Pub Date : 2019-10-01DOI: 10.23919/cnsm46954.2019.9012670
Edenilson Jônatas dos Passos, Adriano Fiorese
Recently, with popularisation of video streaming service, new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. Even so, with the limitation of the TCP/IP network structure, the end user Quality of Experience (QoE) may be affected. One issue that can affect user QoE is the workload of content distribution servers. Thus, the unbalancing of server’s workload comprising user’s attendance can lead to a content server provider non optimised choice. This work presents two load-balancing solutions between MPEG-DASH video servers based on Software-Defined Networks, using as a balancing workload metric the throughput of the content server as well as the CPU load.
{"title":"Janus - A Software-Defined Networking MPEG-DASH Video Streaming Load Balancer","authors":"Edenilson Jônatas dos Passos, Adriano Fiorese","doi":"10.23919/cnsm46954.2019.9012670","DOIUrl":"https://doi.org/10.23919/cnsm46954.2019.9012670","url":null,"abstract":"Recently, with popularisation of video streaming service, new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. Even so, with the limitation of the TCP/IP network structure, the end user Quality of Experience (QoE) may be affected. One issue that can affect user QoE is the workload of content distribution servers. Thus, the unbalancing of server’s workload comprising user’s attendance can lead to a content server provider non optimised choice. This work presents two load-balancing solutions between MPEG-DASH video servers based on Software-Defined Networks, using as a balancing workload metric the throughput of the content server as well as the CPU load.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130159542","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012682
A. Frimpong, H. Meer
Network virtualization is proposed in several research work as a means to overcome the ossification of the Internet. Its application relies on embedding algorithms to instantiate virtual networks on substrate infrastructures. Notably, those considered in the scope of traffic-engineering are developed to focus on efficient resource utilization with the aim of increasing the acceptance ratio of the algorithms. In this paper, a demand-based virtual link embedding approach for multi-service mapping in programmable industrial networks is proposed. The approach aims at increasing the overall acceptance ratio of virtual link embedding algorithms by increasing the acceptance of demand critical requests. The goal is achieved by minimizing the deviation between requested demands and the resources satisfying the demand. The approach, when analyzed against state-of-the-art shortest path approaches under the same simulation conditions, shows good results in terms of utilization of the network resources, acceptance of delay-critical traffic demands and overall acceptance ratio.
{"title":"DBvLEA: A Demand-Based Approach to Virtual Link Mapping for Multi-Service Industrial Applications","authors":"A. Frimpong, H. Meer","doi":"10.23919/CNSM46954.2019.9012682","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012682","url":null,"abstract":"Network virtualization is proposed in several research work as a means to overcome the ossification of the Internet. Its application relies on embedding algorithms to instantiate virtual networks on substrate infrastructures. Notably, those considered in the scope of traffic-engineering are developed to focus on efficient resource utilization with the aim of increasing the acceptance ratio of the algorithms. In this paper, a demand-based virtual link embedding approach for multi-service mapping in programmable industrial networks is proposed. The approach aims at increasing the overall acceptance ratio of virtual link embedding algorithms by increasing the acceptance of demand critical requests. The goal is achieved by minimizing the deviation between requested demands and the resources satisfying the demand. The approach, when analyzed against state-of-the-art shortest path approaches under the same simulation conditions, shows good results in terms of utilization of the network resources, acceptance of delay-critical traffic demands and overall acceptance ratio.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134482990","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012747
Duong Tuan Nguyen, Chuan Pham, K. Nguyen, M. Cheriet
The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, namely controller-switch assignment and Virtual Network Function (VNF) placement. Unlike prior works, our purpose is to jointly solve these two problems, accounting for the complex and counter-intuitive manner they are related to each other. We present a service chain aware framework (SACO) that enables the controller-switch association in a multi-controller network regarding the relationship of switches via their connected VNFs that implement service components of the chain. We also propose a model and formulate the joint optimization problem of dynamic controller-switch mapping and VNF allocation. We apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problem and employ the Markov approximation method to find a near-optimal solution. Simulation results show that our service chain aware approach improves the system cost up to 10 ~ 43% compared to the state-of-the-art solutions.
