2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)最新文献_第5页

A Connection-Free Reliable Transport Protocol 无连接可靠传输协议

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391540

J. Garcia-Luna-Aceves, A. Albalawi

引用次数: 1

Socially-Aware D2D Pair Strategy: A Stable Matching Approach 社会意识的D2D配对策略:一种稳定的匹配方法

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391547

Xian Zhou, Daru Pan, Hui Song, Xu Huang

引用次数: 2

Research of Knowledge Graph Technology and its Applications in Agricultural Information Consultation Field 知识图谱技术及其在农业信息咨询领域的应用研究

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391515

Fan Zhang, Jizhou Wu, Yingli Nie, Lihua Jiang, Ailian Zhou, N. Xie

引用次数: 3

Software Define Radio in Realizing the Intruding UAS Group Behavior Prediction 软件定义无线电实现入侵无人机群行为预测

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391526

Joshua Eason, Chengtao Xu, H. Song

{"title":"Software Define Radio in Realizing the Intruding UAS Group Behavior Prediction","authors":"Joshua Eason, Chengtao Xu, H. Song","doi":"10.1109/IPCCC50635.2020.9391526","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391526","url":null,"abstract":"With the advancement of unmanned aerial vehicle (UAV) technology, UAV swarm has been showing its great security threats towards the ground facility. With current technologies, it is still challenging in unknown UAV swarm tracking and neutralization. In this paper, we propose an analytical method in predicting drone flying behavior based on the machine learning algorithm, which could be integrated into swarm behavior prediction. Radiofrequency (RF) signals emitted from the UAV are captured by software-defined radio (SDR) to form the time series data. By using conventional short-time Fourier transform (STFT), a time-frequency spectrum revealing the RF data energy distribution is obtained for analyzing the signal variance pattern formed by the two different types of UAV flying trajectory. The transformed time-frequency domain matrix would be applied in multiple machine learning classifier for telling the difference of different flying trajectory. The results present the applicability of using machine learning in predicting the flying features and modes of intruding UAV swarm. It shows the potential application of this method in realizing effective UAV swarm negation.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116680942","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: 1

Lifecycle Management Protocols for Batteryless, Intermittent Sensor Nodes 无电池间歇传感器节点的生命周期管理协议

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391571

Mathew L. Wymore, Vishal Deep, Vishak Narayanan, Henry Duwe, D. Qiao

引用次数: 2

Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet? 中断实时物联网任务:将关键嵌入式系统连接到互联网会有多糟糕?

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391536

Ilja Behnke, Lukas Pirl, L. Thamsen, Robert Danicki, A. Polze, O. Kao

{"title":"Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet?","authors":"Ilja Behnke, Lukas Pirl, L. Thamsen, Robert Danicki, A. Polze, O. Kao","doi":"10.1109/IPCCC50635.2020.9391536","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391536","url":null,"abstract":"Embedded systems have been used to control physical environments for decades. Usually, such use cases require low latencies between commands and actions as well as a high predictability of the expected worst-case delay. To achieve this on small, low-powered microcontrollers, Real-Time Operating Systems (RTOSs) are used to manage the different tasks on these machines as deterministically as possible. However, with the advent of the Internet of Things (IoT) in industrial applications, the same embedded systems are now equipped with networking capabilities, possibly endangering critical real-time systems through an open gate to interrupts.This paper presents our initial study of the impact network connections can have on real-time embedded systems. Specifically, we look at three aspects: The impact of network-generated interrupts, the overhead of the related networking tasks, and the feasibility of sharing computing resources between networking and real-time tasks. We conducted experiments on two setups: One treating NICs and drivers as black boxes and one simulating network interrupts on the machines. The preliminary results show that a critical task performance loss of up to 6.67% per received packet per second could be induced where lateness impacts of 1% per packet per second can be attributed exclusively to ISR-generated delays.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134116748","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: 10

An Encrypted Traffic Classification Method Combining Graph Convolutional Network and Autoencoder 结合图卷积网络和自编码器的加密流量分类方法

