Luis Corral, Ilenia Fronza, Nabil El Ioini, Andrea Janes, Peter Plant
In this paper, we present our experience designing and testing anenergy saving strategy for mobile phones, implemented atoperating system level, using Android OS. Our approach was todeploy kernel extensions that assess the status of the device, andenable economic profiles without user intervention. Ourexperiments showed that the power management kernel extensionwas able to extend the battery runtime by 70% to 75%, at theexpense of impacting the experience of the user with an estimated performance degradation of 20% to 30%.
{"title":"Preserving Energy Resources Using an Android Kernel Extension: A Case Study","authors":"Luis Corral, Ilenia Fronza, Nabil El Ioini, Andrea Janes, Peter Plant","doi":"10.1145/2897073.2897124","DOIUrl":"https://doi.org/10.1145/2897073.2897124","url":null,"abstract":"In this paper, we present our experience designing and testing anenergy saving strategy for mobile phones, implemented atoperating system level, using Android OS. Our approach was todeploy kernel extensions that assess the status of the device, andenable economic profiles without user intervention. Ourexperiments showed that the power management kernel extensionwas able to extend the battery runtime by 70% to 75%, at theexpense of impacting the experience of the user with an estimated performance degradation of 20% to 30%.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114448228","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}
This paper proposes a novel cloud testing platform specialized for software testing. Our novel approach aims to perform dynamic analysis on mobile application binaries, generate the model of the application, its test cases and test input sets on the run. Domain information generated via dynamic analysis and utilization of combinatorial interaction testing for test case and input set analysis will be used for improving the systems coverage capability. The system will be a self learning system in the sense that the lessons learned from testing one application will be used to test another application.
{"title":"Towards Having a Cloud of Mobile Devices Specialized for Software Testing","authors":"Mehmet Çagri Çalpur, Cemal Yilmaz","doi":"10.1145/2897073.2897109","DOIUrl":"https://doi.org/10.1145/2897073.2897109","url":null,"abstract":"This paper proposes a novel cloud testing platform specialized for software testing. Our novel approach aims to perform dynamic analysis on mobile application binaries, generate the model of the application, its test cases and test input sets on the run. Domain information generated via dynamic analysis and utilization of combinatorial interaction testing for test case and input set analysis will be used for improving the systems coverage capability. The system will be a self learning system in the sense that the lessons learned from testing one application will be used to test another application.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"40 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120866562","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}
The human daily and the professional life demand a high amount of communication ability, but every fourth adult above 50 is hearing-impaired, a fraction that steadily in- creases in an aging society. For an autonomous, self-condent and long productive life, a good speech understanding in ev- eryday life situations is necessary to reduce the listening ef- fort. For this purpose, a mobile app-based assistance system based on Blind Source Separation is required that makes every-day acoustic scenarios more transparent by the op- portunity of an interactive focusing on the preferred sound source in as close to real-time as possible. In case of highly costly BSS algorithms, at least an oine separation is to be provided. Developing such an app in the context of a short-term research project with limited budget to realize this goal statement makes it impossible to meet the chal- lenge as a stand-alone solution with existing technologies and hardware. As an alternative, employing part of the re- quired soft- and/or hardware on a remote server at least maintains the mobile context, given sucient connectivity. For this purpose, a client-server architecture that combines Android, Java, MatlabControl and MATLAB, and that con- ducts separation of live recorded audio data remotely, is ex- plained and tested. Conclusions about what is possible for the oine case are drawn from that. Tests are evaluated using a set of objective and subjective criteria. This demon- strates the possibility of realizing the assistance system in a mobile context.