{"title":"SACO: A Service Chain Aware SDN Controller-Switch Mapping Framework","authors":"Duong Tuan Nguyen, Chuan Pham, K. Nguyen, M. Cheriet","doi":"10.23919/CNSM46954.2019.9012747","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012747","url":null,"abstract":"The emerging paradigm of Software Defined Network (SDN) and virtualization technology promises an efficient solution for network providers to deploy services. Adopting them not only facilitates network management but also helps reduce the cost of maintaining network infrastructure. However, despite these advantages, there are still obstacles that must be overcome before SDN and virtualization can advance to reality in industrial deployments. In this paper, we focus on two well-researched issues, namely controller-switch assignment and Virtual Network Function (VNF) placement. Unlike prior works, our purpose is to jointly solve these two problems, accounting for the complex and counter-intuitive manner they are related to each other. We present a service chain aware framework (SACO) that enables the controller-switch association in a multi-controller network regarding the relationship of switches via their connected VNFs that implement service components of the chain. We also propose a model and formulate the joint optimization problem of dynamic controller-switch mapping and VNF allocation. We apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problem and employ the Markov approximation method to find a near-optimal solution. Simulation results show that our service chain aware approach improves the system cost up to 10 ~ 43% compared to the state-of-the-art solutions.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"48 5 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128209345","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012663
Imane Oussakel, P. Owezarski, Pascal Berthou
In cellular networks, the emergence of machine communications such as connected vehicles increases the high demand of uplink transmissions, thus, degrading the quality of service per user equipment. Enforcing quality-of-service in such cellular network is challenging, as radio phenomena, as well as user (and their devices) mobility and dynamics, are uncontrolled. To solve this issue, estimating what the quality of transmissions will be in a short future for a connected user is essential. For that purpose, we argue that lower layer metrics are a key feature whose evolution can help predict the bandwidth that the considered connections can take advantage of in the following hundreds of milliseconds. The paper then describes how a 4G testbed has been deployed in order to investigate throughput prediction in uplink transmissions at a small time granularity of 100 ms. Based on lower layer metrics (physical and mac layers), the main supervised machine learning algorithms are used, such as Linear Regressor and Random Forest to predict the uplink received bandwidth in different radio phenomena environment. Hence, a deep investigation of the impact of radio issues on bandwidth prediction is conducted. Further, our evaluation shows that the prediction is highly accurate: at the time granularity of 100 ms, the average prediction error is in the range of 6% to 12% for all the scenarios we explored.
{"title":"Experimental Estimation of LTE-A Performance","authors":"Imane Oussakel, P. Owezarski, Pascal Berthou","doi":"10.23919/CNSM46954.2019.9012663","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012663","url":null,"abstract":"In cellular networks, the emergence of machine communications such as connected vehicles increases the high demand of uplink transmissions, thus, degrading the quality of service per user equipment. Enforcing quality-of-service in such cellular network is challenging, as radio phenomena, as well as user (and their devices) mobility and dynamics, are uncontrolled. To solve this issue, estimating what the quality of transmissions will be in a short future for a connected user is essential. For that purpose, we argue that lower layer metrics are a key feature whose evolution can help predict the bandwidth that the considered connections can take advantage of in the following hundreds of milliseconds. The paper then describes how a 4G testbed has been deployed in order to investigate throughput prediction in uplink transmissions at a small time granularity of 100 ms. Based on lower layer metrics (physical and mac layers), the main supervised machine learning algorithms are used, such as Linear Regressor and Random Forest to predict the uplink received bandwidth in different radio phenomena environment. Hence, a deep investigation of the impact of radio issues on bandwidth prediction is conducted. Further, our evaluation shows that the prediction is highly accurate: at the time granularity of 100 ms, the average prediction error is in the range of 6% to 12% for all the scenarios we explored.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881176","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012689
Fan Zhang, B. Butler, B. Jennings
As more and more services are deployed on devices near the network edge, security operations (such as authentication and authorization) need to move with them. Typically, edge devices have fewer resources than data center servers and so the security operations need to make more efficient use of what is available while offering adequate performance. Authorization adds latency and requires system resources, but the need for security management with strong authorization at the network edge is growing. We have released the first open source, high-performance, resource-efficient, XACML3 standard-compatible Policy Decision Point (PDP) called Luas (means “speed’' in the Irish language) based on an event-driven architecture and a non-blocking computational model, using a Bloom Filter for better performance. We compared its performance, resource usage and reliability against existing open source PDPs. Like those we tested, it provides accurate decisions, but Luas offers much faster security policy evaluation while using fewer system resources, and provides responses in a reasonable timeframe even when resources are scarce.
