Rodrigo Teles Hermeto, A. Gallais, Fabrice Théoleyre
Industrial networks differ from others kinds of networks because they require real-time performance in order to meet strict requirements. With the rise of low-power wireless standards, the industrial applications have started to use wireless communications in order to reduce deployment and management costs. IEEE802.15.4-TSCH represents currently a promising standard relying on a strict schedule of the transmissions to provide strong guarantees. However, the radio environment still exhibits time-variable characteristics. Thus, the network has to provision sufficient resource (bandwidth) to cope with the worst case while still achieving high energy efficiency. The 6TiSCH IETF working group defines a stack to tune dynamically the TSCH schedule. In this paper, we analyze in depth the stability and the convergence of a 6TiSCH network in an indoor testbed. We identify the main causes of instabilities, and we propose solutions to address each of them. We show that our solutions improve significantly the stability.
{"title":"On the (over)-Reactions and the Stability of a 6TiSCH Network in an Indoor Environment","authors":"Rodrigo Teles Hermeto, A. Gallais, Fabrice Théoleyre","doi":"10.1145/3242102.3242104","DOIUrl":"https://doi.org/10.1145/3242102.3242104","url":null,"abstract":"Industrial networks differ from others kinds of networks because they require real-time performance in order to meet strict requirements. With the rise of low-power wireless standards, the industrial applications have started to use wireless communications in order to reduce deployment and management costs. IEEE802.15.4-TSCH represents currently a promising standard relying on a strict schedule of the transmissions to provide strong guarantees. However, the radio environment still exhibits time-variable characteristics. Thus, the network has to provision sufficient resource (bandwidth) to cope with the worst case while still achieving high energy efficiency. The 6TiSCH IETF working group defines a stack to tune dynamically the TSCH schedule. In this paper, we analyze in depth the stability and the convergence of a 6TiSCH network in an indoor testbed. We identify the main causes of instabilities, and we propose solutions to address each of them. We show that our solutions improve significantly the stability.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126832272","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}
To achieve device-free person detection, various types of signal features, such as moving statistics and wavelet representations, have been extracted from the Wi-Fi Received Signal Strength Index (RSSI), whose value fluctuates when human subjects move near the Wi-Fi transceivers. However, these features do not work effectively under different deployments of Wi-Fi transceivers because each transceiver has a unique RSSI fluctuation pattern that depends on its specific wireless channel and hardware characteristics. To address this problem, we present WiDet, a system that uses a deep Convolutional Neural Network (CNN) approach for person detection. The CNN achieves effective and robust detection feature extraction by exploring distinguishable patterns in Wi-Fi RSSI data. With a large number of internal parameters, the CNN can record and recognize the different RSSI fluctuation patterns from different transceivers. We further apply the data augmentation method to improve the algorithm robustness to wireless interferences and pedestrian speed changes. To take advantage of the wide availability of the existing Wi-Fi devices, we design a collaborative sensing technique that can recognize the subject moving directions. To validate the proposed design, we implement a prototype system that consists of three Wi-Fi packet transmitters and one receiver on low-cost off-the-shelf embedded development boards. In a multi-day experiment with a total of 163 walking events, WiDet achieves 94.5% of detection accuracy in detecting pedestrians, which outperforms the moving statistics and the wavelet representation based approaches by 22% and 8%, respectively.
{"title":"WiDet","authors":"Hua Huang, Shane Lin","doi":"10.1145/3242102.3242119","DOIUrl":"https://doi.org/10.1145/3242102.3242119","url":null,"abstract":"To achieve device-free person detection, various types of signal features, such as moving statistics and wavelet representations, have been extracted from the Wi-Fi Received Signal Strength Index (RSSI), whose value fluctuates when human subjects move near the Wi-Fi transceivers. However, these features do not work effectively under different deployments of Wi-Fi transceivers because each transceiver has a unique RSSI fluctuation pattern that depends on its specific wireless channel and hardware characteristics. To address this problem, we present WiDet, a system that uses a deep Convolutional Neural Network (CNN) approach for person detection. The CNN achieves effective and robust detection feature extraction by exploring distinguishable patterns in Wi-Fi RSSI data. With a large number of internal parameters, the CNN can record and recognize the different RSSI fluctuation patterns from different transceivers. We further apply the data augmentation method to improve the algorithm robustness to wireless interferences and pedestrian speed changes. To take advantage of the wide availability of the existing Wi-Fi devices, we design a collaborative sensing technique that can recognize the subject moving directions. To validate the proposed design, we implement a prototype system that consists of three Wi-Fi packet transmitters and one receiver on low-cost off-the-shelf embedded development boards. In a multi-day experiment with a total of 163 walking events, WiDet achieves 94.5% of detection accuracy in detecting pedestrians, which outperforms the moving statistics and the wavelet representation based approaches by 22% and 8%, respectively.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122614941","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}
Modeling mobility is a key aspect when simulating different types of networks. To cater to this requirement, a large number of models has emerged in the last years. They are typically (a) trace-based, where GPS recordings are re-run in simulation, (b) synthetic models, which describe mobility with formal methods, or (c) hybrid models, which are synthetic models based on statistically evaluated traces. All these families of models have advantages and disadvantages. For example, trace-based models are very inflexible in terms of simulation scenarios, but have realistic behaviour, while synthetic models are very flexible, but lack realism. In this paper, we propose a new mobility model, called TRAILS (TRAce-based ProbabILiStic Mobility Model), which bridges the gap between these families and combines their advantages into a single model. The main idea is to extract a mobility graph from real traces and to use it in simulation to create scalable, flexible simulation scenarios. We show that TRAILS is more realistic than synthetic models, while achieving full scalability and flexibility. We have implemented and evaluated TRAILS in the OMNeT++ simulation framework.
