Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523587
Nikos Perpinias, Alexandros Palaios, Janne Riihijärvi, P. Mähönen
In recent years, novel spectrum access schemes have increased the need to further explore and exploit radio propagation dynamics. One proposed approach is to use spectrum sensing nodes for propagation environment estimation, and particularly to allow shadowing field extraction. In general there is a tendency to develop techniques that allow the automated and localized estimation of the spatial correlation structure of the shadowing field. Key issue when using such data is to take into account inherent localization errors and the impact that it has on these estimates. In this paper we use real measurement data acquired with extremely high localization accuracy to demonstrate and study the impact of localization errors. We present results for the propagation estimation, the extracted shadow field, and estimated shadow correlations. In particular, we show that spatial correlation metrics such as semivariograms are robust against localization errors higher than arising from the typical embedded GPS chipsets, up to approximately 20 m. Moreover, we have considered various probability distributions of localization errors, exploring their impact into our analysis. The reported results are highly relevant to development of measurement-based coverage estimation techniques and planning of drive tests or understanding limits of crowd-sourced data from user devices.
{"title":"Measurement-based study on the influence of localization errors on estimated shadow correlations","authors":"Nikos Perpinias, Alexandros Palaios, Janne Riihijärvi, P. Mähönen","doi":"10.1109/WoWMoM.2016.7523587","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523587","url":null,"abstract":"In recent years, novel spectrum access schemes have increased the need to further explore and exploit radio propagation dynamics. One proposed approach is to use spectrum sensing nodes for propagation environment estimation, and particularly to allow shadowing field extraction. In general there is a tendency to develop techniques that allow the automated and localized estimation of the spatial correlation structure of the shadowing field. Key issue when using such data is to take into account inherent localization errors and the impact that it has on these estimates. In this paper we use real measurement data acquired with extremely high localization accuracy to demonstrate and study the impact of localization errors. We present results for the propagation estimation, the extracted shadow field, and estimated shadow correlations. In particular, we show that spatial correlation metrics such as semivariograms are robust against localization errors higher than arising from the typical embedded GPS chipsets, up to approximately 20 m. Moreover, we have considered various probability distributions of localization errors, exploring their impact into our analysis. The reported results are highly relevant to development of measurement-based coverage estimation techniques and planning of drive tests or understanding limits of crowd-sourced data from user devices.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668212","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523569
A. Kumar, Bernd Schäufele, Daniel Becker, Oliver Sawade, I. Radusch
Modern vehicles are equipped with numerous driver assistance and telematics functions, such as Turn-by-Turn navigation. Most of these systems rely on precise positioning of the vehicle. While Global Navigation Satellite Systems (GNSS) are available outdoors, these systems fail in indoor environments such as a car-park or a tunnel. Alternatively, the vehicle can localize itself with landmark-based positioning and internal car sensors, yet this is not only costly but also requires precise knowledge of the enclosed area. Instead, our approach is to use infrastructure-based positioning. Here, we utilize off-the shelf cameras mounted in the car-park and Vehicle-to-Infrastructure Communication to allow all vehicles to obtain an indoor position given from an infrastructure-based localization service. Our approach uses a Convolutional Neural Network (CNN) with Deep Learning to identify and localize vehicles in a car-park. We thus enable position-based Driver Assistance Systems (DAS) and telematics in an underground facility. We compare the novel Deep Learning classifier to a conventional classifier using Haar-like features.
