Search Results (1,155)

Thin-walled structures are widely used in aeronautical and aerospace engineering due to their light weight and high structural performance. Ensuring their integrity is crucial for safety and reliability, which is why structural health monitoring (SHM) methods, such as guided wave-based techniques, have been developed to detect and characterize damage in such components. This study presents a novel damage identification procedure for guided wave-based SHM using deep neural networks (DNNs) trained with experimental data. This technique employs the so-called wave damage interaction coefficients (WDICs) as highly sensitive damage features that describe the unique scattering pattern around possible damage. The DNNs learn intricate relationships between damage characteristics, e.g., size or orientation, and corresponding WDIC patterns from only a limited number of damage cases. An experimental training data set is used, where the WDICs of a selected damage type are extracted from measurements using a scanning laser Doppler vibrometer. Surface-bonded artificial damages are selected herein for demonstration purposes. It is demonstrated that smart DNN interpolations can replicate WDIC patterns even when trained on noisy measurement data, and their generalization capabilities allow for precise predictions for damages with arbitrary properties within the range of trained damage characteristics. These WDIC predictions are readily available, i.e., ad hoc, and can be compared to measurement data from an unknown damage for damage characterization. Furthermore, the fully trained DNN allows for predicting WDICs specifically for the sensing angles requested during inspection. Additionally, an anglewise principal component analysis is proposed to efficiently reduce the feature dimensionality on average by more than $90\%$ while accounting for the angular dependencies of the WDICs. The proposed damage identification methodology is investigated under challenging conditions using experimental data from only three sensors of a damage case not contained in the training data sets. Detailed statistical analyses indicate excellent performance and high recognition accuracy for this experimental data-based approach. This study also analyzes differences between simulated and experimental WDIC patterns. Therefore, an existing DNN trained on simulated data is also employed. The differences between the simulations and experiments affect the identification performance, and the resulting limitations of the simulation-based approach are clearly explained. This highlights the potential of the proposed experimental data-based DNN methodology for practical applications of guided wave-based SHM. Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

With the rapid development of unmanned aerial vehicle (UAV) manufacturing technology, large-scale UAV swarm ad hoc networks are becoming widely used in military and civilian spheres. UAV swarms equipped with ad hoc networks and satellite networks are being developed for 6G heterogeneous networks, especially in offshore and remote areas. A key operational aspect in large-scale UAV swarm networks is slot allocation for large capacity and a low probability of conflict. Traditional methods typically form coalitions among UAVs that are in close spatial proximity to reduce internal network interference, thereby achieving greater throughput. However, significant internal interference still persists. Given that UAV networks are required to transmit a substantial amount of safety-related control information, any packet loss due to internal interference can easily pose potential risks. In this paper, we propose a distributed time coalition formation game algorithm that ensures the absence of internal interference and collisions while sharing time slot resources, thereby enhancing the network’s throughput performance. Instead of forming a coalition from UAVs within a contiguous block area as used in prior studies, UAV nodes with no interference from each other form a coalition that can be called a time coalition. UAVs belonging to one coalition share their transmitting slots with each other, and thus, every UAV node achieves the whole transmitting slots of coalition members. They can transmit data packets simultaneously with no interference. In addition, a distributed coalition formation game-based TDMA (DCFG-TDMA) protocol based on the distributed time coalition formation algorithm is designed for UAV swarm ad hoc networks. Our simulation results verify that the proposed algorithm can significantly improve the UAV throughput compared with that of the conventional TDMA protocol. Full article

Recently, with the advantages of easy deployment, flexibility, diverse functions, and low cost, flying ad hoc network (FANET) has captured great attention for its huge potential in military and civilian applications, whereas the high-speed movement and limited node energy of unmanned aerial vehicles (UAVs) leads to high dynamic topology and high packet loss rate in FANET. Thus, we introduce the software-defined networking (SDN) architecture into FANET and investigate routing scheme in an SDN-FANET to harvest the advantages of SDN centralized control. Firstly, a FANET segmented routing scheme based on the hybrid SDN architecture is proposed, where inter-segment conducts energy-balanced routing and intra-segment adopts three-dimensional (3D) greedy perimeter stateless routing (GPSR). Specifically, we design the specific process of message interaction between SDN controller and UAV nodes to ensure the execution of the inter-segment routing based on energy balance. Further, to reduce the packet loss rate in high-speed motion scenes, an adaptive extended Kalman prediction algorithm is also proposed to track and predict the 3D movement of UAVs. Simulations verify the effectiveness of the proposed routing scheme in terms of end-to-end delay and packet delivery ratio. Full article

