The next generation of wireless networks, the 'fifth generation" or 5G, will have to cope with impressive new challenges. It includes a traffic expected to grow by up to 1000, an extremely low latency, the connection of cars, robots, smart cities, with billions of machines talking to each other and their sensors, new use of spectrum, new architectures and so on. The EU has committed €700 million of public funding over seven years to boost the research in 5G communications and a first wave of about 20 projects started this summer. The talk will address the scientific research challenges to develop 5G networks, the technology building blocks new projects are dealing with, notably as regards the Radio Access Network and the novel mobile architectures.
{"title":"European Research towards 5G","authors":"R. Bayou","doi":"10.1145/2789168.2802127","DOIUrl":"https://doi.org/10.1145/2789168.2802127","url":null,"abstract":"The next generation of wireless networks, the 'fifth generation\" or 5G, will have to cope with impressive new challenges. It includes a traffic expected to grow by up to 1000, an extremely low latency, the connection of cars, robots, smart cities, with billions of machines talking to each other and their sensors, new use of spectrum, new architectures and so on. The EU has committed €700 million of public funding over seven years to boost the research in 5G communications and a first wave of about 20 projects started this summer. The talk will address the scientific research challenges to develop 5G networks, the technology building blocks new projects are dealing with, notably as regards the Radio Access Network and the novel mobile architectures.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124878191","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}
Context-awareness is the ability of software systems to sense and adapt to their physical environment. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. In this framework, developers are required--only--to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context. CAreDroid is implemented as part of the Android runtime system. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. In particular, case study applications implemented using CAreDroid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs.
{"title":"CAreDroid: Adaptation Framework for Android Context-Aware Applications","authors":"Salma Elmalaki, L. Wanner, M. Srivastava","doi":"10.1145/2789168.2790108","DOIUrl":"https://doi.org/10.1145/2789168.2790108","url":null,"abstract":"Context-awareness is the ability of software systems to sense and adapt to their physical environment. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. In this framework, developers are required--only--to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context. CAreDroid is implemented as part of the Android runtime system. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. In particular, case study applications implemented using CAreDroid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599822","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}
Wen Wang, A. Liu, Muhammad Shahzad, Kang Ling, Sanglu Lu
Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.
{"title":"Understanding and Modeling of WiFi Signal Based Human Activity Recognition","authors":"Wen Wang, A. Liu, Muhammad Shahzad, Kang Ling, Sanglu Lu","doi":"10.1145/2789168.2790093","DOIUrl":"https://doi.org/10.1145/2789168.2790093","url":null,"abstract":"Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116748991","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}
Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.
{"title":"ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization","authors":"Jie Xiong, K. Sundaresan, K. Jamieson","doi":"10.1145/2789168.2790125","DOIUrl":"https://doi.org/10.1145/2789168.2790125","url":null,"abstract":"Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127059070","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}
Lixin Shi, Zachary Kabelac, D. Katabi, D. Perreault
Each year, consumers carry an increasing number of gadgets on their person: mobile phones, tablets, smartwatches, etc. As a result, users must remember to recharge each device, every day. Wireless charging promises to free users from this burden, allowing devices to remain permanently unplugged. Today's wireless charging, however, is either limited to a single device, or is highly cumbersome, requiring the user to remove all of her wearable and handheld gadgets and place them on a charging pad. This paper introduces MultiSpot, a new wireless charging technology that can charge multiple devices, even as the user is wearing them or carrying them in her pocket. A MultiSpot charger acts as an access point for wireless power. When a user enters the vicinity of the MultiSpot charger, all of her gadgets start to charge automatically. We have prototyped MultiSpot and evaluated it using off-the-shelf mobile phones, smartwatches, and tablets. Our results show that MultiSpot can charge 6 devices at distances of up to 50cm.
