2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)最新文献

On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements 通过智能手机内测量分析蜂窝网络中YouTube QoE

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-11 DOI: 10.23919/WMNC.2019.8881828

Sarah Wassermann, P. Casas, Michael Seufert, Florian Wamser

{"title":"On the Analysis of YouTube QoE in Cellular Networks through in-Smartphone Measurements","authors":"Sarah Wassermann, P. Casas, Michael Seufert, Florian Wamser","doi":"10.23919/WMNC.2019.8881828","DOIUrl":"https://doi.org/10.23919/WMNC.2019.8881828","url":null,"abstract":"Cellular-network operators are becoming increasingly interested in knowing the Quality of Experience (QoE) of their customers. QoE measurements represent today a main source of information to monitor, analyze, and subsequently manage operational networks. In this paper, we focus on the analysis of YouTube QoE in cellular networks, using QoE and distributed network measurements collected in real users' smartphones. Relying on YoMoApp, a well-known tool for collecting YouTube smartphone measurements and QoE feedback in a crowdsourcing fashion, we have built a dataset covering about 360 different cellular users around the globe, throughout the past five years. Using this dataset, we study the characteristics of different QoE-relevant features for YouTube in smartphones. Measurements reveal a constant improvement of YouTube QoE in cellular networks over time, as well as an enhancement of the YouTube video streaming functioning in smartphones. Using the gathered measurements, we additionally investigate two case studies for YouTube QoE monitoring and analysis in cellular networks: the machine-learning-based prediction of QoE-relevant metrics from network-level measurements, and the modeling and assessment of YouTube QoE and user engagement in cellular networks and smartphone devices. Last but not least, we introduce the YoMoApp cloud dashboard to openly share smartphone YouTube QoE measurements, which allows anyone using the YoMoApp smartphone app to get immediate access to all the raw measurements collected at her devices.","PeriodicalId":197152,"journal":{"name":"2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115044285","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: 8

Towards a reference context model for adaptive learning 建立适应学习的参考语境模型

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881825

Ouissem Benmesbah, Mahnane Lamia, Mohamed Hafidi, Ishaq Zouaghi

引用次数: 3

Comparing different Machine-Learning techniques to predict Vehicles’ Positions using the received Signal Strength of periodic messages 比较不同的机器学习技术，利用周期性信息的接收信号强度来预测车辆的位置

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881819

Mamoudou Sangare, Dinh-Van Nguyen, S. Banerjee, P. Mühlethaler, S. Bouzefrane

{"title":"Comparing different Machine-Learning techniques to predict Vehicles’ Positions using the received Signal Strength of periodic messages","authors":"Mamoudou Sangare, Dinh-Van Nguyen, S. Banerjee, P. Mühlethaler, S. Bouzefrane","doi":"10.23919/WMNC.2019.8881819","DOIUrl":"https://doi.org/10.23919/WMNC.2019.8881819","url":null,"abstract":"In this paper, vehicles use the beacons sent by Road Side Units (RSUs) to predict their positions on a road. The reception power is strongly influenced by the distance between a vehicle and the neighboring RSUs and thus Machine-Learning can be used to predict the position of vehicles between RSUs. We have to assume that the vehicles know their own positions, at least for a given duration, to build the model of the machine-learning algorithm. This position information can be obtained for instance from a GPS. When this information is no longer available, the machine-learning algorithm can be used to predict the vehicles’ positions. The vehicles can send a position request to the RSUs which will know the reception power of their beacons and the machine-learning algorithm can respond with the estimated position. In this study, we compare four well-known machine-learning techniques : K Nearest Neighbors (KNN), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We study these techniques with different assumptions and discuss their respective advantages and drawbacks. Our results show that these four techniques provide very good results in terms of position predictions when the error on the transmission power is small.","PeriodicalId":197152,"journal":{"name":"2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115865220","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

Performance of Consensus and Formation Control subject to Bernoulli, Slotted Aloha and IEEE 802.11p Simulation Models 基于Bernoulli, Slotted Aloha和IEEE 802.11p仿真模型的共识和编队控制性能

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881681

J. Radak, Daniel Schneider, Christian Henke, Hannes Frey

{"title":"Performance of Consensus and Formation Control subject to Bernoulli, Slotted Aloha and IEEE 802.11p Simulation Models","authors":"J. Radak, Daniel Schneider, Christian Henke, Hannes Frey","doi":"10.23919/WMNC.2019.8881681","DOIUrl":"https://doi.org/10.23919/WMNC.2019.8881681","url":null,"abstract":"We consider distributed control algorithms in multi-agent systems in which all the agents cooperatively work on a given task. Timely exchange of information is essential for good performance of distributed control algorithms. Wireless communication used for information exchange in such systems is lossy and the reliability of wireless communication depends on many factors. We consider systems where one communication channel is shared by all the agents. To establish reliable communication we need efficient media access schemes. In this paper we evaluate with slotted Aloha and IEEE 802.11p the influence of two different media access control algorithms on the performance of the two distributed control algorithms consensus and formation control. We compare these findings to the performance when wireless media access control is modelled using a simple Bernoulli model. We show simulation results how far consensus and formation in multi-agent systems using IEEE 802.11p media access control outperform the ones using slotted Aloha. Moreover, we present results showing that performance simulations using IEEE 802.11p networks is comparable to the results retrieved from multi-agent systems simulated with a simple Bernoulli modelled communication channel.","PeriodicalId":197152,"journal":{"name":"2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134232068","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