{"title":"Indoor localization of vehicles using Deep Learning","authors":"A. Kumar, Bernd Schäufele, Daniel Becker, Oliver Sawade, I. Radusch","doi":"10.1109/WoWMoM.2016.7523569","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523569","url":null,"abstract":"Modern vehicles are equipped with numerous driver assistance and telematics functions, such as Turn-by-Turn navigation. Most of these systems rely on precise positioning of the vehicle. While Global Navigation Satellite Systems (GNSS) are available outdoors, these systems fail in indoor environments such as a car-park or a tunnel. Alternatively, the vehicle can localize itself with landmark-based positioning and internal car sensors, yet this is not only costly but also requires precise knowledge of the enclosed area. Instead, our approach is to use infrastructure-based positioning. Here, we utilize off-the shelf cameras mounted in the car-park and Vehicle-to-Infrastructure Communication to allow all vehicles to obtain an indoor position given from an infrastructure-based localization service. Our approach uses a Convolutional Neural Network (CNN) with Deep Learning to identify and localize vehicles in a car-park. We thus enable position-based Driver Assistance Systems (DAS) and telematics in an underground facility. We compare the novel Deep Learning classifier to a conventional classifier using Haar-like features.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127247478","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523509
Giang T. Nguyen, Stefanie Roos, Benjamin Schiller, T. Strufe
In pull-based Peer-to-Peer video streaming systems, peers exchange buffer maps to reveal the availability of video chunks in their buffer. When collecting these buffer maps, a malicious party can infer the system's overlay structure and even identify head nodes, the direct communication partners of the stream's source. Attacking these head nodes can isolate peers from the source resulting in a disruption of the video dissemination for most peers in the system. We introduce a lightweight SWAP scheme, which allows peers to proactively change their partners, to reduce the chance of head nodes to be identified by such an inference attacker. Extensive simulation studies demonstrate that our scheme effectively undermines the attack's accuracy in identifying head nodes. So, SWAP lowers the chunk miss ratio while causing only a slight increase in signaling overhead.
{"title":"SWAP: Protecting pull-based P2P video streaming systems from inference attacks","authors":"Giang T. Nguyen, Stefanie Roos, Benjamin Schiller, T. Strufe","doi":"10.1109/WoWMoM.2016.7523509","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523509","url":null,"abstract":"In pull-based Peer-to-Peer video streaming systems, peers exchange buffer maps to reveal the availability of video chunks in their buffer. When collecting these buffer maps, a malicious party can infer the system's overlay structure and even identify head nodes, the direct communication partners of the stream's source. Attacking these head nodes can isolate peers from the source resulting in a disruption of the video dissemination for most peers in the system. We introduce a lightweight SWAP scheme, which allows peers to proactively change their partners, to reduce the chance of head nodes to be identified by such an inference attacker. Extensive simulation studies demonstrate that our scheme effectively undermines the attack's accuracy in identifying head nodes. So, SWAP lowers the chunk miss ratio while causing only a slight increase in signaling overhead.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126114602","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523497
Dimitris Chatzopoulos, Mahdieh Ahmadi, Sokol Kosta, P. Hui
During the last years, researchers have proposed solutions to help smartphones offload heavy tasks to remote entities in order to improve execution time and reduce energy consumption. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices. None of these strategies, though, proposes any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to “sacrifice” when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.
{"title":"Have you asked your neighbors? A Hidden Market approach for device-to-device offloading","authors":"Dimitris Chatzopoulos, Mahdieh Ahmadi, Sokol Kosta, P. Hui","doi":"10.1109/WoWMoM.2016.7523497","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523497","url":null,"abstract":"During the last years, researchers have proposed solutions to help smartphones offload heavy tasks to remote entities in order to improve execution time and reduce energy consumption. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices. None of these strategies, though, proposes any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to “sacrifice” when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"514 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127046262","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523529
S. Kandukuri, J. Lebreton, N. Murad, R. Lorion, Jean-Daniel Lan Sun Luk
We present an adaptive data aggregative window function (A-DAWF) for a distributed sensor network model in which nodes store data in their attribute window functions, and provide non-correlated data towards the base station (BS). Unlike previous works, namely data collection or data gathering management systems, we propose a novel approach that aims to process temporal redundant techniques in sensor nodes as well as providing spatial redundant filtration methods in cluster-head (CH) nodes. In this regard, preliminary results show that A-DAWF can suppress up to 90% of temporal redundant data among the considered sensor nodes by an optimal threshold of the window sizes, and their spatial correlations in CH node by a maximum error threshold compared to either periodic or a continuous data transmission system.