{"title":"An Access Control Implementation Targeting Resource-constrained Environments","authors":"Fan Zhang, B. Butler, B. Jennings","doi":"10.23919/CNSM46954.2019.9012689","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012689","url":null,"abstract":"As more and more services are deployed on devices near the network edge, security operations (such as authentication and authorization) need to move with them. Typically, edge devices have fewer resources than data center servers and so the security operations need to make more efficient use of what is available while offering adequate performance. Authorization adds latency and requires system resources, but the need for security management with strong authorization at the network edge is growing. We have released the first open source, high-performance, resource-efficient, XACML3 standard-compatible Policy Decision Point (PDP) called Luas (means “speed’' in the Irish language) based on an event-driven architecture and a non-blocking computational model, using a Bloom Filter for better performance. We compared its performance, resource usage and reliability against existing open source PDPs. Like those we tested, it provides accurate decisions, but Luas offers much faster security policy evaluation while using fewer system resources, and provides responses in a reasonable timeframe even when resources are scarce.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115498787","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012708
Pedro Ferreira, Duc C. Le, N. Zincir-Heywood
Insider threat is one of the most damaging cyber security attacks to companies and organizations. In this paper, we explore different techniques to leverage spatial and temporal characteristics of user behaviours for insider threat detection. In particular, feature normalization (scaling) techniques and a scheme for representing explicit temporal information are explored to improve the performance of the machine learning based insider threat detection. The results show that these data characteristics have different effects on different classifiers, where Standard Scaler with Random Forest classifier produces the best performance.
{"title":"Exploring Feature Normalization and Temporal Information for Machine Learning Based Insider Threat Detection","authors":"Pedro Ferreira, Duc C. Le, N. Zincir-Heywood","doi":"10.23919/CNSM46954.2019.9012708","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012708","url":null,"abstract":"Insider threat is one of the most damaging cyber security attacks to companies and organizations. In this paper, we explore different techniques to leverage spatial and temporal characteristics of user behaviours for insider threat detection. In particular, feature normalization (scaling) techniques and a scheme for representing explicit temporal information are explored to improve the performance of the machine learning based insider threat detection. The results show that these data characteristics have different effects on different classifiers, where Standard Scaler with Random Forest classifier produces the best performance.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019350","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012707
Ren Quinn, Nico Holguin, Ben Poster, Corey Roach, J. V. D. Merwe
Every day, university networks are bombarded with attempts to steal the sensitive data of the various disparate domains and organizations they serve. For this reason, universities form teams of information security specialists called a Security Operations Center (SOC) to manage the complex operations involved in monitoring and mitigating such attacks. When a suspicious event is identified, members of the SOC are tasked to understand the nature of the event in order to respond to any damage the attack might have caused. This process is defined by administrative policies which are often very high-level and rarely systematically defined. This impedes the implementation of generalized and automated event response solutions, leading to specific ad hoc solutions based primarily on human intuition and experience as well as immediate administrative priorities. These solutions are often fragile, highly specific, and more difficult to reuse in other scenarios.