Uplink Channel Estimation and Equalization in NB-IoT System NB-IoT系统中的上行信道估计与均衡

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881479

V. Savaux, Hamidou Dembélé, M. Kanj

引用次数: 5

Constructing Robust Spanning Trees in Distributed Optical Communication Satellite Networks 分布式光通信卫星网络鲁棒生成树的构建

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881811

Xianfeng Liu, Shuai Wu, Quan Chen, Jianming Guo, Lei Yang, Chengguang Fan, Yong Zhao

引用次数: 0

[WMNC 2019 Title Page] [WMNC 2019标题页]

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/wmnc.2019.8881649

Wmnc, M. Debbah, M. Curado, Thi Mai Trang Nguyen, S. Boumerdassi

引用次数: 0

Distributed Network, Neighbor Discovery and Blind Routing for Mobile Wireless Ad-hoc Networks 移动无线自组织网络的分布式网络、邻居发现和盲路由

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881802

Jed Carty, S. Jayaweera

{"title":"Distributed Network, Neighbor Discovery and Blind Routing for Mobile Wireless Ad-hoc Networks","authors":"Jed Carty, S. Jayaweera","doi":"10.23919/WMNC.2019.8881802","DOIUrl":"https://doi.org/10.23919/WMNC.2019.8881802","url":null,"abstract":"Mobile Ad-Hoc Networks (MANETs) often exist in dynamic environments that present challenges for network control functions, such as drone networks. Lacking infrastructure, MANETs require distributed control functions. Factors such as unpredictable movements, limited battery life and transmission ranges encourage efficient network control functions. Integrating control functions into a single protocol allows efficient, distributed control of the network which can be more robust against failures in the network. This paper presents the routing and network/neighbor discovery of a distributed network control protocol suitable for drone networks or other MANETS and an integrated protocol combining routing, and network/neighbor discovery. Using modified versions of Ad hoc On Demand Distance Vector (AODV) routing and Carrier-Sense Multiple Access (CSMA), the protocol performs blind route discovery and forwards packets while simultaneously performing network/neighbor discovery. Simulation shows the proposed protocol performing similarly to existing protocols for routing, scheduling, synchronization and network discovery even without having a priori knowledge of the network parameters which existing protocols use.","PeriodicalId":197152,"journal":{"name":"2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130969676","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: 5

An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding 多播无线虚拟网络嵌入的近似功率控制算法

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881324

Haitham Afifi, H. Karl

{"title":"An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding","authors":"Haitham Afifi, H. Karl","doi":"10.23919/WMNC.2019.8881324","DOIUrl":"https://doi.org/10.23919/WMNC.2019.8881324","url":null,"abstract":"Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes’ computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate.","PeriodicalId":197152,"journal":{"name":"2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129031992","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: 6

Intelligence Slicing: A Unified Framework to Integrate Artificial Intelligence into 5G Networks 智能切片:将人工智能集成到5G网络的统一框架

2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) Pub Date : 2019-09-01 DOI: 10.23919/WMNC.2019.8881402

Wei Jiang, S. D. Antón, H. Schotten

{"title":"Intelligence Slicing: A Unified Framework to Integrate Artificial Intelligence into 5G Networks","authors":"Wei Jiang, S. D. Antón, H. Schotten","doi":"10.23919/WMNC.2019.8881402","DOIUrl":"https://doi.org/10.23919/WMNC.2019.8881402","url":null,"abstract":"The fifth-generation and beyond mobile networks should support extremely high and diversified requirements from a wide variety of emerging applications. It is envisioned that more advanced radio transmission, resource allocation, and networking techniques are required to be developed. Fulfilling these tasks is challenging since network infrastructure becomes increasingly complicated and heterogeneous. One promising solution is to leverage the great potential of Artificial Intelligence (AI) technology, which has been explored to provide solutions ranging from channel prediction to autonomous network management, as well as network security. As of today, however, the state of the art of integrating AI into wireless networks is mainly limited to use a dedicated AI algorithm to tackle a specific problem. A unified framework that can make full use of AI capability to solve a wide variety of network problems is still an open issue. Hence, this paper will present the concept of intelligence slicing where an AI module is instantiated and deployed on demand. Intelligence slices are applied to conduct different intelligent tasks with the flexibility of accommodating arbitrary AI algorithms. Two example slices, i.e., neural network based channel prediction and anomaly detection based industrial network security, are illustrated to demonstrate this framework.","PeriodicalId":197152,"journal":{"name":"2019 12th IFIP Wireless and Mobile Networking Conference (WMNC)","volume":"21 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126449624","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