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Adaptive Task Offloading in Coded Edge Computing: A Deep Reinforcement Learning Approach

IEEE COMMUNICATIONS LETTERS 2021年第12期25卷 3878-3882页

作者： Nguyen Van Tam Nguyen Quang Hieu Nguyen Thi Thanh Van Nguyen Cong Luong Niyato, Dusit Kim, Dong In Phenikaa Univ Fac Comp Sci Hanoi 12116 Vietnam A&A Phoenix Grp JSC Phenikaa Res & Technol Inst PRATT Hanoi 11313 Vietnam Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Phenikaa Univ Fac Elect & Elect Engn Hanoi 12116 Vietnam Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea

In this letter, we consider a Coded Edge Computing (CEC) network in which a client encodes its computation subtasks using the Maximum Distance Separable (MDS) code before offloading them to helpers. The CEC network is heterogeneous in which the helpers are different in computing capacity, wireless communication stability, and computing price. Thus, the client needs to determine a desirable size of MDS-coded subtasks and selects proper helpers such that the computation latency is within the deadline and the incentive cost is minimal. This problem is challenging since the helpers are generally dynamic and random in the computing, communication, and computing price. We thus propose to adopt a Deep Reinforcement Learning (DRL) algorithm that allows the client to learn and find optimal decisions without any prior knowledge of network environments. The experiment results reveal that the proposed algorithm outperforms the standard Q-learning and baseline algorithms in both terms of computation latency and incentive cost.

关键词： Task analysis Costs Reinforcement learning Codes Edge computing partitioning algorithms Optimization Maximum distance separable code coded edge computing deep reinforcement learning

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Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第11期59卷 9504-9517页

作者： Qu, Jiahui Du, Qian Li, Yunsong Tian, Long Xia, Haoming Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Network Xian 710071 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA Henan Univ Minist Educ Key Lab Geospatial Technol Middle & Lower Yellow Kaifeng 475004 Peoples R China

Hyperspectral images (HSIs) with rich spectral information have been widely used in many fields. Anomaly detection is one of the most interesting and important applications. In this article, a novel Gaussian mixture model (GMM)-based anomaly detection (GMMD) method for HSI is proposed. The main contributions of this article are a new GMM-based extraction approach for extracting the anomaly pixels and an effective GMM-based weighting approach for fusing the extracted anomaly results. Specifically, based on the fact that the spectral values of anomaly pixels in some bands are different from those of background pixels, we propose a GMM-based anomaly extraction approach in which the HSI is characterized by the GMM and the anomaly pixels are extracted by a range prescribed by the GMM parameters. In order to fuse the extracted anomaly results, the GMM-based weighting method is introduced to adaptively construct the detection map. The detection map is rectified by using a guided filter to obtain the final anomaly detection map. Experimental results conducted on four hyperspectral data sets demonstrate the superior performance of the proposed GMMD method.

关键词： Anomaly detection Hyperspectral imaging Gaussian distribution Correlation partitioning algorithms Gaussian mixture model Rivers Anomaly detection anomaly extraction band fusion Gaussian mixture model (GMM) hyperspectral image (HSI)

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Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis

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IEEE ACCESS 2021年 9卷 35573-35583页

作者： Zheng, Bo Gao, Huiying Ma, Xin Zhang, Xiaoqiang Civil Aviat Flight Univ China Aviat Engn Inst Guanghan 618307 Peoples R China Civil Aviat Flight Univ China Air Traff Management Sch Guanghan 618307 Peoples R China

In order to improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the dimensionless similarity method is proposed to overcome the influence of attributes with different dimensions. So, the algorithm is named graph partition based on dimensionless similarity (GPDS). Moreover, similarity threshold determination method based on frequency distribution histogram is proposed to reduce the dependency on the experiences of experts. Finally, different characteristic data are applied to verify the theories. The experimental results indicate that the compressed training samples can maintain the classification accuracy. Furthermore, the elapsed time can be obviously reduced. Therefore, the GPDS method can be used in fault diagnosis properly.

