Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a ...popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.
Due to the random delay, local maximum and data congestion in vehicular networks, the design of a routing is really a challenging task especially in the urban environment. In this paper, a ...distributed routing protocol DGGR is proposed, which comprehensively takes into account sparse and dense environments to make routing decisions. As the guidance of routing selection, a road weight evaluation (RWE) algorithm is presented to assess road segments, the novelty of which lies that each road segment is assigned a weight based on two built delay models via exploiting the real-time link property when connected or historic traffic information when disconnected. With the RWE algorithm, the determined routing path can greatly alleviate the risk of local maximum and data congestion. Specially, in view of the large size of a modern city, the road map is divided into a series of Grid Zones (GZs). Based on the position of the destination, the packets can be forwarded among different GZs instead of the whole city map to reduce the computation complexity, where the best path with the lowest delay within each GZ is determined. The backbone link consisting of a series of selected backbone nodes at intersections and within road segments, is built for data forwarding along the determined path, which can further avoid the MAC contentions. Extensive simulations reveal that compared with some classic routing protocols, DGGR performs best in terms of average transmission delay and packet delivery ratio by varying the packet generating speed and density.
In this study, tsunami generation and propagation model involved trans-Pacific (China’s offshore) -local- coastal reef has been established, based on Okada model, nonlinear shallow water equations, ...and coupled grid with high resolution. The Phoenix Island in Sanya City is selected as research subject. Firstly, the simulation of 2011 Japan Tohoku tsunami has been carried out, and the characteristics of tsunami propagation along the continental shelf of Chinese coasts and the impacts on Phoenix Island are presented, combined with real-time measurements. The impacts of tsunami source along Manila Trench, Ryukyu Trench, and 21 extreme sources around Pacific on Phoenix Island are discussed. According to characteristics of tsunami wave near the Phoenix Island, the amplification effect of tsunami wave near coastal reef is discussed based on Fouriers analysis. It turns out that relatively moderate tsunami near Chinese coasts and extreme trans-Pacific tsunami will have some impacts on Phoenix Island, which may induce
The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance ...compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.
随着物联网(IoT)行业的快速发展, 无线传感器网络(WSN)融合云计算技术面临着任务处理时延高、传感器节点能量有限的挑战。因此, 提出了一种基于云雾网络架构的路径计算方法, 利用雾计算层的网络边缘设备计算资源, 将WSN监测任务合理地部署到指定边缘设备上完成处理, 以减少能耗制约下的任务处理时延。为了将任务有效地分配到雾计算层, 采用了一种任务映射规则, ...将有向无环图表示的监测任务映射到无向图表示的雾计算层网络; 结合时延和能耗约束建立了一个关于寻求最优映射关系的二值优化问题; 采用模拟退火-离散二值粒子群优化(SA-BPSO)算法实现了对该优化问题的求解。仿真结果显示, 在数据量为10 Mb时, 该方法的时延性能相比较WSN融合云计算技术提高了约40%。
With the rapid development of the Internet of Things (IoT) industry, wireless sensor network (WSN) fusion cloud computing technology is encountering the challenges of high task processing latency and limited sensor node energy. Therefore, a path calculation method based on cloud computing network architecture is proposed. WSN monitoring tasks are deployed to specific edge devices reasonably by using the computing resources of network edge devices in the fog computing layer to reduce the task processing latency under the constraints of energy consumption. In order to efficiently assign tasks to the fog computing layer, a task mapping rule is used to map the monitoring tasks represen
Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the ...electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.
The eruption and collapse of the Anak Krakatau volcano generated tsunamis in the Sunda Strait on December 22, 2018, leading to damage and casualties. In this paper, we use the two-layer model and ...nonlinear shallow equation model to study the triggering mechanism of the tsunami event. We first simulate the tsunami generated by volcano eruption and landslide, respectively. The tsunami source is analyzed by comparing with gauge measurements. It indicates that the volume of partial collapse for the landslide is 0.2–0.3 km
3
. The comparison between the numerical results of landslide and tide gauge measurements presents well-fitted results, especially for the leading tsunami waves and arrival time. Computed maximum tsunami amplitude distribution points out that the most hazardous area is located at the south of the Sunda Strait (Pandeglang), which suffered the most casualties.
Due to the limitations of textual datasets, there are currently few studies on character inpainting. Second, unlike image inpainting, character inpainting cannot replicate the results of image ...inpainting, and some methods are even inaccessible. Finally, the extraction of character features in the existing model is insufficient, resulting in the model being unable to reconstruct characters based on complete and accurate character features. In this paper, we first threshold the obtained inscription dataset to obtain a visually better-binarized inscription dataset. Second, we improve the Context encoders to design BCEs(Binary Context Encoders) and add dilated convolution to learn the structural features of the character. It has been experimentally proven that BCEs is not only slightly better than methods in the same field, but can also restore real-life inscription characters with missing strokes.
ABSTRACT We present a multiwavelength observational study of the NGC 2024 filament using infrared to submillimeter continuum and the and inversion transitions centered on FIR-3, the most massive core ...therein. FIR-3 is found to have no significant infrared point sources in the Spitzer/IRAC bands. But the kinetic temperature map shows a peak value at the core center with K, which is significantly higher than the surrounding level ( 15-19 K). Such internal heating signature without an infrared source suggests an ongoing core collapse possibly at a transition stage from first hydrostatic core (FHSC) to protostar. The eight dense cores in the filament have dust temperatures between 17.5 and 22 K. They are much cooler than the hot ridge ( K) around the central heating star IRS-2b. Comparison with a dust heating model suggests that the filament should have a distance of 3-5 pc from IRS-2b. This value is much larger than the spatial extent of the hot ridge, suggesting that the filament is spatially separated from the hot region along the line of sight.
Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering ...potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.
PDF
