2021 International Conference on Information Networking (ICOIN)最新文献_第10页

Recycling of Adversarial Attacks on the DNN of Autonomous Cars 自动驾驶汽车DNN对抗性攻击的回收

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333975

Hyunjun Mun, Seonggwan Seo, J. Yun

引用次数: 2

Context-aware Model Selection for On-Device Object Detection 设备上对象检测的上下文感知模型选择

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333918

Seongju Kang, Chaeeun Jeong, K. Chung

{"title":"Context-aware Model Selection for On-Device Object Detection","authors":"Seongju Kang, Chaeeun Jeong, K. Chung","doi":"10.1109/ICOIN50884.2021.9333918","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333918","url":null,"abstract":"A deep neural network (DNN) has become a key technique in many intelligent application domains. Since the DNN model has dozens of layers and millions of levels of parameters, the machine has to execute computation-intensive workloads. Therefore, it is difficult to perform DNN inference in resource-constrained devices such as mobile devices. In this paper, we propose a context-aware model selection for on-device DNN inference. The proposed model selection chooses a DNN model corresponding to a spatiotemporal domain based on the context information of the device. Since a context-aware model detects related objects by spatiotemporal domain, it has a low dimension of parameters. In resource-constrained environments, the proposed context-aware model enables high-accuracy inference at low latency. To evaluate the performance of the proposed model selection, we conduct comparison experiments with the existing object detection model. Through experiments, we confirm that the context-aware model performs better than the existing trained models when on-device object detection is performed. Finally, we discuss the limits of the proposed model selection.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"45 1","pages":"662-666"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74145586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatio-Temporal Degree of Freedom: Interference Management in 5G Edge SON Networks 时空自由度:5G边缘SON网络中的干扰管理

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333899

Joonpyo Hong, Y. Cho, S. K. Kim, J. Na, Jeongho Kwak

引用次数: 2

Implementing Viewport Tile Extractor for Viewport-Adaptive 360-Degree Video Tiled Streaming 实现视口自适应360度视频平铺流的视口平铺提取器

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333964

Jong-Beom Jeong, Soonbin Lee, I. Kim, Eun‐Seok Ryu

引用次数: 2

Using Machine Learning for Task Distribution in Fog-Cloud Scenarios: A Deep Performance Analysis 在雾云场景中使用机器学习进行任务分配:深度性能分析

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333929

M. Pourkiani, Masoud Abedi

引用次数: 1

ICOIN 2021 Conference Room Map ICOIN 2021会议室地图

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/icoin50884.2021.9333945

引用次数: 0

Lowest-In First-Service System for the Efficient MPT (Microwave Power Transfer) to Multiple Receivers 多接收机高效MPT(微波功率传输)的最低优先服务系统

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333980

Changyoung An, Hyeong Min Kwon, H. Ryu

{"title":"Lowest-In First-Service System for the Efficient MPT (Microwave Power Transfer) to Multiple Receivers","authors":"Changyoung An, Hyeong Min Kwon, H. Ryu","doi":"10.1109/ICOIN50884.2021.9333980","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333980","url":null,"abstract":"In this paper, a new lowest-in first-service system is proposed for the efficient MPT (Microwave Power Transfer) to multiple receivers. Basically, the power transmitter of the conventional MPT system for the multiple receivers provides the wireless power to a receiver in the power request sequence order, which can be consider as the first-in first-service method. From the aspect of overall power management and control of wireless power transfer system, this has critically serious problem to the receivers with very low battery power level because of the too long waiting or delay time to them. This drawback can be overcome by the proposed system. Using the power receiver identification (ID) and battery state information (BSI), the proposed system transmits the wireless power to the receiver of the lowest battery power. In this paper, to evaluate the performance of the proposed system, the total workload of the receivers is evaluated by applying each of the proposed system and the conventional system. Through the simulation results, it is confirmed that the proposed MPT system can provide a power efficient environment in which each receiver can perform work more effectively than the conventional MPT system in various receiver conditions.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"28 1","pages":"594-596"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80311258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Resource Allocation Scheme Based on Deep Reinforcement Learning for Device-to-Device Communications 基于深度强化学习的设备间通信资源分配方案

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333953

Seoyoung Yu, Yun Jae Jeong, J. W. Lee

引用次数: 3

Enhancing dynamism in management and network slice establishment through deep learning 通过深度学习增强管理和网络切片建立的动态性

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333872

Rodrigo Moreira, Larissa Ferreira Rodrigues, P. F. Rosa, R. Aguiar, Flávio de Oliveira Silva

{"title":"Enhancing dynamism in management and network slice establishment through deep learning","authors":"Rodrigo Moreira, Larissa Ferreira Rodrigues, P. F. Rosa, R. Aguiar, Flávio de Oliveira Silva","doi":"10.1109/ICOIN50884.2021.9333872","DOIUrl":"https://doi.org/10.1109/ICOIN50884.2021.9333872","url":null,"abstract":"With the variety of applications and the different user requirements, it is necessary to offer tailored resources efficiently not only in access but also in the core of the network. Inspired by the definition and standardization of mobile networks, especially 5G that focused on business verticals, the term network slicing has received numerous state-of-the-art efforts to materialize an approach that meets dynamism, programmability, and flexibility requirements. Leveraged by SDN and NFV technologies, network slicing is inspiring by resource sharing similar to virtual machine management, allowing standard network hardware to accommodate a wide variety of logical networks with specific requirements and data and control planes. However, state-of-the-art approaches do not address resource slicing at the core of the network in detail and appropriately. Therefore, we built NASOR to provide network slicing over the Internet data plane spanning across multiple domains through a segment routing and a distributed-based approach. Our approach excels those found in state-of-the-art by delivering an open policy interface that allows third-party applications to manage network slices dynamically. In this sense, this paper exploits this interface through a mechanism of convolutional neural networks that classifies network traffic, instructing the path-setting agent to be aware of application which predominantly runs on the network improving dynamism in the network slices deployment. Experiments showcase the convolutional neural network applicability and suitability as an enabling technology to enhance and instruct NASOR to establish network slices over multiple domains.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"7 1","pages":"321-326"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84389790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

UAV Trajectory Design for UAV-2-GV Communication in VANETs UAV-2- gv通信在VANETs中的UAV轨迹设计

2021 International Conference on Information Networking (ICOIN) Pub Date : 2021-01-13 DOI: 10.1109/ICOIN50884.2021.9333983

Aunas Manzoor, Nguyen Dang Tri, C. Hong

引用次数: 1