{"title":"TRAILS - A Trace-Based Probabilistic Mobility Model","authors":"Anna Förster, Anas Bin Muslim, A. Udugama","doi":"10.1145/3242102.3242134","DOIUrl":"https://doi.org/10.1145/3242102.3242134","url":null,"abstract":"Modeling mobility is a key aspect when simulating different types of networks. To cater to this requirement, a large number of models has emerged in the last years. They are typically (a) trace-based, where GPS recordings are re-run in simulation, (b) synthetic models, which describe mobility with formal methods, or (c) hybrid models, which are synthetic models based on statistically evaluated traces. All these families of models have advantages and disadvantages. For example, trace-based models are very inflexible in terms of simulation scenarios, but have realistic behaviour, while synthetic models are very flexible, but lack realism. In this paper, we propose a new mobility model, called TRAILS (TRAce-based ProbabILiStic Mobility Model), which bridges the gap between these families and combines their advantages into a single model. The main idea is to extract a mobility graph from real traces and to use it in simulation to create scalable, flexible simulation scenarios. We show that TRAILS is more realistic than synthetic models, while achieving full scalability and flexibility. We have implemented and evaluated TRAILS in the OMNeT++ simulation framework.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129460973","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}
P. Bellavista, L. Foschini, S. Luciano, M. Solimando
The widespread exploitation of cloud technologies forces cloud providers to forecast peaks of requests to guarantee always the adequate quality of service to their currently served customers. Static resource provisioning is rarely affordable for large Data Center Networks (DCNs) and dynamic resource management can be rather complex, in particular for networking. Hence, we claim the relevance of simulator-based approaches, helpful in planning DCN deployment and in analyzing performance behaviors in response to expected traffic patterns. However, existing cloud simulators exhibit non-negligible limitations for what relates to the modeling of networking issues of cloud Infrastructure as a Service (IaaS) deployments. Therefore, we propose DCNs-2, a novel extension package for the ns-2 simulator, as a valid solution to efficiently simulate DCNs with all their primary entities, such as switches, physical machines, racks, virtual machines, and so on.
{"title":"DCNs-2: A Cloud Network Simulator Extension for ns-2","authors":"P. Bellavista, L. Foschini, S. Luciano, M. Solimando","doi":"10.1145/3242102.3242136","DOIUrl":"https://doi.org/10.1145/3242102.3242136","url":null,"abstract":"The widespread exploitation of cloud technologies forces cloud providers to forecast peaks of requests to guarantee always the adequate quality of service to their currently served customers. Static resource provisioning is rarely affordable for large Data Center Networks (DCNs) and dynamic resource management can be rather complex, in particular for networking. Hence, we claim the relevance of simulator-based approaches, helpful in planning DCN deployment and in analyzing performance behaviors in response to expected traffic patterns. However, existing cloud simulators exhibit non-negligible limitations for what relates to the modeling of networking issues of cloud Infrastructure as a Service (IaaS) deployments. Therefore, we propose DCNs-2, a novel extension package for the ns-2 simulator, as a valid solution to efficiently simulate DCNs with all their primary entities, such as switches, physical machines, racks, virtual machines, and so on.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130337297","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}
In the future, libraries and warehouses will gain benefits from the spatial location of books and merchandises attached with RFID tags. Existing localization algorithms, however, usually focus on improving positioning accuracy or the ordering one for RFID tags on the same layer. Nevertheless, books or merchandises are placed on the multilayer in reality and the layer of RFID tagged object is also an important position indication. To this end, we design PRMS, an RFID based localization system which utilizes both phase and RSSI values of the backscattered signal provided by a single antenna to estimate the spatial position for RFID tags. Our basic idea is to gain initial estimated locations of RFID tags through a basic model which extracts the phase differences between received signals to locate tags. Then an advanced model is proposed to improve the positioning accuracy combined with RF hologram based on basic model. We further change traditional deployment of a single antenna to distinguish the features of RFID tags on multilayer and adopt a machine learning algorithm to get the layer information of tagged objects. The experiment results show that the average accuracy of layer detection and sorting at low tag spacing ($2sim8$cm) are about 93% and 84% respectively.