关键词： Fault diagnosis Euclidean distance Computational efficiency partitioning algorithms Monitoring Licenses Histograms Granular computing graph partition dimensionless similarity data mining fault diagnosis

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A Fast Neighborhood Grouping Method for Hyperspectral Band Selection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第6期59卷 5028-5039页

作者： Wang, Qi Li, Qiang Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, so the richness of information has been greatly improved. However, these bands lead to increasing complexity of data processing, and the redundancy of adjacent bands is large. Recently, although many band selection methods have been proposed, this task is rarely handled through the context information of the whole spectral bands. Moreover, the scholars mainly focus on the different numbers of selected bands to explain the influence by accuracy measures, neglecting how many bands to choose is appropriate. To tackle these issues, we propose a fast neighborhood grouping method for hyperspectral band selection (FNGBS). The hyperspectral image cube in space is partitioned into several groups using coarse-fine strategy. By doing so, it effectively mines the context information in a large spectrum range. Compared with most algorithms, the proposed method can obtain the most relevant and informative bands simultaneously as subset in accordance with two factors, such as local density and information entropy. In addition, our method can also automatically determine the minimum number of recommended bands by determinantal point process. Extensive experimental results on benchmark data sets demonstrate the proposed FNGBS achieves satisfactory performance against state-of-the-art algorithms.

关键词： Hyperspectral imaging Clustering algorithms partitioning algorithms Information entropy Complexity theory Task analysis Band selection context information determinantal point process (DPP) hyperspectral image neighborhood grouping

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Operators for Data Redistribution: Applications to the STL Library and RayTracing Algorithm

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IEEE ACCESS 2021年 9卷 38557-38570页

作者： Moreton-Fernandez, Ana De La Sierra, Yuri Torres Gonzalez-Escribano, Arturo Llanos, Diego R. Satlantis Univ Sci Pk Bilbao 48940 Spain Univ Valladolid Dept Informat ETS Ingn Informa Campus Miguel Delibes S-N Valladolid 47011 Spain

In distributed-memory systems, data redistributions are operations that change the ownership and location of a selected subset of a data structure at runtime. They allow the improvement of the performance of parallel algorithms which operate on changing or partial domains, aiming to create a balanced workload among the active processes. To manually redistribute data is a cumbersome and error-prone task. In this paper, we present a method based on four combinable operators to redistribute partial domains selected by the programmer at runtime in an efficient and simple way. They abstract to the programmer the data-redistribution implementation details, such as the new mapping, relocation, and communication of the selected data. We also present the application of the proposed operators to a RayTracing application and to a significant part of STL (C++ Standard Template Library). Our experimental results show that our approach automatically generates a good load balance, which leads to performance improvements for generic data-distribution policies. It does not introduce significant performance overheads compared with tailored data redistributions directly programmed using MPI (Message Passing Interface), while it greatly reduces the code development effort.

关键词： Libraries Arrays Task analysis partitioning algorithms Distributed databases Runtime Data models Parallel programming distributed memory data redistributions RayTracing STL library

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RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

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IEEE ACCESS 2021年 9卷 125561-125568页

作者： Yang, Kuo Liang, Zhonghua Liu, Ren Li, Wei Changan Univ Sch Informat Engn Dept Commun Engn Xian 710064 Peoples R China

Min-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended Min-Max (E-Min-Max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved Min-Max algorithm with area partition strategy (Min-Max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is "closest" to the target node. Then the target node's location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed Min-Max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-Min-Max algorithm, and is also less than one-seventh of that of the original Min-Max algorithm. Moreover, for the proposed Min-Max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-Min-Max and 1.12 meters for the original Min-Max, respectively.

关键词： Location awareness partitioning algorithms Meters Estimation Area measurement Noise measurement Licenses Min-Max algorithm received signal strength (RSS) area partition indoor localization target node (TN)

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Two-Stage Optimization Strategy for Solving the VVO Problem Considering High Penetration of Plug-In Electric Vehicles to Unbalanced Distribution Networks

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第4期57卷 3425-3440页

作者： Aljohani, Tawfiq Masad Saad, Ahmed Mohammed, Osama A. Florida Int Univ Elect & Comp Engn Dept Miami FL 33199 USA

This article proposes a two-stage optimization strategy for solving the voltage-var optimization (VVO) problem considering stochastic penetration of plug-in electric vehicles (PEVs) to unbalanced, three-phase power distribution networks (PDN). The optimization strategy considers the prospect of forced active power curtailment at a minimized level, bidirectional PEVs activities. It also includes simple tap operations of shunt capacitor banks, online tap changing transformers, and voltage regulators to achieve optimal economic gain while satisfying the VVO operational constraints. The first stage aims for the optimal decomposition of the PDN into well-defined, cross-checked smaller partitions via a proposed community-based detection particle swarm optimization algorithm. The second stage incorporates a mixed-integer linear programming formulation to solve the VVO problem per partition level. This is done considering the nonlinearity and nonconvexity of the electrical system via applied approximation. This reduces the complexity and computational burdens that usually arise in solving the problem on a larger scale. The proposed two-stage strategy was tested on the modified IEEE 123 bus system with various case scenarios. Economical operation of the PDN is achieved during peak demand hours while maintaining a safe operation within the VVO problem.

