2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)最新文献_第6页

Modeling Simple HetNet Configurations with Mixed Traffic Loads 混合流量负载下的简单HetNet配置建模

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2021-06-01 DOI: 10.1109/WoWMoM51794.2021.00025

M. Marsan, M. Meo, M. Sereno

引用次数: 1

How UAVs’ Highly Dynamic 3D Movement Improves Network Security? 无人机的高动态3D运动如何提高网络安全?

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2021-05-06 DOI: 10.1109/WoWMoM51794.2021.00022

Mohammed Gharib, F. Afghah

{"title":"How UAVs’ Highly Dynamic 3D Movement Improves Network Security?","authors":"Mohammed Gharib, F. Afghah","doi":"10.1109/WoWMoM51794.2021.00022","DOIUrl":"https://doi.org/10.1109/WoWMoM51794.2021.00022","url":null,"abstract":"Cooperative ad hoc unmanned aerial vehicle (UAV) networks need essential security services to ensure their communication security. Cryptography, as the inseparable tool for providing security services, requires a robust key management system. Alas, the absence of infrastructure in cooperative networks leads to the infeasibility of providing conventional key management systems. Key pre-distribution schemes have shown promising performance in different cooperative networks due to their lightweight nature. However, intermediate decryption-encryption (DE) steps and the lack of key updates are the most concerning issues they suffer from. In this paper, we propose a simple and effective key management algorithm inspired by the idea of key pre-distribution, where it utilizes the highly dynamic UAV node movement in 3D space to provide the key update feature and optimizes the number of intermediate DE steps. Although it is a general model for any mobile ad hoc network, we have selected UAV network as an example domain to show the efficiency of the model given the high mobility. We define the communication density parameter to analytically show that using any highly dynamic random movement pattern leads our algorithm to work effectively. To show the proposed algorithm’s effectiveness, we exhaustively analyze its security and performance in the UAV network using the ns-3 network simulator. Results validate our analytical findings and show how the highly dynamic UAV network movement helps our algorithm to provide the key update feature and to optimize the number of DE steps 1.","PeriodicalId":131571,"journal":{"name":"2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690620","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

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2021-05-05 DOI: 10.1109/WoWMoM51794.2021.00053

Marcin Hoffmann, P. Kryszkiewicz

{"title":"Similarity Measures for Location-Dependent MMIMO, 5G Base Stations On/Off Switching Using Radio Environment Map","authors":"Marcin Hoffmann, P. Kryszkiewicz","doi":"10.1109/WoWMoM51794.2021.00053","DOIUrl":"https://doi.org/10.1109/WoWMoM51794.2021.00053","url":null,"abstract":"The Massive Multiple-Input Multiple-Output (MMIMO) technique together with Heterogeneous Network (Het-Net) deployment enables high throughput of 5G and beyond networks. However, a high number of antennas and a high number of Base Stations (BSs) can result in significant power consumption. Previous studies have shown that the energy efficiency (EE) of such a network can be effectively increased by turning off some BSs depending on User Equipments (UEs) positions. Such mapping is obtained by using Reinforcement Learning. Its results are stored in a so-called Radio Environment Map (REM). However, in a real network, the number of UEs’ positions patterns would go to infinity. This paper aims to determine how to match the current set of UEs’ positions to the most similar pattern, i.e., providing the same optimal active BSs set, saved in REM. We compare several state-of-the-art distance metrics using a computer simulator: an accurate 3D-Ray-Tracing model of the radio channel and an advanced system-level simulator of MMIMO Het-Net. The results have shown that the so-called Sum of Minimums Distance provides the best matching between REM data and UEs’ positions, enabling up to 56% EE improvement over the scenario without EE optimization.","PeriodicalId":131571,"journal":{"name":"2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126665722","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

Fairness in Network-Friendly Recommendations 网络友好推荐中的公平性

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2021-04-02 DOI: 10.1109/WoWMoM51794.2021.00020

Theodoros Giannakas, Pavlos Sermpezis, A. Giovanidis, T. Spyropoulos, G. Arvanitakis