{"title":"Energy-efficient cluster-based protocol using an adaptive data aggregative window function (A-DAWF) for wireless sensor networks","authors":"S. Kandukuri, J. Lebreton, N. Murad, R. Lorion, Jean-Daniel Lan Sun Luk","doi":"10.1109/WoWMoM.2016.7523529","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523529","url":null,"abstract":"We present an adaptive data aggregative window function (A-DAWF) for a distributed sensor network model in which nodes store data in their attribute window functions, and provide non-correlated data towards the base station (BS). Unlike previous works, namely data collection or data gathering management systems, we propose a novel approach that aims to process temporal redundant techniques in sensor nodes as well as providing spatial redundant filtration methods in cluster-head (CH) nodes. In this regard, preliminary results show that A-DAWF can suppress up to 90% of temporal redundant data among the considered sensor nodes by an optimal threshold of the window sizes, and their spatial correlations in CH node by a maximum error threshold compared to either periodic or a continuous data transmission system.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442677","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523498
V. Mancuso, A. Asadi, P. Jacko
Opportunistic schedulers such as MaxRate and Proportional Fair are known for trading off between throughput and fairness of users in cellular networks. In this paper, we propose a novel solution that integrates opportunistic scheduling design principles and cooperative D2D communication capabilities in order to maximize fairness without sacrificing throughput. Specifically, we develop a mathematical approach and design a smart tie-breaking scheme which maximizes the fairness achieved by the MaxRate scheduler. However, our approach could be applied to improve fairness of any scheduler. In addition, we show that users that cooperatively form D2D clusters benefit from both higher throughput and fairness. Our scheduling scheme is simple to implement, scales linearly with the number of clusters, and is able to double the throughput of Equal Time schedulers and to outperform by 20% or more Proportional Fair schedulers, while providing a user fairness index comparable to or better than Proportional Fair.
{"title":"Tie-breaking can maximize fairness without sacrificing throughput in D2D-assisted networks","authors":"V. Mancuso, A. Asadi, P. Jacko","doi":"10.1109/WoWMoM.2016.7523498","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523498","url":null,"abstract":"Opportunistic schedulers such as MaxRate and Proportional Fair are known for trading off between throughput and fairness of users in cellular networks. In this paper, we propose a novel solution that integrates opportunistic scheduling design principles and cooperative D2D communication capabilities in order to maximize fairness without sacrificing throughput. Specifically, we develop a mathematical approach and design a smart tie-breaking scheme which maximizes the fairness achieved by the MaxRate scheduler. However, our approach could be applied to improve fairness of any scheduler. In addition, we show that users that cooperatively form D2D clusters benefit from both higher throughput and fairness. Our scheduling scheme is simple to implement, scales linearly with the number of clusters, and is able to double the throughput of Equal Time schedulers and to outperform by 20% or more Proportional Fair schedulers, while providing a user fairness index comparable to or better than Proportional Fair.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127625674","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523527
José Núñez-Martínez, J. Baranda, I. Pascual, J. Mangues‐Bafalluy
The deployment of dense networks of Small Cells (SC) is one of the key components of 5G mobile networks and will pose several challenges to the backhaul network. Software-Defined Networking (SDN) is a key technique to cope with the increased management complexity of such heterogeneous deployments, helping in the task of achieving a system-wide network management, which includes the 5G mobile wireless backhaul. Therefore, it is fundamental to devise the tools for the evaluation of 5G backhauling SDN architectures. This paper presents WiseHAUL: an SDN-empowered Wireless Small Cell Backhaul testbed. We present the design considerations of WiseHAUL, deployed in the form of an hypercube mesh that combines heterogeneous wireless technologies with the final goal of conducting SDN research and experimentation. Preliminary experimental results serve to identify the main research challenges faced by canonical SDN management techniques in an all-wireless backhaul deployment, hence acting as key drivers for further redesign and testbed development towards hybrid SDN models.
