一种结合机器学习的移动边缘计算的切换预测方法

随着移动通信的发展,减少通信延时成为关键性问题,因此,提出了一种使用机器学习方法的移动边缘计算(MEC)移动性管理。移动性决策基于参考信号接收功率(RSRP)值和不确定性预测器。使用神经网络建立预测器,输出不同相邻单元的RSRP平均值和标准偏差,推导了切换概率的封闭表达式。基于这些可能性,MEC服务器能够提前缓存用户服务,以便将切换期间的中断降至最低。实验结果表明,提出的方法能够满足实际需求。     

A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

With the rapid development of mobile technology and smart devices, crowdsensing has shown its large potential to collect massive data. Considering the limitation of calculation power, edge computing is introduced to release unnecessary data transmission. In edge-computing-enabled crowdsensing, massive data is required to be preliminary processed by edge computing devices (ECDs). Compared with the traditional central platform, these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically. ECDs involved in one task are required to cooperate to process the task data. The privacy of participants is important in crowdsensing, so blockchain is used due to its decentralization and tamper-resistance. In crowdsensing tasks, it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced. As mentioned before, ECDs can’t process task data comprehensively and they are required to cooperate quality assessment. Therefore, a blockchain-based framework for data quality in edge-computing-enabled crowdsensing (BFEC) is proposed in this paper. DPoR (Delegated Proof of Reputation), which is proposed in our previous work, is improved to be suitable in BFEC. Iteratively, the final result is calculated without revealing the privacy of participants. Experiments on the open datasets Adult, Blog, and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.     

Bragg Edge Neutron Transmission Strain Tomography in Granular Systems

The advent of pixelated detectors for time‐of‐flight neutron transmission experiments has raised significant interest in terms of the potential for tomographic reconstructions of triaxial strain distributions. A recent publication by Lionheart and Withers [WRB Lionheart and PJ Withers, “Diffraction tomography of strain”, Inverse Problems, v31:045005, 2015] has demonstrated that reconstruction is not possible in the general sense; however, various special cases may exist. In this paper, we outline a process by which it is possible to tomographically reconstruct average triaxial elastic strains within individual particles in a granular assembly from a series of Bragg edge strain measurements. This algorithm is tested on simulated data in two and three dimensions and is shown to be capable of rejecting Gaussian measurement noise. Sources of systematic error that may present problems in an experimental implementation are briefly discussed.     

Nucleation Crystallography of Ni Grains on CrFeNb Inoculants Investigated by Edge‐to‐Edge Matching Model in an IN718 Superalloy

In this work, the nucleation crystallography of CrFeNb intermetallic particles as a grain refiner for Ni‐based IN718 superalloys is studied using Edge‐to‐Edge Matching model. Three distinguishable orientation relationships between CrFeNb intermetallic particles and Ni grains are well predicted: , (111)_Ni 1.28° from (0004)_CrFeNb, , (111)_Ni 1.32° from _CrFeNb, and _Ni//[0001]_CrFeNb, (111)_Ni 0.72° from _CrFeNb. The results indicate that CrFeNb intermetallic particles have a strong nucleation potency as an effective grain refiner for Ni‐based superalloy and the existence of semi‐coherent interfaces between the CrFeNb intermetallic particles and Ni grains. Furthermore, the IN718 superalloy is used to experimentally validate the grain refinement effect of CrFeNb intermetallic particles, showing that its grain size is obviously refined from 8.60 to 1.23 mm. And, the corresponding heterogeneous nucleation mechanism of grain refinement at the atomic level is further identified.

     

Multi-Zone-Wise Blockchain Based Intrusion Detection and Prevention System for IoT Environment

Blockchain merges technology with the Internet of Things (IoT) for addressing security and privacy-related issues. However, conventional blockchain suffers from scalability issues due to its linear structure, which increases the storage overhead, and Intrusion detection performed was limited with attack severity, leading to performance degradation. To overcome these issues, we proposed MZWB (Multi-Zone-Wise Blockchain) model. Initially, all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm (EBA), considering several metrics. Then, the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph (B-DAG), which considers several metrics. The intrusion detection is performed based on two tiers. In the first tier, a Deep Convolution Neural Network (DCNN) analyzes the data packets by extracting packet flow features to classify the packets as normal, malicious, and suspicious. In the second tier, the suspicious packets are classified as normal or malicious using the Generative Adversarial Network (GAN). Finally, intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization (IMO) is used for attack path discovery by considering several metrics, and the Graph cut utilized algorithm for attack scenario reconstruction (ASR). UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator (NS-3.26). Compared with previous performance metrics such as energy consumption, storage overhead accuracy, response time, attack detection rate, precision, recall, and F-measure. The simulation result shows that the proposed MZWB method achieves high performance than existing works     

Container-Based Internet of Vehicles Edge Application Migration Mechanism

Internet of Vehicles (IoV) applications integrating with edge computing will significantly drive the growth of IoV. However, the contradiction between the high-speed mobility of vehicles, the delay sensitivity of corresponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications. IoV application migration is a promising solution that can be supported by application containerization, a technology forseamless cross-edge-server application migration without user perception. Therefore, this paper proposes the container-based IoV edge application migration mechanism, consisting of three parts. The first is the migration trigger determination algorithm for cross-border migration and service degradation migration, respectively, based on trajectory prediction and traffic awareness to improve the determination accuracy. The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions. The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions. Simulation results show that the proposed migration mechanism can reduce migration times, reduce average migration time, improve average service time and enhance the stability and adaptability of IoV application services.     

基于二阶梯度的图像增强算法研究

针对PCB图像的特点对图像进行增强处理,使得图像的边缘更加突出,灰度的对比度增加,同时滤除靠近边缘的噪声,为后续图像分割时更有效的选取阈值打下良好基础。使用二阶梯度算子获得图像的增强效果,实验表明,不仅解决了边缘附近噪声去除问题,而且有效地突出了边缘信息,图像的黑白对比分明,尤其是目标图像靠近边缘部分灰度值明显高于其他的非边缘区域。     

基于BBV的有向加权网络模型

在BBV模型基础上,结合方向性和网络演化特性,提出一种有向加权网络模型。引入参数p、q,将节点强度分为入强度和出强度,根据BBV建模思想进行模型择优和演化。理论分析和数值模拟仿真结果表明,该模型的节点出入强度和出入度分布满足幂律指数为[2,3]的幂律分布,且通过调节参数可使平均路径和聚簇系数符合复杂网络特性。     

基于相邻尺度小波变换乘积的镜头渐变检测

镜头边界检测是进行视频内容分析的首要步骤。提出一种基于相邻尺度小波变换乘积的镜头边缘检测方法。对镜头内的每帧视频图像进行小波变换,求出各帧图像相邻尺度小波变换尺度积的模和方向。计算连续帧图像尺度积与模之间的绝对距离,得到2帧图像间的不连续值。通过自适应阈值分割,检测出镜头边界。     

The bipartite edge frustration of graphs under subdivided edges and their related sums

The bipartite edge frustration of a graph G, denoted by φ(G), is the smallest number of edges that have to be deleted from G to obtain a bipartite spanning subgraph of G. This topological index is related to the well-known Max-cut problem, and has important applications in computing stability of fullerenes. In this paper, the bipartite edge frustration of an infinite family of fullerenes is computed. Moreover, this quantity for four classes of graphs arising from a given graph under different types of edge subdivisions is investigated.