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A Service Placement Algorithm Based on Merkle Tree in MEC Systems Assisted by Digital Twin networks

A Service Placement Algorithm Based on Merkle Tree in MEC Sy...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Chengan Dai Kun Yang Chunjian Deng School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

This paper considers a digital twin network (DTN) assisted mobile edge computing (MEC) system. When wireless device (WD) users request services from MECs, we assume in the system there are a cloud server, storing all of service entities, and a lightweight digital twin network, providing the digital replicas of state information of all MECs instead of service entities. We aim to maximize the number of service requests served by the MECs, or, equivalently, to minimize the load of the cloud. This is formulated as a mixed-integer non-convex optimization problem. We propose a service placement algorithm based on hash-based data structure called Merkle tree to solve the problem. The introduction of candidate mode pruning effectively reduces the time complexity of the algorithm in iterations. Simulation results show that our proposed method has a better performance compared with the other benchmarks.

关键词： Wireless communication Multi-access edge computing Simulation Heuristic algorithms Benchmark testing Ubiquitous computing Digital twins

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CopGAT: Co-propagation Self-supervised Graph Attention Network

CopGAT: Co-propagation Self-supervised Graph Attention Netwo...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Baoming Zhang Ming Xu Mingcai Chen Mingyuan Chen Chongjun Wang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Graph Attention Network (GAT) is one of the state-of-the-art architectures for Graph Neural networks (GNNs). In this paper, we first propose Label Purity to explore the relationship between the graph attention and the node labels. By tracking the label purity of graph attention, we observe that graph attention suppresses message passing between inter-class nodes in the graph with homophily. Upon such basis, we further improve graph attention's capability to capture label information by designing a self-supervised graph attention loss. We propose a novel approach based on self-supervised graph attention loss, Co-propagation Self-supervised Graph Attention Network (CopGAT), which explicitly restrains message passing between inter-class nodes. The Co-propagation means propagating node representations in Euclidean space and pseudo labels in probability simplex via the shared attention matrix, combining Message Passing Neural networks and Label Propagation Algorithm under the graph attention framework. Constrained with the self-supervised graph attention loss generated by co-propagation, our CopGAT enhances the ability of GAT to attenuate the influence of structural noise. Furthermore, our self-supervised graph attention loss can be extended to other graph attention models in a plug-and-play manner. Experimental results demonstrate that our Cop GAT outperforms previous state-of-the-art methods on five benchmark datasets for the semi-supervised node classification task.

关键词： Message passing Benchmark testing Graph neural networks Task analysis

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Various Approaches and Algorithms for Monitoring Energy Efficiency of Wireless Sensor networks

Various Approaches and Algorithms for Monitoring Energy Effi...

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international conference on Sustainable Development through Engineering Innovations, SDEI 2020

作者： Rachna Chhabra, Yogesh Bhambri, Pankaj CT University Ferozepur Road Ludhiana Punjab India Guru Nanak Dev Engineering College Ludhiana Punjab India

ISBN: (纸本)9789811595530

Wireless sensor network is an operative experimentation region in which various challenging issues related to energy exhaustion, routing protocols, techniques, approaches, algorithms, efficiency, robustness, productivity, and saving are essential to be considered. This article discusses most recent approaches and techniques for the wireless sensor networks with sustainability development. It reviews the numerous research works in different clustering approaches and algorithms along with parallel and distributed computing. The investigation is concentrated on whether one or more sinks are occupied;devices are rigid or not rigid, etc. It also analyzes the comparison among different clustering algorithms with different proprieties like clustering size, merits, clustering types, CH parameter selection, connectivity, device (nodes) capabilities, and device deployments. Consequently, the poor energy delivery is the major restraint to sustain the lengthy term and competent process of the system. As a result, falling energy utilization and increasing energy competence are the main requirements for planning a sustainable wireless sensor network, so that network life could improve. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Wireless sensor networks

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A Comparative Study on Fault Detection Using Twin Support Vector Machines for Wireless Sensor networks 5th

A Comparative Study on Fault Detection Using Twin Support Ve...

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5th international conference on Microelectronics, computing and Communication Systems, MCCS 2020

作者： Singh, Jyotsana Raghuvanshi, Ajay Singh Shukla, Namrita Gavel, Shashank Department of Electronics and Communication National Institute of Technology Raipur Raipur492010 India

ISBN: (纸本)9789811602740

Wireless Sensor networks (WSNs) are composed of distributed small sensors that are deployed in various environmental conditions to collect data such as temperature, moisture, light speed, etc. These sensors are subjected to various damages, noises and low battery life as a result of which sensors account for abnormal data readings. These anomalies must be detected for the reliable and efficient working of a WSN. This study presents a comparative study for fault detection using Twin Support Vector Machines (TSVM) by utilizing different kernel functions. TSVM uses two non-parallel hyperplanes to classify data and is insensitive to class size imbalance. The choice of kernels significantly influences the performance of TSVM. The proposed work is estimated using different standard WSN datasets by inserting various types of fault. The performance of the work is measured using standard performance indexes. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Proceedings - 2020 IEEE international Symposium on parallel and distributed Processing with Applications, 2020 IEEE international conference on Big Data and Cloud computing, 2020 IEEE international Symposium on Social computing and Networking and 2020 IEEE international conference on Sustainable computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

Proceedings - 2020 IEEE International Symposium on Parallel ...

