Search Results (1,194)

Software-Defined Networking (SDN) is a network architecture that decouples the control plane from the data plane, enabling centralized, programmable management of network traffic. SDN introduces centralized control and programmability to modern networks, improving flexibility while also exposing new security vulnerabilities across the application, control, and data planes. This paper provides a comprehensive overview of SDN security threats and defenses, covering recent developments in controller hardening, trust management, route optimization, and anomaly detection. Based on these findings, we present a comparative analysis of SDN controllers in terms of performance, scalability, and deployment complexity. This culminates in the introduction of the Cloud-to-Edge Layer Two (CELT)-Secure switch, a virtual OpenFlow-based data-plane security mechanism. CELT-Secure detects and blocks Internet Control Message Protocol flooding attacks in approximately two seconds and actively disconnects hosts engaging in Address Resolution Protocol-based man-in-the-middle attacks. In comparative testing, it achieved detection performance 10.82 times faster than related approaches. Full article

Vehicle electrification is one of the main development directions within the automobile industry. However, due to the range limit of electric vehicles, electric vehicle users generally have range anxiety, especially toward long-haul driving. Therefore, there is an urgent need to effectively coordinate route planning and charging during long-haul driving, especially considering factors such as insufficient charging facilities, long charging times, battery aging, and changes in energy consumption under variable-temperature environments. In this study, the goal is to collaboratively optimize route planning and charging strategies. To achieve this goal, a mixed-integer nonlinear model is developed to minimize the total system cost, an electrochemical model is applied to accurately track the battery state, and a two-layer IACO-SA is proposed. Finally, the highway network in five provinces of China is adopted as an example to compare the optimal scheme results of our model with those of three other models. The comparison results prove the effectiveness of the proposed model and solution algorithm for the collaborative optimization of route planning and charging strategies of electric vehicles during long-haul driving. Full article

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. Full article

Underwater image enhancement (UIE) is a key technology in the fields of underwater robot navigation, marine resources development, and ecological environment monitoring. Due to the absorption and scattering of different wavelengths of light in water, the quality of the original underwater images usually deteriorates. In recent years, UIE methods based on deep neural networks have made significant progress, but there still exist some problems, such as insufficient local detail recovery and difficulty in effectively capturing multi-scale contextual information. To solve the above problems, a Multi-Scale Contextual Fusion Residual Network (MCFR-Net) for underwater image enhancement is proposed in this paper. Firstly, we propose an Adaptive Feature Aggregation Enhancement (AFAE) module, which adaptively strengthens the key regions in the input images and improves the feature expression ability by fusing multi-scale convolutional features and a self-attention mechanism. Secondly, we design a Residual Dual Attention Module (RDAM), which captures and strengthens features in key regions through twice self-attention calculation and residual connection, while effectively retaining the original information. Thirdly, a Multi-Scale Feature Fusion Decoding (MFFD) module is designed to obtain rich contexts at multiple scales, improving the model’s understanding of details and global features. We conducted extensive experiments on four datasets, and the results show that MCFR-Net effectively improves the visual quality of underwater images and outperforms many existing methods in both full-reference and no-reference metrics. Compared with the existing methods, the proposed MCFR-Net can not only capture the local details and global contexts more comprehensively, but also show obvious advantages in visual quality and generalization performance. It provides a new technical route and benchmark for subsequent research in the field of underwater vision processing, which has important academic and application values. Full article

This study presents a method for evaluating and optimizing the service network capacity of Urban Rail Transit Networks (URTNs) based on existing infrastructure conditions. By integrating passenger route choice behavior, the method assesses the network’s potential maximum capacity through the actual utilization rates of throughput capacity across various sections and routes. Furthermore, by incorporating route design and Transit-Oriented Development (TOD) strategies, the approach achieves a dual enhancement of network capacity and service quality. An optimization model was developed to maximize the network capacity while minimizing passenger travel costs, and it was solved using Adaptive Large Neighborhood Search (ALNS) and the Method of Successive Averages (MSA) algorithms. A case study of the Chongqing URTN demonstrated the model’s effectiveness. The results indicate that integrating route design and TOD strategies can significantly enhance the service capacity of urban rail networks. This method will assist decision-makers in understanding the current utilization status of the network’s capacity and evaluating its potential capacity. During TOD planning at stations, it simultaneously assesses changes in network capacity, thereby achieving a balance between land development, passenger demand, and the transportation system. Full article