{"title":"PRMS","authors":"Huatao Xu, Run Zhao, Qian Zhang, Dong Wang","doi":"10.1145/3242102.3242138","DOIUrl":"https://doi.org/10.1145/3242102.3242138","url":null,"abstract":"In the future, libraries and warehouses will gain benefits from the spatial location of books and merchandises attached with RFID tags. Existing localization algorithms, however, usually focus on improving positioning accuracy or the ordering one for RFID tags on the same layer. Nevertheless, books or merchandises are placed on the multilayer in reality and the layer of RFID tagged object is also an important position indication. To this end, we design PRMS, an RFID based localization system which utilizes both phase and RSSI values of the backscattered signal provided by a single antenna to estimate the spatial position for RFID tags. Our basic idea is to gain initial estimated locations of RFID tags through a basic model which extracts the phase differences between received signals to locate tags. Then an advanced model is proposed to improve the positioning accuracy combined with RF hologram based on basic model. We further change traditional deployment of a single antenna to distinguish the features of RFID tags on multilayer and adopt a machine learning algorithm to get the layer information of tagged objects. The experiment results show that the average accuracy of layer detection and sorting at low tag spacing ($2sim8$cm) are about 93% and 84% respectively.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122243875","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}
Carlos Herranz, Menglei Zhang, M. Mezzavilla, David Martín-Sacristán, S. Rangan, J. Monserrat
The advent of the next iteration of mobile and wireless communication standards, the so called 5G, is already a reality. 3GPP released in December 2017 the first set of specifications of the 5G New Radio (NR), which introduced important innovations with respect to legacy networks. One of the main novelties is the use of very-high frequencies in the radio access, which requires highly-directional transmissions or beams to overcome the severe propagation losses. Therefore, it is paramount to manage these beams in an efficient manner in order to always choose the optimum set of beams. In this work, we describe the first NR-compliant beam management framework for the ns-3 network simulator. We aim at providing an open-source and fully-customizable solution to let the scientific community implement their solutions and assess their impact on the end-to-end network performance. Additionally, we describe the necessary modifications in ns-3 to align the radio frame structure to what the 3GPP standards mandate. Finally, we validate our results by running a simple mobility scenario.
{"title":"A 3GPP NR Compliant Beam Management Framework to Simulate End-to-End mmWave Networks","authors":"Carlos Herranz, Menglei Zhang, M. Mezzavilla, David Martín-Sacristán, S. Rangan, J. Monserrat","doi":"10.1145/3242102.3242117","DOIUrl":"https://doi.org/10.1145/3242102.3242117","url":null,"abstract":"The advent of the next iteration of mobile and wireless communication standards, the so called 5G, is already a reality. 3GPP released in December 2017 the first set of specifications of the 5G New Radio (NR), which introduced important innovations with respect to legacy networks. One of the main novelties is the use of very-high frequencies in the radio access, which requires highly-directional transmissions or beams to overcome the severe propagation losses. Therefore, it is paramount to manage these beams in an efficient manner in order to always choose the optimum set of beams. In this work, we describe the first NR-compliant beam management framework for the ns-3 network simulator. We aim at providing an open-source and fully-customizable solution to let the scientific community implement their solutions and assess their impact on the end-to-end network performance. Additionally, we describe the necessary modifications in ns-3 to align the radio frame structure to what the 3GPP standards mandate. Finally, we validate our results by running a simple mobility scenario.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130882998","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}
Cellular networks are susceptible to being severely capacity-constrained during peak traffic hours or at special events such as sports and concerts. Many other applications are emerging for LTE and 5G networks that inject machine-to-machine (M2M) communications for Internet of Things (IoT) devices that sense the environment and react to diurnal patterns observed. Both for users and devices, the high congestion levels frequently lead to numerous retransmissions and severe battery depletion. However, there are frequently social cues that could be gleaned from interactions from websites and social networks of shared interest to a particular region at a particular time. Cellular network operators have sought to address these high levels of fluctuations and traffic burstiness via the use of offloading to unlicensed bands, which may be instructed by these social cues. In this paper, we leverage shared interest information in a given area to conserve power via the use of offloading to the emerging Citizens Radio Band Service (CBRS). Our GreenLoading framework enables hierarchical data delivery to significantly reduce power consumption and includes a Broker Priority Assignment (BPA) algorithm to select data brokers for users. With the use of in-field measurements and web-based Google data across four diverse U.S. cities, we show that, on average, an order of magnitude power savings via GreenLoading over a 24-hour period and up to 35 times at peak traffic times. Finally, we consider the role that a relaxation of wait times can play in the power efficiency of a GreenLoaded network.