{"title":"WASPP: Workflow Automation for Security Policy Procedures","authors":"Ren Quinn, Nico Holguin, Ben Poster, Corey Roach, J. V. D. Merwe","doi":"10.23919/CNSM46954.2019.9012707","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012707","url":null,"abstract":"Every day, university networks are bombarded with attempts to steal the sensitive data of the various disparate domains and organizations they serve. For this reason, universities form teams of information security specialists called a Security Operations Center (SOC) to manage the complex operations involved in monitoring and mitigating such attacks. When a suspicious event is identified, members of the SOC are tasked to understand the nature of the event in order to respond to any damage the attack might have caused. This process is defined by administrative policies which are often very high-level and rarely systematically defined. This impedes the implementation of generalized and automated event response solutions, leading to specific ad hoc solutions based primarily on human intuition and experience as well as immediate administrative priorities. These solutions are often fragile, highly specific, and more difficult to reuse in other scenarios.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332407","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012674
Godfrey Kibalya, J. Serrat, J. Gorricho, R. Pasquini, Haipeng Yao, Peiying Zhang
Network Function Virtualization (NFV) and Machine Learning (ML) are envisioned as possible techniques for the realization of a flexible and adaptive 5G network. ML will provide the network with experiential intelligence to forecast, adapt and recover from temporal network fluctuations. On the other hand, NFV will enable the deployment of slice instances meeting specific service requirements. Moreover, a single slice instance may require to be deployed across multiple substrate networks; however, existing works on multi-substrate Virtual Network Embedding fall short on addressing the realistic slice constraints such as delay, location, etc., hence they are not suited for applications transcending multiple domains. In this paper, we address the multi-substrate slicing problem in a coordinated manner, and we propose a Reinforcement Learning (RL) algorithm for partitioning the slice request to the different candidate substrate networks. Moreover, we consider realistic slice constraints such as delay, location, etc. Simulation results show that the RL approach results into a performance comparable to the combinatorial solution, with more than 99% of time saving for the processing of each request.
{"title":"A Reinforcement Learning Based Approach for 5G Network Slicing Across Multiple Domains","authors":"Godfrey Kibalya, J. Serrat, J. Gorricho, R. Pasquini, Haipeng Yao, Peiying Zhang","doi":"10.23919/CNSM46954.2019.9012674","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012674","url":null,"abstract":"Network Function Virtualization (NFV) and Machine Learning (ML) are envisioned as possible techniques for the realization of a flexible and adaptive 5G network. ML will provide the network with experiential intelligence to forecast, adapt and recover from temporal network fluctuations. On the other hand, NFV will enable the deployment of slice instances meeting specific service requirements. Moreover, a single slice instance may require to be deployed across multiple substrate networks; however, existing works on multi-substrate Virtual Network Embedding fall short on addressing the realistic slice constraints such as delay, location, etc., hence they are not suited for applications transcending multiple domains. In this paper, we address the multi-substrate slicing problem in a coordinated manner, and we propose a Reinforcement Learning (RL) algorithm for partitioning the slice request to the different candidate substrate networks. Moreover, we consider realistic slice constraints such as delay, location, etc. Simulation results show that the RL approach results into a performance comparable to the combinatorial solution, with more than 99% of time saving for the processing of each request.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659784","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012751
N. Kamiyama, A. Nakao
In many countries, mobile virtual network operators (MVNOs) provide mobile network services to users by leasing the wireless bandwidth from mobile network operators (MNOs). To attract many users and increase the number of subscribers, some MVNOs introduce the strategy called zero rating (ZR) which exempts traffic of specific content providers (CPs) from usage-based charging. The ZR differentiates traffic of specific CPs from that of other CPs, so the ZR violates the principle of network neutrality, and the ZR is prohibited in some countries. However, to clarify the desirable rules against the ZR, we need to analyze its impact on end users. In this paper, we investigate the charging strategy of ZR MVNOs by analyzing the price plans of major MVNOs in Japan. Moreover, we model the dynamics of the MVNO market consisting of low-price (LP) MVNOs and ZR MVNOs by the evolutionary game which can model the dynamics of social environment described by strategic distribution. We show that the MVNO market will be monopolized by MVNOs using either strategy, and the monthly fee of users will increase at the steady state. Therefore, we conclude that ZR MVNOs are required to introduce a service plan for users who do not benefit from the ZR.