The metro railway system is pivotal not just as a crucial transportation network for daily commuters but also as a significant enhancer of urban vibrancy, especially through its role in attracting a substantial volume of non-commuters. This study focuses on non-commuting travel behaviors around metro stations, exploring how the built environment affects non-commuters’ destination choices. A Random Forest model is developed based on data from Chengdu, China. The model is interpreted with SHapley Additive exPlanations (SHAP) analysis. Route length, building coverage, greenery, and proximity are key factors and indicate a nonlinear impact on non-commuters’ destination choices. The impact of these factors was found to vary significantly depending on the scale and context, indicating a need for nuanced urban planning approaches. The findings highlight the need for sophisticated urban planning that balances functionality and needs in transit-oriented development, aiming to cater to non-commuters and promote sustainable, vibrant urban spaces. Full article

To address the issues of cluster imbalance and inefficient energy utilization in IoT-based microgrids with static nodes, a new routing protocol, called the link efficiency and available-energy-driven routing protocol (LEAD-RP), has been developed. This protocol introduces a flexible method for determining the optimal number of cluster heads by minimizing overall energy consumption during both cluster formation and steady-state transmission phases, thus improving network efficiency. The protocol refines the cluster head selection process by integrating residual energy and distance factors, resulting in a more balanced energy distribution and strategically placed cluster heads. Simulation results demonstrate that the LEAD-RP significantly extends the network’s operational lifetime. Full article

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments. Full article

Path flow always plays a critical role in extracting vehicle travel patterns and reflecting network-scale traffic features. However, the comprehensive topological structure of urban road networks induces massive route choices, so frequent travel routes have been gradually regarded as an ideal countermeasure to represent traffic states. Widely used license plate recognition (LPR) devices can collect the abundant traffic features of all vehicles, but their sparse spatial distributions restrict the conventional models in frequent travel identification. Therefore, this study develops a network reconstruction method to construct a topological network from the LPR dataset, avoiding the adverse effects caused by the sparse distribution of detectors on the road network and further uses the Snake algorithm to fully utilize the road network structure and traffic attributes for clustering to obtain various travel patterns, with frequent routes under different travel patterns finally identified based on Steiner trees and frequent item recognition. To address the sparse spatial distribution of LPR devices, we utilize the word2vec model to extract spatial correlations among intersections. A threshold-based method is then applied to transform the correlation matrix into a reconstructed network, connecting intersections with strong vehicle transition relationships. This community structure can be interpreted as representing different travel patterns. Consequently, the Snake algorithm is employed to cluster intersections into distinct categories, reflecting these varied travel patterns. By leveraging the word2vec model, the detector installation rate requirement for Snake is significantly reduced, ensuring that the clustering results accurately represent the intrinsic relevance of traffic roads. Subsequently, frequent routes are identified from both macro- and micro-perspectives using the Steiner tree and Frequent Pattern Growth (FP Growth) algorithm, respectively. Validated on the LPR dataset in Changsha, China, the experiment results demonstrate that the proposed method can effectively identify travel patterns and extract frequent routes in the sparsely installed LPR devices. Full article

As typical environmental hormones, endocrine-disrupting chemicals (EDCs) have become a global environmental health issue of high concern due to their property of interfering with the endocrine systems of organisms. As a commonly used substitute for bisphenol A (BPA), bisphenol E (BPE) has been frequently detected in environmental matrices such as soil and water in recent years. Existing research has unveiled the developmental and reproductive toxicity of BPE; however, only one in vitro cellular experiment has preliminarily indicated potential neurotoxic risks, with its underlying mechanisms remaining largely unelucidated in the current literature. Potential toxic mechanisms and action targets of BPE were predicted using the zebrafish model via network toxicology and molecular docking, with RT-qPCRs being simultaneously applied to uncover neurotoxic effects and associated mechanisms of BPE. A significant decrease (p < 0.05) in the frequency of embryonic spontaneous movements was observed in zebrafish at exposure concentrations ≥ 0.01 mg/L. At 72 hpf and 144 hpf, the larval body length began to shorten significantly from 0.1 mg/L to 1 mg/L, respectively (p < 0.01), accompanied by a reduced neuronal fluorescence intensity and a shortened neural axon length (p < 0.01). By 144 hpf, the motor behavior in zebrafish larvae was inhibited. Through network toxicology and molecular docking, HSP90AB1 was identified as the core target, with the cGMP/PKG signaling pathway determined to be the primary route through which BPE induces neurotoxicity in zebrafish larvae. BPE induces neuronal apoptosis and disrupts neurodevelopment by inhibiting the cGMP/PKG signaling pathway, ultimately suppressing the larval motor behavior. To further validate the experimental outcomes, we measured the expression levels of genes associated with neurodevelopment (elavl3, mbp, gap43, syn2a), serotonergic synaptic signaling (5-ht1ar, 5-ht2ar), the cGMP/PKG pathway (nos3), and apoptosis (caspase-3, caspase-9). These results offer crucial theoretical underpinnings for evaluating the ecological risks of BPE and developing environmental management plans, as well as crucial evidence for a thorough comprehension of the toxic effects and mechanisms of BPE on neurodevelopment in zebrafish larvae. Full article