{"title":"GreenLoading: Using the Citizens Band Radio for Energy-Efficient Offloading of Shared Interests","authors":"Pengfei Cui, S. Chen, J. Camp","doi":"10.1145/3242102.3242113","DOIUrl":"https://doi.org/10.1145/3242102.3242113","url":null,"abstract":"Cellular networks are susceptible to being severely capacity-constrained during peak traffic hours or at special events such as sports and concerts. Many other applications are emerging for LTE and 5G networks that inject machine-to-machine (M2M) communications for Internet of Things (IoT) devices that sense the environment and react to diurnal patterns observed. Both for users and devices, the high congestion levels frequently lead to numerous retransmissions and severe battery depletion. However, there are frequently social cues that could be gleaned from interactions from websites and social networks of shared interest to a particular region at a particular time. Cellular network operators have sought to address these high levels of fluctuations and traffic burstiness via the use of offloading to unlicensed bands, which may be instructed by these social cues. In this paper, we leverage shared interest information in a given area to conserve power via the use of offloading to the emerging Citizens Radio Band Service (CBRS). Our GreenLoading framework enables hierarchical data delivery to significantly reduce power consumption and includes a Broker Priority Assignment (BPA) algorithm to select data brokers for users. With the use of in-field measurements and web-based Google data across four diverse U.S. cities, we show that, on average, an order of magnitude power savings via GreenLoading over a 24-hour period and up to 35 times at peak traffic times. Finally, we consider the role that a relaxation of wait times can play in the power efficiency of a GreenLoaded network.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129182680","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}
Recent work shows that an adversary can exploit a coupling effect induced by hidden nodes to launch a cascading attack causing global congestion in a Wi-Fi network. The underlying assumption is that the power of interference caused by a hidden node is an order of magnitude stronger than the signal sent to the receiver. In this paper, we investigate the feasibility of cascading attacks with weakly interfering hidden nodes, that is when the signal-to-interference ratio is high. Through extensive ns-3 simulations, including for an indoor building model, we show that cascading attacks are still feasible. The attacks leverage two PHY-layer phenomena: receiver capture and bit rate adaptation. We show that the attack relies on a coupling effect, whereby the average bit rate of a transmission pair drops sharply as the channel utilization of a neighboring pair gets higher. This coupling effect facilitates the propagation of the attack throughout the network.
{"title":"Cascading Attacks on Wi-Fi Networks with Weak Interferers","authors":"Liangxiao Xin, D. Starobinski","doi":"10.1145/3242102.3242142","DOIUrl":"https://doi.org/10.1145/3242102.3242142","url":null,"abstract":"Recent work shows that an adversary can exploit a coupling effect induced by hidden nodes to launch a cascading attack causing global congestion in a Wi-Fi network. The underlying assumption is that the power of interference caused by a hidden node is an order of magnitude stronger than the signal sent to the receiver. In this paper, we investigate the feasibility of cascading attacks with weakly interfering hidden nodes, that is when the signal-to-interference ratio is high. Through extensive ns-3 simulations, including for an indoor building model, we show that cascading attacks are still feasible. The attacks leverage two PHY-layer phenomena: receiver capture and bit rate adaptation. We show that the attack relies on a coupling effect, whereby the average bit rate of a transmission pair drops sharply as the channel utilization of a neighboring pair gets higher. This coupling effect facilitates the propagation of the attack throughout the network.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126354769","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}
Vipin N. Sathi, Manikantan Srinivasan, P. Thiruvasagam, C. Murthy
Slicing of a 5G network by creating virtualized instances of network functions facilitates the support of different service types with varying requirements. The management and orchestration layer identifies the components in the virtualization infrastructure to form an end-to-end slice for an intended service type. The key security challenges for the softwarized 5G networks are, (i) ensuring availability of a centralized controller/orchestrator, (ii) association between legitimate network slice components, and (iii) network slice isolation. To address these challenges, in this paper, we propose a novel implicit mutual authentication and key establishment with group anonymity protocol using proxy re-encryption on elliptic curve. The protocol provides (i) controller independent distributed association between components of a network slice, (ii) implicit authentication between network slice components to allow secure association, (iii) secure key establishment between component pairs for secure slice isolation, and (iv) service group anonymity. The proposed protocol's robustness is validated with necessary security analysis. The computation and bandwidth overheads of the proposed protocol are compared with that of the certificate based protocol, and our proposed protocol has 9.52% less computation overhead and 13.64% less bandwidth overhead for Type A1 pairing.