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%. Full article

Road safety is a critical concern, as accidents happen globally. Despite efforts to enhance roads and enforce stricter driving rules, the number of accidents remains high. These issues arise from distracted driving, speeding, and driving under the influence. In the United States, fatal accidents increased by 16% from 2018 to 2022. The number of deaths rose from 36,835 in 2018 to 42,795 in 2022. This trend reveals a critical need for new solutions to reduce traffic incidents and improve road safety. Machine learning (ML) can help make roads safer and reduce traffic-related deaths. This paper presents an ML-based real-time highway traffic incident forecasting system named “VehiCast”. VehiCast utilizes vehicular ad hoc networks (VANETs) and federated learning (FL) to collect real-time traffic data, such as average delay, average speed, and the total number of vehicles across several highway zones, to enhance traffic incident prediction accuracy in real-time. Our extensive experimental results showcase that VehiCast reaches an impressive prediction accuracy of 91%, highlighting the power of innovation and determination. Full article

Mobile ad hoc networks (MANETs) are becoming a popular networking technology as they can easily be set up and provide communication support on the go. These networks can be used in application areas, such as battlefields and disaster relief operations, where infrastructure networks are not available. Like media access control protocols, MANET routing protocols can also play an important role in determining network capacity and system performance. Research on the impact of heterogeneous nodes in terms of MANET performance is required for proper deployment of such systems. While MANET routing protocols have been studied and reported extensively in the networking literature, the performance of heterogeneous nodes/devices in terms of system performance has not been fully explored yet. The main objective of this paper is to review and compare the performance of four selected MANET routing protocols (AODV, OLSR, BATMAN and DYMO) in a heterogeneous MANET setting. We consider three different types of nodes in the MANET routing performance study, namely PDAs (fixed nodes with no mobility), laptops (low-mobility nodes) and mobile phones (high-mobility nodes). We measure the QoS metrics, such as the end-to-end delays, throughput, and packet delivery ratios, using the OMNeT++-network simulator. The findings reported in this paper provide some insights into MANET routing performance issues and challenges that can help network researchers and engineers to contribute further toward developing next-generation wireless networks capable of operating under heterogeneous networking constraints. Full article

The exponential growth of wireless traffic has imposed new technical challenges on the Internet and defined new approaches to dealing with its intensive use. Caching, especially cooperative caching, has become a revolutionary paradigm shift to advance environments based on wireless technologies to enable efficient data distribution and support the mobility, scalability, and manageability of wireless networks. Mobile ad hoc networks (MANETs), wireless mesh networks (WMNs), Wireless Sensor Networks (WSNs), and Vehicular ad hoc Networks (VANETs) have adopted caching practices to overcome these hurdles progressively. In this paper, we discuss the problems and issues in the current wireless ad hoc paradigms as well as spotlight versatile cooperative caching as the potential solution to the increasing complications in ad hoc networks. We classify and discuss multiple cooperative caching schemes in distinct wireless communication contexts and highlight the advantages of applicability. Moreover, we identify research directions to further study and enhance caching mechanisms concerning new challenges in wireless networks. This extensive review offers useful findings on the design of sound caching strategies in the pursuit of enhancing next-generation wireless networks. Full article

Unmanned Aerial Vehicles (UAVs) have revolutionized numerous domains by introducing exceptional capabilities and efficiencies. As UAVs become increasingly integrated into critical operations, ensuring the security of their communication channels emerges as a paramount concern. This paper investigates the importance of safeguarding UAV communication against cyber threats, considering both intra-UAV and UAV–ground station interactions in the scope of the Flying Ad Hoc Networks (FANETs). To leverage the advancements in security methodologies, particularly focusing on Physical Unclonable Functions (PUFs), this paper proposes a novel authentication framework tailored for UAV networking systems. Investigating the existing literature, we categorize related studies into authentication strategies, illuminating the evolving landscape of UAV security. The proposed framework demonstrated a high level of security with lower communication and computation costs in comparison with selected studies with similar types of attacks. This paper highlights the urgent need for strong security measures to mitigate the increasing threats that UAVs encounter and ensure their sustained effectiveness in a variety of applications. The results indicate that the proposed protocol is sufficiently secure and, in terms of communication cost, achieves an 18% improvement compared to the best protocol in the referenced studies. Full article

