in Edge Computing and Metaverse ecosystems that require low-latency, highly reliable connectivity with adaptation to dynamic network conditions. Furthermore, SHARP-AODV satisfies 6G network requirements for connection reliability, ultra-low latency, and high device density, unlocking new opportunities for employing AAVs in smart cities, environmental monitoring, and distributed VR/AR systems. Full article

This paper proposes a transmission-path selection algorithm with joint computation and communication resource allocation for sixth-generation (6G) mobile edge computing (MEC) networks enhanced by helper-assisted device-to-device (D2D) communication and reconfigurable intelligent surfaces (RIS). The novelties of this work lie in the joint design of three key components: a helper-assisted D2D uplink scheme, a packet-partitioning cooperative MEC offloading mechanism, and RIS-assisted downlink transmission and deployment design. These components collectively enable diverse transmission paths under strict latency constraints, helping mitigate overload and reduce delay. To demonstrate its performance advantages, the proposed algorithm is compared with a baseline algorithm without helper-assisted D2D or RIS support, under two representative scheduling policies—modified maximum rate and modified proportional fair. Simulation results in single-base station (BS) and dual-BS environments show that the proposed algorithm consistently achieves a higher effective packet-delivery success percentage, defined as the fraction of packets whose total delay (uplink, MEC computation, and downlink) satisfies service-specific latency thresholds, and a lower average total delay, defined as the mean total delay of all successfully delivered packets, regardless of whether individual delays exceed their thresholds. Both metrics are evaluated separately for ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications services. These results indicate that the proposed algorithm provides solid performance and robustness in supporting diverse 6G services under stringent latency requirements across different scheduling policies and deployment scenarios. Full article

Network covert channels embed secret data into legitimate traffic, but existing methods struggle to balance undetectability, robustness, and throughput. Application-independent channels at lower protocol layers are easily normalized or disrupted by network noise, while application-dependent streaming schemes rely on handcrafted traffic manipulations that fail to preserve the spatio-temporal dynamics of real encrypted flows and thus remain detectable by modern machine learning (ML)-based classifiers. Meanwhile, with the rapid adoption of HTTP/3, Quick UDP Internet Connections (QUIC) has become the dominant transport for streaming services, offering stable long-lived flows with rich spatio-temporal structure that create new opportunities for constructing resilient covert channels. In this paper, a QUIC streaming-based Covert Channel framework, QuicCC-SMD, is proposed that dynamically Shapes Multi-Dimensional traffic features to identify and exploit redundancy spaces for secret data embedding. QuicCC-SMD models the statistical and temporal dependencies of QUIC flows via Markov chain-based state representations and employs convex optimization to derive an optimal deformation matrix that maps source traffic to legitimate target distributions. Guided by this matrix, a packet-level modulation performs through packet padding, insertion, and delay operations under a periodic online optimization strategy. Evaluations on a real-world HTTP/3 over QUIC (HTTP/3-QUIC) dataset containing 18,000 samples across four video resolutions demonstrate that QuicCC-SMD achieves an average F1 score of 56% at a 1.5% embedding rate, improving detection resistance by at least 7% compared with three representative baselines. Full article

Unmanned aerial vehicle (UAV) swarms have shown great potential in large-scale IoT (Internet of Things) and smart agriculture applications, particularly for cooperative monitoring and multi-target tracking in field environments. However, most existing coordination strategies assume ideal communication conditions, overlooking realistic network impairments such as congestion, packet loss, and latency. These impairments disrupt the timely exchange of information between UAVs and the ground base station, leading to delayed or lost control signals. As a result, coordination quality deteriorates and tracking performance is severely degraded in real-world deployments. To address this gap, we propose CSOOC (Communication-State Driven Online–Offline Coordination with Congestion Control), a hybrid control architecture that integrates centralized learning-based decision-making with decentralized rule-based policies to adapt UAV behaviors according to real-time network states. CSOOC consists of three key components: (1) an online module that enables centralized coordination under reliable communication, (2) an offline profit-driven mobility strategy based on local Gaussian maps for autonomous target tracking during communication loss, and (3) a congestion control mechanism based on STAR(Stratified Transmission and RTS/CTS), which combines temporal transmission desynchronization and RTS/CTS handshaking to enhance uplink reliability. We establish a unified co-simulation paradigm that connects network communication with swarm control and swarm coordination behavior. Experiments demonstrate that CSOOC achieves an average observation rate of 39.7%, surpassing baseline algorithms by 4.4–11.13%, while simultaneously improving network stability through significantly higher packet delivery ratios under congested conditions. These results demonstrate that CSOOC effectively bridges the gap between algorithmic performance in simulation and practical UAV swarm operations in communication-constrained environments. Full article