{"title":"WiseHAUL: An SDN-empowered Wireless Small Cell Backhaul testbed","authors":"José Núñez-Martínez, J. Baranda, I. Pascual, J. Mangues‐Bafalluy","doi":"10.1109/WoWMoM.2016.7523527","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523527","url":null,"abstract":"The deployment of dense networks of Small Cells (SC) is one of the key components of 5G mobile networks and will pose several challenges to the backhaul network. Software-Defined Networking (SDN) is a key technique to cope with the increased management complexity of such heterogeneous deployments, helping in the task of achieving a system-wide network management, which includes the 5G mobile wireless backhaul. Therefore, it is fundamental to devise the tools for the evaluation of 5G backhauling SDN architectures. This paper presents WiseHAUL: an SDN-empowered Wireless Small Cell Backhaul testbed. We present the design considerations of WiseHAUL, deployed in the form of an hypercube mesh that combines heterogeneous wireless technologies with the final goal of conducting SDN research and experimentation. Preliminary experimental results serve to identify the main research challenges faced by canonical SDN management techniques in an all-wireless backhaul deployment, hence acting as key drivers for further redesign and testbed development towards hybrid SDN models.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131042940","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523584
F. Ramalho, A. Neto
The rapid growth of the Internet of Things (IoT) is driving the evolution of Cloud technologies towards resource distribution across multi-clouds and the inclusion of various heterogeneous devices. IoT specific applications may require the deployment of gateways at the network edge to enable its interaction with physical sensors, pre-processing data from these sensors, and synchronizing it with the cloud. The orchestration, deployment, and maintenance of the software running on the gateways in large-scale deployments is known to be challenging. In this paper we analyze two virtualization techniques commonly used for deployment and management of applications at the cloud, but on the context of IoT and network edge. A study was conducted to evaluate the performance of the container-based approach compared to a hypervisor-based virtualization when running on devices typically used at the network edge. The study was performed through the execution of several synthetic benchmarks providing an insight in the performance overhead introduced by Docker containers (lightweight-virtualization) and KVM VMs (hypervisor-virtualization) running at network edge devices.
{"title":"Virtualization at the network edge: A performance comparison","authors":"F. Ramalho, A. Neto","doi":"10.1109/WoWMoM.2016.7523584","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523584","url":null,"abstract":"The rapid growth of the Internet of Things (IoT) is driving the evolution of Cloud technologies towards resource distribution across multi-clouds and the inclusion of various heterogeneous devices. IoT specific applications may require the deployment of gateways at the network edge to enable its interaction with physical sensors, pre-processing data from these sensors, and synchronizing it with the cloud. The orchestration, deployment, and maintenance of the software running on the gateways in large-scale deployments is known to be challenging. In this paper we analyze two virtualization techniques commonly used for deployment and management of applications at the cloud, but on the context of IoT and network edge. A study was conducted to evaluate the performance of the container-based approach compared to a hypervisor-based virtualization when running on devices typically used at the network edge. The study was performed through the execution of several synthetic benchmarks providing an insight in the performance overhead introduced by Docker containers (lightweight-virtualization) and KVM VMs (hypervisor-virtualization) running at network edge devices.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134256139","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523579
D. Bankov, E. Khorov, A. Lyakhov, S. Schelstraete
To address 5G challenges, IEEE 802.11 is currently developing new amendments to the Wi-Fi standard, the most promising of which is 802.11ax. A key scenario considered by the developers of this amendment is dense and overlapped networks typically present in residential buildings, offices, airports, stadiums, and other places of a modern city. Being crucial for Wi-Fi hotspots, the hidden station problem becomes even more challenging for dense and overlapped networks, where even access points (APs) can be hidden. In this case, user stations can experience continuous collisions of beacons sent by different APs, which can cause disassociation and break Internet access. In this paper, we show that beacon collisions are rather typical for residential networks and may lead to unexpected and irreproducible malfunction. We investigate how often beacon collisions occur, and describe a number of mechanisms which can be used to avoid beacon collisions in dense deployment. Specifically, we pay much attention to those mechanisms which are currently under consideration of the IEEE 802.11ax group.