{"title":"A Novel Protocol for Securing Network Slice Component Association and Slice Isolation in 5G Networks","authors":"Vipin N. Sathi, Manikantan Srinivasan, P. Thiruvasagam, C. Murthy","doi":"10.1145/3242102.3242135","DOIUrl":"https://doi.org/10.1145/3242102.3242135","url":null,"abstract":"Slicing of a 5G network by creating virtualized instances of network functions facilitates the support of different service types with varying requirements. The management and orchestration layer identifies the components in the virtualization infrastructure to form an end-to-end slice for an intended service type. The key security challenges for the softwarized 5G networks are, (i) ensuring availability of a centralized controller/orchestrator, (ii) association between legitimate network slice components, and (iii) network slice isolation. To address these challenges, in this paper, we propose a novel implicit mutual authentication and key establishment with group anonymity protocol using proxy re-encryption on elliptic curve. The protocol provides (i) controller independent distributed association between components of a network slice, (ii) implicit authentication between network slice components to allow secure association, (iii) secure key establishment between component pairs for secure slice isolation, and (iv) service group anonymity. The proposed protocol's robustness is validated with necessary security analysis. The computation and bandwidth overheads of the proposed protocol are compared with that of the certificate based protocol, and our proposed protocol has 9.52% less computation overhead and 13.64% less bandwidth overhead for Type A1 pairing.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114013","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}
Muhammad Jawad Khokhar, Thierry Spetebroot, C. Barakat
Video streaming is a dominant contributor to the global Internet traffic. Consequently, gauging network performance w.r.t. the video Quality of Experience (QoE) is of paramount importance to both telecom operators and regulators. Modern video streaming systems, e.g. YouTube, have huge catalogs of billions of different videos that vary significantly in content type. Owing to this difference, the QoE of different videos as perceived by end users can vary for the same network Quality of Service (QoS). In this paper, we present a methodology for benchmarking performance of mobile operators w.r.t Internet video that considers this variation in QoE. We take a data-driven approach to build a predictive model using supervised machine learning (ML) that takes into account a wide range of videos and network conditions. To that end, we first build and analyze a large catalog of YouTube videos. We then propose and demonstrate a framework of controlled experimentation based on active learning to build the training data for the targeted ML model. Using this model, we then devise YouScore, an estimate of the percentage of YouTube videos that may play out smoothly under a given network condition. Finally, to demonstrate the benchmarking utility of YouScore, we apply it on an open dataset of real user mobile network measurements to compare performance of mobile operators for video streaming.
{"title":"A Methodology for Performance Benchmarking of Mobile Networks for Internet Video Streaming","authors":"Muhammad Jawad Khokhar, Thierry Spetebroot, C. Barakat","doi":"10.1145/3242102.3242128","DOIUrl":"https://doi.org/10.1145/3242102.3242128","url":null,"abstract":"Video streaming is a dominant contributor to the global Internet traffic. Consequently, gauging network performance w.r.t. the video Quality of Experience (QoE) is of paramount importance to both telecom operators and regulators. Modern video streaming systems, e.g. YouTube, have huge catalogs of billions of different videos that vary significantly in content type. Owing to this difference, the QoE of different videos as perceived by end users can vary for the same network Quality of Service (QoS). In this paper, we present a methodology for benchmarking performance of mobile operators w.r.t Internet video that considers this variation in QoE. We take a data-driven approach to build a predictive model using supervised machine learning (ML) that takes into account a wide range of videos and network conditions. To that end, we first build and analyze a large catalog of YouTube videos. We then propose and demonstrate a framework of controlled experimentation based on active learning to build the training data for the targeted ML model. Using this model, we then devise YouScore, an estimate of the percentage of YouTube videos that may play out smoothly under a given network condition. Finally, to demonstrate the benchmarking utility of YouScore, we apply it on an open dataset of real user mobile network measurements to compare performance of mobile operators for video streaming.","PeriodicalId":241359,"journal":{"name":"Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125008714","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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