In vehicular ad hoc networks (VANETs), the presence of untrustworthy nodes poses a significant threat, impacting the network’s reliability. This has led to the emergence of node-centric misbehavior detection as a crucial aspect of VANET security, focusing on the behavior of vehicles rather than the content of their interactions. While the trust model is a popular approach, the computational complexity of trust computations and management in VANETs is attributed to the intricate relationships among vehicles and the dynamic autonomous movement of nodes. To tackle these challenges, we developed a hybrid trust model scheme for node-centric misbehavior detection. Our method represents complex vehicular relationships using a hyper-graph within a dynamic behavior-homogeneous cluster. The model incorporates direct and indirect trust in a multi-layered hybrid trust framework, enabling accurate computation of the aggregate trust level for each cluster member vehicle. Experimental results demonstrate the effectiveness of our scheme, particularly in high-density vehicle cooperation scenarios, highlighting its promising ability to detect misbehaving nodes. Full article

The integrated heterogeneous 7G/8G networks may face multiple challenges for reliable data delivery such as link disruption, intermittent link availability, long latency and a highly lossy channel. Delay tolerant networking (DTN) was proposed as a highly reliable networking technology for space networks that will be part of future 7G/8G networks. In this paper, an experimental evaluation of transmission control protocol (TCP)-based DTN (i.e., running TCP at the transport layer of DTN) for space-vehicle communications in the cislunar domain is presented. The impact of link disruption is also considered. The evaluation was conducted using the DTN protocol suites over a realistic experimental testbed. The study results show that TCP-based DTN works effectively for space-vehicle communications in cislunar domain in the presence of a link disruption event. However, a roughly exponential goodput decrease is observed with a linear increase in link delay from 1250 ms to 5 s. Full article

This paper presents the concept, design, and performance analysis of an active radio wake-up and radio identification subsystem as part of an advanced localization device intended to operate within a large-scale freight wagon localization system. The system provides an efficient and cost-effective way to localize freight carriages, which, in the majority of cases, are currently not tracked. The localization device is battery-powered and uses an ultra-low-power radio interface for detecting wake-on-radio signals from nearby operator devices. The same interface is also used for communication within an ad-hoc wireless mesh network, which allows the localization devices to select the best device to send out localization information from the whole cluster through a cellular connection in order to minimize overall battery energy usage. The article presents the overall system architecture construction of the radio interface, including the wake-up subsystem, as well as the results of performance measurements. Full article

Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML. Full article

IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect to the network using IPv6. This study focuses on examining the performance and power consumption of routing protocols in the context of 6LoWPAN, drawing insights from prior research and utilizing simulation techniques. The simulation involves the application of routing protocols, namely Routing Protocol for Low-power and Lossy (RPL) Networks, Ad hoc On-demand Distance Vector (AODV), Lightweight On-demand Ad hoc Distance-vector Next Generation (LOADng), implemented through the Cooja simulator. The simulation also runs in different network topologies to gain an insight into the performance of the protocols in the specific topology including random, linear, and eclipse topology. The raw data gathered from the tools including Powertrace and Collect-View were then analyzed with Python code to transfer into useful information and visualize the graph. The results demonstrate that the power consumption, specifically CPU power, Listen Power, and Total Consumption Power, will increase with the incremental of motes. The result also shows that RPL is the most power-efficient protocol among the scenarios compared to LOADng and AODV. The result is helpful because it brings insights into the performance, specifically power consumption in the 6LoWPAN network. This result is valuable to further implement these protocols in the testbed as well as provide an idea of the algorithmic enhancements. Full article

Vehicular Ad Hoc Networks play a crucial role in enabling Smart City applications by facilitating seamless communication between vehicles and infrastructure. This study evaluates the throughput performance of different routing protocols, specifically AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM, under various city and highway scenarios in complex networks. The analysis covers key parameters including traffic generation, packet sizes, mobility speeds, and pause times. Results indicate that TOM and DEM profiles significantly improve throughput compared to traditional AODV and GPSR protocols. GPSR:TOM achieves the highest throughput across most scenarios, making it a promising solution for high-performance data transmission in Smart Cities. For instance, GPSR:TOM achieves an average throughput of 3.2 Mbps in city scenarios compared to 2.8 Mbps for GPSR, while in highway scenarios, the throughput increases to 3.6 Mbps. Additionally, AODV:DEM records a throughput of 3.4 Mbps for high traffic generation, outperforming AODV:TOM at 3.1 Mbps and baseline AODV at 2.7 Mbps. The findings highlight the importance of optimizing data throughput to ensure reliability and efficiency in complex vehicle interconnection systems, which are critical for traffic management, accident prevention, and real-time communication in smart urban environments. Full article