{"title":"Beacons in dense Wi-Fi networks: How to befriend with neighbors in the 5G world?","authors":"D. Bankov, E. Khorov, A. Lyakhov, S. Schelstraete","doi":"10.1109/WoWMoM.2016.7523579","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523579","url":null,"abstract":"To address 5G challenges, IEEE 802.11 is currently developing new amendments to the Wi-Fi standard, the most promising of which is 802.11ax. A key scenario considered by the developers of this amendment is dense and overlapped networks typically present in residential buildings, offices, airports, stadiums, and other places of a modern city. Being crucial for Wi-Fi hotspots, the hidden station problem becomes even more challenging for dense and overlapped networks, where even access points (APs) can be hidden. In this case, user stations can experience continuous collisions of beacons sent by different APs, which can cause disassociation and break Internet access. In this paper, we show that beacon collisions are rather typical for residential networks and may lead to unexpected and irreproducible malfunction. We investigate how often beacon collisions occur, and describe a number of mechanisms which can be used to avoid beacon collisions in dense deployment. Specifically, we pay much attention to those mechanisms which are currently under consideration of the IEEE 802.11ax group.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132873182","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}
Pub Date : 2016-06-21DOI: 10.1109/WoWMoM.2016.7523502
L. Tian, J. Famaey, Steven Latré
IEEE 802.11ah is a new Wi-Fi draft for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT): connectivity among a large number of power-constrained stations deployed over a wide area. The new Restricted Access Window (RAW) mechanism promises to increase throughput and energy efficiency by dividing stations into different RAW groups. Only the stations in the same group can access the channel simultaneously, which reduces collision probability in dense scenarios. However, the draft does not specify any RAW grouping algorithms, while the grouping strategy is expected to severely impact RAW performance. To study the impact of parameters such as traffic load, number of stations and RAW group duration on optimal number of RAW groups, we implemented a sub-1Ghz PHY model and the 802.11ah MAC protocol in ns-3 to evaluate its transmission range, throughput, latency and energy efficiency in dense IoT network scenarios. The simulation shows that, with appropriate grouping, the RAW mechanism substantially improves throughput, latency and energy efficiency. Furthermore, the results suggest that the optimal grouping strategy depends on many parameters, and intelligent RAW group adaptation is necessary to maximize performance under dynamic conditions. This paper provides a major leap towards such a strategy.
{"title":"Evaluation of the IEEE 802.11ah Restricted Access Window mechanism for dense IoT networks","authors":"L. Tian, J. Famaey, Steven Latré","doi":"10.1109/WoWMoM.2016.7523502","DOIUrl":"https://doi.org/10.1109/WoWMoM.2016.7523502","url":null,"abstract":"IEEE 802.11ah is a new Wi-Fi draft for sub-1Ghz communications, aiming to address the major challenges of the Internet of Things (IoT): connectivity among a large number of power-constrained stations deployed over a wide area. The new Restricted Access Window (RAW) mechanism promises to increase throughput and energy efficiency by dividing stations into different RAW groups. Only the stations in the same group can access the channel simultaneously, which reduces collision probability in dense scenarios. However, the draft does not specify any RAW grouping algorithms, while the grouping strategy is expected to severely impact RAW performance. To study the impact of parameters such as traffic load, number of stations and RAW group duration on optimal number of RAW groups, we implemented a sub-1Ghz PHY model and the 802.11ah MAC protocol in ns-3 to evaluate its transmission range, throughput, latency and energy efficiency in dense IoT network scenarios. The simulation shows that, with appropriate grouping, the RAW mechanism substantially improves throughput, latency and energy efficiency. Furthermore, the results suggest that the optimal grouping strategy depends on many parameters, and intelligent RAW group adaptation is necessary to maximize performance under dynamic conditions. This paper provides a major leap towards such a strategy.","PeriodicalId":187747,"journal":{"name":"2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114860899","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}
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