2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS)最新文献_第2页

RTagCare: Deep human activity recognition powered by passive computational RFID sensors RTagCare:由无源计算RFID传感器驱动的深度人类活动识别

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737258

Guibing Hu, Xue-song Qiu, Luoming Meng

引用次数: 4

An efficient and reliable Green Light Optimal Speed Advisory system for autonomous cars 一种高效可靠的自动驾驶汽车绿灯最佳速度咨询系统

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737260

Vandung Nguyen, O. Kim, Nguyen Dang Tri, S. I. Moon, C. Hong

{"title":"An efficient and reliable Green Light Optimal Speed Advisory system for autonomous cars","authors":"Vandung Nguyen, O. Kim, Nguyen Dang Tri, S. I. Moon, C. Hong","doi":"10.1109/APNOMS.2016.7737260","DOIUrl":"https://doi.org/10.1109/APNOMS.2016.7737260","url":null,"abstract":"An autonomous car is a self-driving car that is to keep the human being out of the car and to relieve them from the task of driving. An autonomous car can make more convenient, safer, and less energy intensive. In addition, Green Light Optimal Speed Advisory (GLOSA) systems reduce the travel time and CO2 emission. In this paper, we propose a novel GLOSA, called R-GLOSA system to support to autonomous cars. We assume that an autonomous car can access to all traffic light schedules that it will encounter on its route. The route is divided into each segment according to traffic lights. In each segment, an autonomous car can communicate to Road Side Units (RSUs) distributed among the road. An autonomous car collects the road information transmitted by an RSU and then, it optimizes speed in order to arrive at the intersection when the light is green. The R-GLOSA system provides an autonomous car with speed advisory for each RSU's coverage. The simulation results show that an autonomous car using R-GLOSA system outperforms in terms of travel time and waiting time compared to using single-segment and multi-segment GLOSA system.","PeriodicalId":194123,"journal":{"name":"2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117177337","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: 25

Development of QoE initiated wireless LAN base station with DPI based QoS control QoE的发展引发了基于DPI的QoS控制的无线局域网基站

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737245

S. Asano, Yoshihiro Ito, Yuma Watai

引用次数: 1

Distributed resource allocation for interference management and QoS guarantee in underlay cognitive femtocell networks 基于底层认知飞蜂窝网络干扰管理和QoS保障的分布式资源分配

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737247

T. Ho, Nguyen H. Tran, S. M. Ahsan Kazmi, Do Hyun Kim, C. Hong

引用次数: 6

A PSO-based wireless network virtual mapping algorithm in smart grid 一种基于pso的智能电网无线网络虚拟映射算法

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737205

Zhilin Li, Shao-Yong Guo, Yang Yang, Cheng Zhong, Zhu Liu

引用次数: 0

A converged network architecture for ICT and IoT combined applications ICT与IoT融合应用的融合网络架构

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737284

Chung-Shih Tang, Yi-Kai Chiang, Ying-Dian Tsou, Jen-Hong Ju, Chin-Ywu Twu

引用次数: 9

LM-BP based operation quality assessment method for OTN in Smart grid 基于LM-BP的智能电网OTN运行质量评估方法

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737257

Manman Wu, Shao-Yong Guo, Xingyu Chen, Ningzhe Xing, Cheng Zhong

引用次数: 5

Double ended Spectrum Defragmentation on Software Defined optical Networks 软件定义光网络的双端频谱碎片整理

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737262

Tsan-Chang Kuo, Min-Chia Chang, Tai-Yuan Lee, Y. Tsai

引用次数: 0

Adaptive mobile application identification through in-network machine learning 通过网络内机器学习的自适应移动应用识别

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737226

Takamitsu Iwai, A. Nakao

{"title":"Adaptive mobile application identification through in-network machine learning","authors":"Takamitsu Iwai, A. Nakao","doi":"10.1109/APNOMS.2016.7737226","DOIUrl":"https://doi.org/10.1109/APNOMS.2016.7737226","url":null,"abstract":"Application identification is beneficial for malware detection, content cache, application-specific QoS, traffic control, etc. The existing identification methods using machine learning are usually limited to identification of protocols, not applications, and hard to adapt to the emergence of new applications. We have been proposing a new method for adaptive application identification with machine learning where we create training dataset in real-time by tagging traffic with application process names from a small number of modified smartphones and identify the unknown flow using the classifier trained by the training data. Our previous research [1] achieves more than 80% of accuracy in application identification when not using DPI (Deep Packet Inspection). In this paper, we propose an extension to the previous method using DPI and on-line machine learning with pre-classification of traffic based on ports to improve the inference accuracy. The evaluation shows that our method can identify more than 92% of traffic accurately using DPI when learning period is 5 days, and achieves up to 93% of accuracy at best for general traffic. When limited to HTTP, the accuracy becomes 96%. This result shows that we can build a system identifying more than 92% of the applications adaptively in relatively short training period, e.g., 5 days of training, even when new applications emerge. We envision that our proposed system eventually enables application specific traffic engineering as well as application specific network function execution within the network.","PeriodicalId":194123,"journal":{"name":"2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116771689","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: 13

A load-balancing-based fault-tolerant mapping method in smart grid virtual networks 基于负载均衡的智能电网虚拟网络容错映射方法

2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS) Pub Date : 2016-10-01 DOI: 10.1109/APNOMS.2016.7737206

Li-Qian Sun, Shao-Yong Guo, Siya Xu, Zhu Liu, Lei Wei

引用次数: 1