{"title":"Wireless Power Hotspot that Charges All of Your Devices","authors":"Lixin Shi, Zachary Kabelac, D. Katabi, D. Perreault","doi":"10.1145/2789168.2790092","DOIUrl":"https://doi.org/10.1145/2789168.2790092","url":null,"abstract":"Each year, consumers carry an increasing number of gadgets on their person: mobile phones, tablets, smartwatches, etc. As a result, users must remember to recharge each device, every day. Wireless charging promises to free users from this burden, allowing devices to remain permanently unplugged. Today's wireless charging, however, is either limited to a single device, or is highly cumbersome, requiring the user to remove all of her wearable and handheld gadgets and place them on a charging pad. This paper introduces MultiSpot, a new wireless charging technology that can charge multiple devices, even as the user is wearing them or carrying them in her pocket. A MultiSpot charger acts as an access point for wireless power. When a user enters the vicinity of the MultiSpot charger, all of her gadgets start to charge automatically. We have prototyped MultiSpot and evaluated it using off-the-shelf mobile phones, smartwatches, and tablets. Our results show that MultiSpot can charge 6 devices at distances of up to 50cm.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115400418","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}
Ju Wang, Dingyi Fang, Xiaojiang Chen, Liqiong Chang, Zhanyong Tang, Tianzhang Xing, Chen Liu
For a long history, stampede is one of the high potential disaster when thousands of people gathered. Current monitoring systems, however, can only detect the presence of a small number of sparsely located targets, rather than to monitor the change of people flow where there are large number of dense crowd in the environment. This paper presents DanSen, a low-cost people flow monitoring system for sensing the potential danger using the existing wifi infrastructures. Inspired by the dynamic light scattering (DLS) theory, the designed DanSen calculates the correlations between the initial channel state information (CSI) data and all the history CSI data to monitor the changes of people flow and also estimates the sharpness of the changes. By doing so, DanSen can be utilised to perceive the potential danger. Real-world experimental results illustrate the advantage and effectiveness of DanSen.
{"title":"Poster: A Low Cost People Flow Monitoring System For Sensing The Potential Danger","authors":"Ju Wang, Dingyi Fang, Xiaojiang Chen, Liqiong Chang, Zhanyong Tang, Tianzhang Xing, Chen Liu","doi":"10.1145/2789168.2795169","DOIUrl":"https://doi.org/10.1145/2789168.2795169","url":null,"abstract":"For a long history, stampede is one of the high potential disaster when thousands of people gathered. Current monitoring systems, however, can only detect the presence of a small number of sparsely located targets, rather than to monitor the change of people flow where there are large number of dense crowd in the environment. This paper presents DanSen, a low-cost people flow monitoring system for sensing the potential danger using the existing wifi infrastructures. Inspired by the dynamic light scattering (DLS) theory, the designed DanSen calculates the correlations between the initial channel state information (CSI) data and all the history CSI data to monitor the changes of people flow and also estimates the sharpness of the changes. By doing so, DanSen can be utilised to perceive the potential danger. Real-world experimental results illustrate the advantage and effectiveness of DanSen.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121363235","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}
Tan Zhang, Aakanksha Chowdhery, P. Bahl, K. Jamieson, Suman Banerjee
Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigil's bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user's query.
{"title":"The Design and Implementation of a Wireless Video Surveillance System","authors":"Tan Zhang, Aakanksha Chowdhery, P. Bahl, K. Jamieson, Suman Banerjee","doi":"10.1145/2789168.2790123","DOIUrl":"https://doi.org/10.1145/2789168.2790123","url":null,"abstract":"Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigil's bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user's query.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122411138","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}
Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze and address the synchronization (sync) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstance even though the incremental sync is implemented. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 52.9% sync time in our experiment settings.
{"title":"QuickSync: Improving Synchronization Efficiency for Mobile Cloud Storage Services","authors":"Yong Cui, Zeqi Lai, Xin Wang, Ningwei Dai, Congcong Miao","doi":"10.1145/2789168.2790094","DOIUrl":"https://doi.org/10.1145/2789168.2790094","url":null,"abstract":"Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze and address the synchronization (sync) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstance even though the incremental sync is implemented. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 52.9% sync time in our experiment settings.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124840110","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}
A. Ding, Yanhe Liu, S. Tarkoma, H. Flinck, J. Crowcroft
This demonstration presents a novel software defined platform for achieving collaborative and energy-aware WiFi offloading. The platform consists of an extensible central controller, programmable offloading agents, and offloading extensions on mobile devices. Driven by our extensive measurements of energy consumption on smartphones, we propose an effective energy-aware offloading algorithm and integrate it to our platform. By enabling collaboration between wireless networks and mobile users, our solution can make optimal offloading decisions that improve offloading efficiency for network operators and achieve energy saving for mobile users. To enhance deployability, we have released our platform under open-source licenses on GitHub.