Mission-critical applications such as telemedicine demand extremely high communication reliability. Multipath redundant transmission ensures reliable packet delivery by concurrently forwarding replicated packets over multiple paths. However, fewer studies focus on implementing redundant transmission in a distributed manner by in-network nodes, which limits the applicability and efficiency of such mechanisms in wide-area networks. This paper proposes a Distributed Adaptive Multipath Redundant Transmission (DAMR-T) mechanism, where intermediate nodes autonomously determine replica counts and make routing decisions based on local link conditions and a custom residual loss budget field in each packet. Simulations show that DAMR-T achieves reliability comparable to the source-side replication scheme while incurring the lowest redundant bandwidth overhead among all evaluated schemes. Specifically, it reduces redundant bandwidth radio by an average of 35.44% compared to the source-side replication scheme, with up to 56.1% in specific scenarios. It also achieves the lowest 95th percentile first-arrival delay across all baselines. Full article

With the rapid development of the civil aviation industry, the reliability and real-time performance of airborne data transmission are becoming increasingly important. The traditional airborne network cannot meet the future flight requirements of the aircraft. To ensure the reliable and real-time transmission of data, the time-sensitive network introduces the Frame Replication and Elimination for Reliability (FRER) mechanism. The standard FRER mechanism defines the methods of frame replication and elimination of redundant frames. However, the description of how the replicated frames are transmitted is not in-depth. The frame replication and elimination function at the source and destination nodes will also reduce the reliability and real-time performance of the network. In order to realize the application of the time-sensitive network in the airborne network, this article independently builds an airborne time-sensitive network test simulation platform. It carries out in-depth research on improving the reliability of the network. It puts forward a path-finding algorithm based on a time-sensitive network with the FRER mechanism in response to the problem of low reliability of the selected data transmission paths in the airborne network. The algorithm integrates the constraints of transmission link delay and packet loss rate. It performs link reliability calculation before selecting redundant paths to obtain non-overlapping data transmission paths. The experimental results show that, compared with the dynamic link redundancy selection algorithm, the path delay is reduced by 21.51%. Compared with the multilevel P-cycle cascading algorithm, the path delay is reduced by 19.70%. At a 120 Mbps data transmission rate, the packet loss rate is reduced by 18.67% compared with the dynamic link redundancy selection algorithm. It is also reduced by 24.00% compared with the multilevel P-cycle cascading algorithm. These results show that the proposed method improves the reliability of data transmission in the airborne network. Full article

Accurate prediction of maximum flow latency is crucial for ensuring the efficient transport of latency-sensitive fronthaul traffic in packet-switched Xhaul networks while maintaining the reliable operation of 5G and beyond Radio Access Networks (RANs). Deterministic worst-case (WC) models provide strict latency guarantees but tend to overestimate actual delays, resulting in resource over-provisioning and inefficient network utilization. To address this limitation, this study evaluates a data-driven Quantile Regression (QR) model for latency prediction in Time-Sensitive Networking (TSN)-enabled packet-switched Xhaul networks operating under dynamic traffic conditions. The proposed QR model estimates high-percentile (tail) latency values by leveraging both deterministic and queuing-related data features. Its performance is quantitatively compared with the WC estimator across diverse network topologies and traffic load scenarios. The results demonstrate that the QR model achieves significantly higher prediction accuracy—particularly for midhaul flows—while still maintaining compliance with latency constraints. Furthermore, when applied to dynamic Xhaul network operation, QR-based latency predictions enable a reduction in active processing-node utilization compared with WC-based estimations. These findings confirm that data-driven models can effectively complement deterministic methods in supporting latency-aware optimization and adaptive operation of 5G/6G Xhaul networks. Full article

This paper discusses the method for extending the range of Magnetic Induction (MI) and its application in underwater networks for the Internet of Underwater Things (IoUT). In underwater communication, this technology would provide a wider frequency band than acoustic systems, shorter propagation delay, and increased conductivity, with the added benefit of underwater wireless power transfer. As a use case, we consider a system that allows energy to be transferred from one circuit to another without cables, as in an aerial environment. In this work, transmit and receive coils for underwater environments are designed and analyzed using ANSYS Maxwell v16.0 software. The results show an improvement in terms of underwater magnetic field propagation. We have conducted underwater experiments by applying a frequency range up to 100 kHz and 12 Volts with varied current, achieving a distance up to 80% greater than in air, as determined by parametric analysis. With an improved bit error rate, a delay of less than 2 microseconds, a packet delivery ratio near 100%, and a packet loss ratio less than 10%, the results show an improvement in magnetic field propagation underwater. This demonstrates that it is possible to conduct future research into other underwater applications by implementing MI for underwater communication. Full article