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391542

Bo Sun, Wenyuan Yang, Mengqi Yan, Dehao Wu, Yuesheng Zhu, Zhiqiang Bai

{"title":"An Encrypted Traffic Classification Method Combining Graph Convolutional Network and Autoencoder","authors":"Bo Sun, Wenyuan Yang, Mengqi Yan, Dehao Wu, Yuesheng Zhu, Zhiqiang Bai","doi":"10.1109/IPCCC50635.2020.9391542","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391542","url":null,"abstract":"The increase in the source and size of encrypted network traffic brings significant challenges for network traffic analysis. The challenging problem in the encrypted traffic classification field is obtaining high classification accuracy with small number of labeled samples. To solve this problem, we propose a novel encryption traffic classification method that learns the feature representation from the traffic structure and the traffic flow data in this paper. We construct a K-Nearest Neighbor (KNN) traffic graph to represent the structure of traffic data, which contains more similarity information about the traffic. We utilize a two-layer Graph Convolutional Network (GCN) architecture for flows feature extraction and encrypted traffic classification. We further use the autoencoder to learn the representation of the flow data itself and integrate it into the GCN-learned representation to form a more complete feature representation. The proposed method leverages the benefits of the GCN and the autoencoder, which can obtain higher classification performance with only very few labeled data. The experimental results on two public datasets demonstrate that our method achieves impressive results compared to the state-of-the-art competitors.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132914467","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: 16

Research on Power Quality Data Placement Strategy Based on Improved Particle Swarm Optimization Algorithm 基于改进粒子群算法的电能质量数据放置策略研究

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391545

Chengdong Wang, Jun Fang, Zhuofeng Zhao, Bo Zhao

{"title":"Research on Power Quality Data Placement Strategy Based on Improved Particle Swarm Optimization Algorithm","authors":"Chengdong Wang, Jun Fang, Zhuofeng Zhao, Bo Zhao","doi":"10.1109/IPCCC50635.2020.9391545","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391545","url":null,"abstract":"For the national grid power quality monitoring system, the effective integration of monitoring terminals, the master stations of each network and the province and the state grid data center work together, the reasonable placement of the monitoring data in the system, and the relief of the calculation pressure of the state grid data center are the project research focus. From a global perspective, this paper models and describes the data placement problem of the harmonic monitoring system, and proposes a data placement strategy based on an improved particle swarm optimization algorithm. This paper proposes an initial population generation algorithm based on Markov random walk, which enables individuals in the initial population to have a certain degree of clustering accuracy and strong diversity. The initial population generation algorithm cooperates with the particle swarm optimization algorithm, which effectively enhances the algorithm's optimization ability. Through comparative experiments with traditional data placement strategies, the experimental results show that the data placement strategy based on improved particle swarm optimization algorithm has higher efficiency.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132686391","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

UAV Swarm Communication Aware Formation Control via Deep Q Network 基于深度Q网络的无人机群通信感知编队控制

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391509

Chengtao Xu, Kai Zhang, H. Song

引用次数: 3

Hieff: Enabling Efficient VNF Clusters by Coordinating VNF Scaling and Flow Scheduling Hieff:通过协调VNF扩展和流调度，实现高效的VNF集群

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391534

Zenan Wang, Jiao Zhang, Haoran Wei, Tao Huang

{"title":"Hieff: Enabling Efficient VNF Clusters by Coordinating VNF Scaling and Flow Scheduling","authors":"Zenan Wang, Jiao Zhang, Haoran Wei, Tao Huang","doi":"10.1109/IPCCC50635.2020.9391534","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391534","url":null,"abstract":"A cluster of Virtual Network Functions (VNF) can serve massive fluctuating traffic by managing VNF instances and distributing flows. However, how to schedule the flows and manage VNF scaling efficiently in a VNF cluster is still an open question. Existing solutions such as hash based schemes encounter imbalance and passive flow remapping obstacles while flow table-based scheme suffers from high processing latency and flow entries overflow challenges. In this paper, we design and present Hieff, an efficient NFV system that coordinates VNF scaling and flow scheduling within a VNF cluster. The key idea of Hieff is to precisely manage the heavy flows with a flow table while simply allowing light flows be distributed by hash. Though this idea has been explored in previous work, we are the first to apply it with the VNF scaling process. We mathematically model the Hieff system and propose a heuristic algorithm to determine the optimized VNF scaling and flow scheduling strategies. We implement Hieff based on BESS and Click and use real-world tracing to evaluate the system. Results show that Hieff can handle co-existing massive flows efficiently with low latency while balancing the load of VNF instances at low cost.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128246309","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