{"title":"Demo: An Open-source Software Defined Platform for Collaborative and Energy-aware WiFi Offloading","authors":"A. Ding, Yanhe Liu, S. Tarkoma, H. Flinck, J. Crowcroft","doi":"10.1145/2789168.2789174","DOIUrl":"https://doi.org/10.1145/2789168.2789174","url":null,"abstract":"This demonstration presents a novel software defined platform for achieving collaborative and energy-aware WiFi offloading. The platform consists of an extensible central controller, programmable offloading agents, and offloading extensions on mobile devices. Driven by our extensive measurements of energy consumption on smartphones, we propose an effective energy-aware offloading algorithm and integrate it to our platform. By enabling collaboration between wireless networks and mobile users, our solution can make optimal offloading decisions that improve offloading efficiency for network operators and achieve energy saving for mobile users. To enhance deployability, we have released our platform under open-source licenses on GitHub.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121783069","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}
Hongyi Yao, Gyan Ranjan, A. Tongaonkar, Yong Liao, Z. Morley Mao
We present SAMPLES: Self Adaptive Mining of Persistent LExical Snippets; a systematic framework for classifying network traffic generated by mobile applications. SAMPLES constructs conjunctive rules, in an automated fashion, through a supervised methodology over a set of labeled flows (the training set). Each conjunctive rule corresponds to the lexical context, associated with an application identifier found in a snippet of the HTTP header, and is defined by: (a) the identifier type, (b) the HTTP header-field it occurs in, and (c) the prefix/suffix surrounding its occurrence. Subsequently, these conjunctive rules undergo an aggregate-and-validate step for improving accuracy and determining a priority order. The refined rule-set is then loaded into an application-identification engine where it operates at a per flow granularity, in an extract-and-lookup paradigm, to identify the application responsible for a given flow. Thus, SAMPLES can facilitate important network measurement and management tasks --- e.g. behavioral profiling [29], application-level firewalls [21,22] etc. --- which require a more detailed view of the underlying traffic than that afforded by traditional protocol/port based methods. We evaluate SAMPLES on a test set comprising 15 million flows (approx.) generated by over 700 K applications from the Android, iOS and Nokia market-places. SAMPLES successfully identifies over 90% of these applications with 99% accuracy on an average. This, in spite of the fact that fewer than 2% of the applications are required during the training phase, for each of the three market places. This is a testament to the universality and the scalability of our approach. We, therefore, expect SAMPLES to work with reasonable coverage and accuracy for other mobile platforms --- e.g. BlackBerry and Windows Mobile --- as well.
{"title":"SAMPLES: Self Adaptive Mining of Persistent LExical Snippets for Classifying Mobile Application Traffic","authors":"Hongyi Yao, Gyan Ranjan, A. Tongaonkar, Yong Liao, Z. Morley Mao","doi":"10.1145/2789168.2790097","DOIUrl":"https://doi.org/10.1145/2789168.2790097","url":null,"abstract":"We present SAMPLES: Self Adaptive Mining of Persistent LExical Snippets; a systematic framework for classifying network traffic generated by mobile applications. SAMPLES constructs conjunctive rules, in an automated fashion, through a supervised methodology over a set of labeled flows (the training set). Each conjunctive rule corresponds to the lexical context, associated with an application identifier found in a snippet of the HTTP header, and is defined by: (a) the identifier type, (b) the HTTP header-field it occurs in, and (c) the prefix/suffix surrounding its occurrence. Subsequently, these conjunctive rules undergo an aggregate-and-validate step for improving accuracy and determining a priority order. The refined rule-set is then loaded into an application-identification engine where it operates at a per flow granularity, in an extract-and-lookup paradigm, to identify the application responsible for a given flow. Thus, SAMPLES can facilitate important network measurement and management tasks --- e.g. behavioral profiling [29], application-level firewalls [21,22] etc. --- which require a more detailed view of the underlying traffic than that afforded by traditional protocol/port based methods. We evaluate SAMPLES on a test set comprising 15 million flows (approx.) generated by over 700 K applications from the Android, iOS and Nokia market-places. SAMPLES successfully identifies over 90% of these applications with 99% accuracy on an average. This, in spite of the fact that fewer than 2% of the applications are required during the training phase, for each of the three market places. This is a testament to the universality and the scalability of our approach. We, therefore, expect SAMPLES to work with reasonable coverage and accuracy for other mobile platforms --- e.g. BlackBerry and Windows Mobile --- as well.","PeriodicalId":424497,"journal":{"name":"Proceedings of the 21st Annual International Conference on Mobile Computing and Networking","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126848261","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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