{"title":"Analyzing Dynamics of MVNO Market Using Evolutionary Game","authors":"N. Kamiyama, A. Nakao","doi":"10.23919/CNSM46954.2019.9012751","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012751","url":null,"abstract":"In many countries, mobile virtual network operators (MVNOs) provide mobile network services to users by leasing the wireless bandwidth from mobile network operators (MNOs). To attract many users and increase the number of subscribers, some MVNOs introduce the strategy called zero rating (ZR) which exempts traffic of specific content providers (CPs) from usage-based charging. The ZR differentiates traffic of specific CPs from that of other CPs, so the ZR violates the principle of network neutrality, and the ZR is prohibited in some countries. However, to clarify the desirable rules against the ZR, we need to analyze its impact on end users. In this paper, we investigate the charging strategy of ZR MVNOs by analyzing the price plans of major MVNOs in Japan. Moreover, we model the dynamics of the MVNO market consisting of low-price (LP) MVNOs and ZR MVNOs by the evolutionary game which can model the dynamics of social environment described by strategic distribution. We show that the MVNO market will be monopolized by MVNOs using either strategy, and the monthly fee of users will increase at the steady state. Therefore, we conclude that ZR MVNOs are required to introduce a service plan for users who do not benefit from the ZR.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123475823","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 : 2019-10-01DOI: 10.23919/CNSM46954.2019.9012737
Hui Xiong, J. Ilow
To detect the position and the orientation of a mobile device within a Wireless Local Area Network (WLAN) covered by multiple access points (APs), the intrinsic properties of multiple-input multiple-output (MIMO) channels are used linking the received signal strength indicators (RSSIs) to the distance and exploiting the received signal correlation structures. Location and orientation fingerprinting is a map based positioning solution that stores for a given orientation past measurements of RSSIs at known reference/grid points in a database that is later used to localize a mobile device at an unknown location and with unknown orientation to the closest reference point. This paper focuses on processing the RSSI data vectors from multiple receiving antennas on a downlink by applying the core tools of Machine Learning (ML) classification methods to evaluate the effects of MIMO RSSI meta-data when capturing 802.11n/ac packets using commodity hardware. Specifically, the paper provides insights into the design of the overall location fingerprinting system operating with new WiFi physical link layer protocols. To verify the operation of the proposed system, experimental results are presented to investigate the impact of different factors, like the number of receive antennas, affecting the estimation accuracy for the location and the orientation of mobile user.
{"title":"Machine Learning for Location and Orientation Fingerprinting in MIMO WLANs","authors":"Hui Xiong, J. Ilow","doi":"10.23919/CNSM46954.2019.9012737","DOIUrl":"https://doi.org/10.23919/CNSM46954.2019.9012737","url":null,"abstract":"To detect the position and the orientation of a mobile device within a Wireless Local Area Network (WLAN) covered by multiple access points (APs), the intrinsic properties of multiple-input multiple-output (MIMO) channels are used linking the received signal strength indicators (RSSIs) to the distance and exploiting the received signal correlation structures. Location and orientation fingerprinting is a map based positioning solution that stores for a given orientation past measurements of RSSIs at known reference/grid points in a database that is later used to localize a mobile device at an unknown location and with unknown orientation to the closest reference point. This paper focuses on processing the RSSI data vectors from multiple receiving antennas on a downlink by applying the core tools of Machine Learning (ML) classification methods to evaluate the effects of MIMO RSSI meta-data when capturing 802.11n/ac packets using commodity hardware. Specifically, the paper provides insights into the design of the overall location fingerprinting system operating with new WiFi physical link layer protocols. To verify the operation of the proposed system, experimental results are presented to investigate the impact of different factors, like the number of receive antennas, affecting the estimation accuracy for the location and the orientation of mobile user.","PeriodicalId":273818,"journal":{"name":"2019 15th International Conference on Network and Service Management (CNSM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127867162","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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