Urban tourism has evolved into a complex adaptive system, where unregulated expansion disrupts the ecological balance and intensifies resource stress. Understanding the dynamic evolution and resilience mechanisms of the tourism ecological health network (TEHN) is essential for supporting sustainable urban tourism as a coupled human–natural system. Using Shanghai as a case study, we applied the “vigor–organization–resilience–services” (VORS) framework to evaluate ecosystem health, which served as a constraint for constructing the TEHN, using the minimum cumulative resistance (MCR) model for the period from 2001 to 2023. A resilience framework integrating structural and functional dimensions was further developed to assess spatiotemporal evolution and guide targeted enhancement strategies. The results indicated that as ecosystem health degraded, particularly in peripheral areas, the urban TEHN in Shanghai shifted from a dispersed to a centralized structure, with limited connectivity in the periphery. The resilience of the TEHN continued to grow, with structural resilience remaining at a high level, while functional resilience still required enhancement. Specifically, the low integration and limited choice between the tourism network and the transportation system hindered tourists from selecting routes with higher ecosystem health indices. Enhancing functional resilience, while sustaining structural resilience, is essential for transforming the TEHN into a multi-centered, multi-level system that promotes efficient connectivity, ecological sustainability, and long-term adaptability. The results contribute to a systems-level understanding of tourism–ecology interactions and support the development of adaptive strategies for balancing network efficiency and environmental integrity. Full article

The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article

The implementation of the trans-European tourist cycle route network “EuroVelo” is fostering new strategic importance for non-motorized mobility and the associated practice of cycling tourism. Indeed, slow tourism offers a pathway for the development of inland areas. The infrastructure supporting it, such as long-distance cycling and walking paths, can act as a vital connection, stimulating regeneration in peripheral territories by enhancing environmental and landscape assets, as well as preserving heritage, local identity, and culture. The regeneration of peri-urban landscapes through soft mobility is recognized as the cornerstone for accessibility to material and immaterial resources (including ecosystem services) for multiple categories of users, including the most vulnerable, especially following the restoration of green-area systems and non-urbanized areas with degraded ecosystems. Considering the forthcoming implementation of the Magna Grecia cycling route, the southernmost segment of the “EuroVelo” network traversing three regions in southern Italy, this contribution briefly examines the necessity of defining new development policies to effectively integrate sustainable slow tourism with the enhancement of environmental and landscape values in the coastal areas along the route. Specifically, this case study focuses on a coastal stretch characterized by significant morphological and environmental features and notable landscapes interwoven with densely built environments. In this area, environmental and landscape values face considerable threats from scattered, irregular, low-density settlements, abandoned sites, and other inappropriate constructions along the coastline. Full article

Artificial intelligence (AI) has emerged as a significant driving force in the development of technology and industry. It is also integrated with music as music AI in music generation and analysis. It originated from early algorithmic composition techniques in the mid-20th century. Recent advancements in machine learning and neural networks have enabled innovative music generation and exploration. This article surveys the development history and technical route of music AI, analyzes the current status and limitations of music artificial intelligence across various areas, including music generation and composition, rehabilitation and treatment, as well as education and learning. It reveals that music AI has become a promising creator in the field of music generation. The influence of music AI on the music industry and the challenges it encounters are explored. Additionally, an emotional music generation system driven by multimodal signals is proposed. Although music artificial intelligence technology still needs to be further improved, with the continuous breakthroughs in technology, it will have a more profound impact on all areas of music. Full article

This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios. Full article