{"title":"Fairness in Network-Friendly Recommendations","authors":"Theodoros Giannakas, Pavlos Sermpezis, A. Giovanidis, T. Spyropoulos, G. Arvanitakis","doi":"10.1109/WoWMoM51794.2021.00020","DOIUrl":"https://doi.org/10.1109/WoWMoM51794.2021.00020","url":null,"abstract":"As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been proposed recently to boost the network performance by promoting content that can be efficiently delivered (e.g., cached at the edge). NFR increase the network performance, however, at the cost of being unfair towards certain contents when compared to the standard recommendations. This unfairness is a side effect of NFR that has not been studied in literature. Nevertheless, retaining fairness among contents is a key operational requirement for content providers. This paper is the first to study the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics that capture different notions of fairness, and study the unfairness created by existing NFR schemes. Our analysis reveals that NFR can be significantly unfair. We identify an inherent trade-off between the network gains achieved by NFR and the resulting unfairness, and derive bounds for this trade-off. We show that existing NFR schemes frequently operate far from the bounds, i.e., there is room for improvement. To this end, we formulate the design of Fair-NFR (i.e., NFR with fairness guarantees compared to the baseline recommendations) as a linear optimization problem. Our results show that the Fair-NFR can achieve high network gains (similar to non-fair-NFR) with little unfairness.","PeriodicalId":131571,"journal":{"name":"2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704425","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: 3

Relational Deep Reinforcement Learning for Routing in Wireless Networks 无线网络中路由的关系深度强化学习

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2020-12-31 DOI: 10.1109/WoWMoM51794.2021.00029

Victoria Manfredi, Alicia P. Wolfe, Bing Wang, X. Zhang

{"title":"Relational Deep Reinforcement Learning for Routing in Wireless Networks","authors":"Victoria Manfredi, Alicia P. Wolfe, Bing Wang, X. Zhang","doi":"10.1109/WoWMoM51794.2021.00029","DOIUrl":"https://doi.org/10.1109/WoWMoM51794.2021.00029","url":null,"abstract":"While routing in wireless networks has been studied extensively, existing protocols are typically designed for a specific set of network conditions and so do not easily accommodate changes in those conditions. For instance, protocols that assume network connectivity cannot be easily applied to disconnected networks. In this paper, we develop a distributed routing strategy based on deep reinforcement learning that generalizes to diverse traffic patterns, congestion levels, network connectivity, and link dynamics. We make the following key innovations in our design: (i) the use of relational features as inputs to the deep neural network approximating the decision space, which enables our algorithm to generalize to diverse network conditions, (ii) the use of packet-centric decisions to transform the routing problem into an episodic task by viewing packets, rather than wireless devices, as reinforcement learning agents, which provides a natural way to propagate and model rewards accurately during learning, and (iii) the use of extended-time actions to model the time spent by a packet waiting in a queue, which reduces the amount of training data needed and allows the learning algorithm to converge more quickly. We evaluate our routing algorithm using a packet-level simulator and show that the policy our algorithm learns during training is able to generalize to larger and more congested networks, different topologies, and diverse link dynamics. Our algorithm outperforms shortest path and backpressure routing with respect to packets delivered and delay per packet.","PeriodicalId":131571,"journal":{"name":"2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122026457","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

WIP: Short-Term Flow-Based Bandwidth Forecasting using Machine Learning WIP:使用机器学习的短期流量带宽预测

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2020-11-29 DOI: 10.1109/WoWMoM51794.2021.00048

Maxime Labonne, Jorge López, Claude Poletti, Jean-Baptiste Munier

{"title":"WIP: Short-Term Flow-Based Bandwidth Forecasting using Machine Learning","authors":"Maxime Labonne, Jorge López, Claude Poletti, Jean-Baptiste Munier","doi":"10.1109/WoWMoM51794.2021.00048","DOIUrl":"https://doi.org/10.1109/WoWMoM51794.2021.00048","url":null,"abstract":"This paper proposes a novel framework to predict traffic flows’ bandwidth ahead of time. Modern network management systems share a common issue: the network situation evolves between the moment the decision is made and the moment when actions (countermeasures) are applied. This framework converts packets from real-life traffic into flows containing relevant features. Machine learning models, including Decision Tree, Random Forest, XGBoost, and Deep Neural Network, are trained on these data to predict the bandwidth at the next time instance for every flow. Predictions can be fed to the management system instead of current flows bandwidth in order to take decisions on a more accurate network state. Experiments were performed on 981,774 flows and 15 different time windows (from 0.03s to 4s). They show that the Random Forest is the best performing and most reliable model, with a predictive performance consistently better than relying on the current bandwidth (+19.73% in mean absolute error and +18.00% in root mean square error). Experimental results indicate that this framework can help network management systems to take more informed decisions using a predicted network state.","PeriodicalId":131571,"journal":{"name":"2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121904746","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

[Copyright notice] (版权)

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2020-10-01 DOI: 10.1109/icumt51630.2020.9222454

引用次数: 0

Technical Program Committee 技术计划委员会

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2018-07-01 DOI: 10.1109/igarss.2018.8518042

J. Abolarinwa, A. Behboodi

引用次数: 0

Message from the TPC Co-Chairs 贸易政策委员会联合主席的致辞

2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2014-11-01 DOI: 10.1109/WD.2014.7020787

A. Ziviani, C. Sengul, J. Nogueira

引用次数: 0