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18th IEEE international Symposium on parallel and distributed Processing with Applications, 10th IEEE international conference on Big Data and Cloud computing, 13th IEEE international Symposium on Social computing and Networking and 10th IEEE international conference on Sustainable computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

ISBN: (纸本)9781665414852

The proceedings contain 202 papers. The topics discussed include: multi-relational graph convolutional networks for skeleton-based action recognition;identifying the right person in social networks with double metaphone codes;a novel strong cache consistency mechanism in ICN based on role division and lease model;demystifying the largest live game streaming platform via black-box measurement;framework-agnostic optimization of repeated skewed joins at massive scale;remote procedure call optimization of big data systems based on data awareness;MCSEC: secure coded matrix multiplication scheme for edge computing with minimum communication cost;low-rate DoS attack detection based on WPD-EE algorithm;user identity linkage across location-based social networks with spatio-temporal check-in patterns;optimizing 3-D placement of multiple UAVs based on Taguchiâ€™s method;and feature envy detection based on Bi-LSTM with self-attention mechanism.

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distributed Graph Analytics 16th

Distributed Graph Analytics

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16th international conference on distributed computing and Internet Technology (ICDCIT)

作者： Srikant, Y. N. Indian Inst Sci Bengaluru 560012 India

ISBN: (纸本)9783030369873;9783030369866

Graph Analytics is important in different domains: social networks, computer networks, and computational biology to name a few. This paper describes the challenges involved in programming the underlying graph algorithms for graph analytics for distributed systems with CPU, GPU, and multi-GPU machines and how to deal with them. It emphasizes how language abstractions and good compilation can ease programming graph analytics on such platforms without sacrificing implementation efficiency.

关键词： Graph analytics Social networks parallel algorithms Multi-core processors GPU computation Graph frameworks Graph processing languages

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Artificial Intelligence And Machine Learning

Artificial Intelligence And Machine Learning

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2022 IEEE Future networks World Forum, FNWF 2022

作者： Kataria, Deepak Walid, Anwar Daneshmand, Mahmoud Dutta, Ashutosh Enright, Michael A. Gu, Rentao Lackpour, Alex Ramachandran, Prakash Wang, Honggang Chen, Chi-Ming Chng, Baw Darema, Frederica Ip Junction United States Nokia Bell Labs United States Stevens Institute of Technology United States Johns Hopkins University Applied Physics Lab United States Quantum Dimension Inc. United States Bupt China Drexel University United States Dell Technologies United States EOTF India UMass Dartmouth United States United States Bawman Llc United States Info Symbiotic Systems Society United States

ISBN: (纸本)9781665462501

In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives. © 2022 IEEE.

关键词： Cloud computing

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WCES 2021 - Proceedings of the Workshop on Control and Embedded Systems, Co-located with EASTON 100 - 5th international conference on computing Sciences, ICCS 2021

WCES 2021 - Proceedings of the Workshop on Control and Embed...

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2021 Workshop on Control and Embedded Systems, WCES 2021

The proceedings contain 12 papers. The topics discussed include: performance evaluation of distributed networks for Internet of things;connectivity pattern analysis for virtual simulation design, based on high-performance game analysis;computational study for improvement of aerodynamic performance of airfoil by changing various aerodynamic properties;an experimental design approach to IoT enabled smart parallel irrigation system using embedded microcontrollers;to develop, test and record a 3 lead EMG electrode and flex sensor on a 3D prosthetic limb with different gait patterns using Arduino microcontroller;and low-cost system for the management of hospital services, applied to hospitalized patients through the use of IoT technology.

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DIPool: Degree-Induced Pooling for Hierarchical Graph Representation Learning

DIPool: Degree-Induced Pooling for Hierarchical Graph Repres...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Hualei Yu Yirong Yao Jinliang Yuan Chongjun Wang Department of Computer Science and Technology Nanjing University Nanjing China

Graph Neural networks have been popularly investigated and achieved promising performance in node-wise and graph-level tasks, like node classification and graph classification. For graph classification, correctly defining pooling operations to coarsen an input graph for distilling hierarchical knowledge from node representations is demanded. Some works have devised pooling strategies to summarize a graph by evaluating and gathering the ranked top-k representative nodes. Yet there is few literature to explicitly consider the node's degree-specific structure information in pooling procedure. One intuition is the larger the node's degree is, the more important it probably is in the entire graph. In this work, we propose a novel Degree-Induced Pooling (DIPool), which can be integrated into various graph neural network architectures. Specifically, the DIPool layer devises two approaches for explicitly incorporating the characteristics and degree-specific structures of nodes into a unified criterion to evaluate the importance of the nodes within an input graph. This setting permits to retain more representative nodes to generate the newly pooled graph. Experimental results on a collection of public benchmarks have demonstrated the effectiveness of the proposed DIPool operator. Especially, DIPool achieves relative improvements of 3.69% on NCI1 and 3.24% on NCI109 compared with the best baseline ASAP.

关键词： Representation learning Convolution Biological system modeling Aggregates Predictive models Benchmark testing Graph neural networks

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License plate recognition system based on image recognition

License plate recognition system based on image recognition

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Liang Wang Yimei Huanga Chengqun Liang Jinrong Zhou Taoqiang Zhu Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

As people’s living standards rise, efficient and intelligent traffic management system has become the demand of the society. Although there are many researches on vehicle license plate recognition technology in China, there are still many shortcomings in practical application. To solve the above problems, this paper uses OpenCV machine vision library based on Python development platform and convolutional neural network to study the process of license plate recognition, and proposes a vehicle license plate recognition system based on machine vision technology and pattern recognition method. System using digital image correlation algorithm to detect and locate license plate, based on the analysis to extract the edge of the information, character segmentation and character quantity calculation, at last, we use Convolutional Neural network (CNN) for character recognition and classification in order to obtain accurate and clear plates in formation. At last, we use convolution neural network (CNN) for character recognition and classification in order to obtain accurate and clear plates information, its accuracy is as high as 98%, has the good recognition effect.

关键词： Image segmentation Image color analysis Image edge detection Neural networks Object segmentation Feature extraction Convolutional neural networks

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