关键词： Optimization Voltage control Reactive power Partial discharges Indexes partitioning algorithms Particle swarm optimization Community-based detection mixed-integer linear programming (MILP) particle swarm optimization (PSO) plug-in electric vehicle (PEV) integration voltage-var optimization (VVO)

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Accelerating Large-Scale Prioritized Graph Computations by Hotness Balanced Partition

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第4期32卷 746-759页

作者： Gong, Shufeng Zhang, Yanfeng Yu, Ge Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Prioritized computation is shown promising performance for a large class of graph algorithms. It prioritizes the execution of some vertices that play important roles in determining convergence. For large-scale distributed graph processing, graph partitioning is an important preprocessing step that aims to balance workload and to reduce communication costs between workers. However, existing graph partitioning methods are designed for round-robin synchronous distributed frameworks. They balance workload without distinction of vertex importance and fail to consider the characteristics of priority-based scheduling, which may limit the benefit of prioritized graph computation. In this article, to accelerate prioritized iterative graph computations, we propose Hotness Balanced Partition (HBP). In prioritized graph computation, high priority vertices are likely to be executed more frequently and are likely to pass more messages, which result in hot vertices. Based on this observation, we partition graph by distributing vertices with distinction according to their hotness rather than blindly distributing vertices with equal weights, aiming to evenly distribute the hot vertices among workers. We further provide two HBP algorithms: a streaming-based algorithm for efficient one-pass processing and a distributed algorithm for distributed processing. Our results show that our proposed partitioning methods outperform the state-of-the-art partitioning methods, Fennel, HotGraph, and SNE.

关键词： Processor scheduling partitioning algorithms Runtime Scheduling Computational modeling Google Optimal scheduling Hotness balanced partition graph partitioning distributed computing

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Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning

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IEEE ACCESS 2021年 9卷 44770-44783页

作者： Wang, Zhenfei Wang, Hongju Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

Imbalanced learning is a common problem in data mining. There is a different distribution of data samples among other classes in the imbalanced datasets. It's a challenge for standard algorithms designed for balanced class distributions. Although there are various strategies to solve this problem, generating artificial data to achieve a relatively balanced class distribution is universal rather than directly modifying specific classification algorithms. The oversampled data can be combined with any user-specified algorithm without any restrictions. In this paper, we present a novel oversampling method, Global Data Distribution Weighted Synthetic Oversampling Technique (GDDSYN). By applying clustering, optimizing the selection criteria of the minority class samples that are used to generate synthetic samples, avoiding generating more noise samples. GDDSYN assigns weights for the number of synthetic samples to tackle the within-class imbalance and between-class imbalance simultaneously, according to the informative level of the sample and the sparsity of the cluster to which the sample belongs. The use of scores with Silhouette Coefficient and Mutual Information helps the k-means algorithm set a reasonable number of clusters for the minority and majority classes respectively so that the clustering effect can be guaranteed. Next, by using clustering information, synthetic samples' generation path is improved to avoid class overlap. Additionally, GDDSYN has been evaluated extensively on 10 artificial and 10 real-world data sets. The empirical results show that our method is outperforms or comparable with some other existing methods in terms of assessment metrics when artificial data generated by GDDSYN are used.

关键词： Clustering algorithms Classification algorithms partitioning algorithms Licenses Standards Gallium nitride Training Imbalanced learning synthetic sample generation oversampling clustering classification within-class imbalance

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Scalable Clustering algorithms for Big Data: A Review

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IEEE ACCESS 2021年 9卷 80015-80027页

作者： Mahdi, Mahmoud A. Hosny, Khalid M. Elhenawy, Ibrahim Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt

Clustering algorithms have become one of the most critical research areas in multiple domains, especially data mining. However, with the massive growth of big data applications in the cloud world, these applications face many challenges and difficulties. Since Big Data refers to an enormous amount of data, most traditional clustering algorithms come with high computational costs. Hence, the research question is how to handle this volume of data and get accurate results at a critical time. Despite ongoing research work to develop different algorithms to facilitate complex clustering processes, there are still many difficulties that arise while dealing with a large volume of data. In this paper, we review the most relevant clustering algorithms in a categorized manner, provide a comparison of clustering methods for large-scale data and explain the overall challenges based on clustering type. The key idea of the paper is to highlight the main advantages and disadvantages of clustering algorithms for dealing with big data in a scalable approach behind the different other features.

关键词： Clustering algorithms Big Data Scalability partitioning algorithms Data mining Licenses Classification algorithms Clustering unsupervised learning traditional clustering parallel clustering stream clustering high dimensional data big data large-scale

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