{"title":"Evaluating BSS Algorithms in a Mobile Context Realized by a Client-Server Architecture","authors":"M. Offiah, T. Gross, M. Borschbach","doi":"10.1145/2897073.2897098","DOIUrl":"https://doi.org/10.1145/2897073.2897098","url":null,"abstract":"The human daily and the professional life demand a high amount of communication ability, but every fourth adult above 50 is hearing-impaired, a fraction that steadily in- creases in an aging society. For an autonomous, self-condent and long productive life, a good speech understanding in ev- eryday life situations is necessary to reduce the listening ef- fort. For this purpose, a mobile app-based assistance system based on Blind Source Separation is required that makes every-day acoustic scenarios more transparent by the op- portunity of an interactive focusing on the preferred sound source in as close to real-time as possible. In case of highly costly BSS algorithms, at least an oine separation is to be provided. Developing such an app in the context of a short-term research project with limited budget to realize this goal statement makes it impossible to meet the chal- lenge as a stand-alone solution with existing technologies and hardware. As an alternative, employing part of the re- quired soft- and/or hardware on a remote server at least maintains the mobile context, given sucient connectivity. For this purpose, a client-server architecture that combines Android, Java, MatlabControl and MATLAB, and that con- ducts separation of live recorded audio data remotely, is ex- plained and tested. Conclusions about what is possible for the oine case are drawn from that. Tests are evaluated using a set of objective and subjective criteria. This demon- strates the possibility of realizing the assistance system in a mobile context.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328717","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 app developers declare permissions, but these do not guarantee that apps will behave as expected. Existing work focuses on checking predefined app properties, e.g., clone detection and API analysis. We propose BehaviorDroid, a framework for monitoring general app properties at runtime. Properties are currently specified using automata, describing desired and unwanted interactions between an app and phone resources. BehaviorDroid is a robust, extensible and configurable framework that can simultaneously monitor multiple apps and properties, showing reasonable CPU and memory usage during execution. Initial experiments show that we can improve memory usage by combining automata that have similar alphabets.
{"title":"BehaviorDroid: Monitoring Android Applications","authors":"Alexis Silva, J. Simmonds","doi":"10.1145/2897073.2897121","DOIUrl":"https://doi.org/10.1145/2897073.2897121","url":null,"abstract":"Mobile app developers declare permissions, but these do not guarantee that apps will behave as expected. Existing work focuses on checking predefined app properties, e.g., clone detection and API analysis. We propose BehaviorDroid, a framework for monitoring general app properties at runtime. Properties are currently specified using automata, describing desired and unwanted interactions between an app and phone resources. BehaviorDroid is a robust, extensible and configurable framework that can simultaneously monitor multiple apps and properties, showing reasonable CPU and memory usage during execution. Initial experiments show that we can improve memory usage by combining automata that have similar alphabets.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"375 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127587324","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}
Dilay Parmar, A. S. Kumar, Ashwin Nivangune, Padmaja Joshi, U. P. Rao
In this paper a mechanism to identify a cloudlet for computation offloading in a decentralized manner is proposed. The cloudlet identification is carried out in two phases. In the first phase, cloudlets within WiFi range of the mobile device are identified without connecting to any of the cloudlets. In the second phase, selection of the ideal offloading cloudlet is done based on infrastructure specific parameters making the mechanism more generic.
{"title":"Discovery and Selection Mechanism of Cloudlets in a Decentralized MCC Environment","authors":"Dilay Parmar, A. S. Kumar, Ashwin Nivangune, Padmaja Joshi, U. P. Rao","doi":"10.1145/2897073.2897114","DOIUrl":"https://doi.org/10.1145/2897073.2897114","url":null,"abstract":"In this paper a mechanism to identify a cloudlet for computation offloading in a decentralized manner is proposed. The cloudlet identification is carried out in two phases. In the first phase, cloudlets within WiFi range of the mobile device are identified without connecting to any of the cloudlets. In the second phase, selection of the ideal offloading cloudlet is done based on infrastructure specific parameters making the mechanism more generic.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133373113","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}
D. Lubensky, Marco Pistoia, Ching-Yung Lin, Omer Tripp
Mobile devices carry a number of vulnerabilities that, when exploited, can result in proprietary-data leakage, data alteration, fraudulent transactions and, in extreme cases, physical damage to the user and surroundings. Such attacks can be instigated by both outsiders and insiders, and can leverage vulnerabilities embedded in the hardware and software components of the device, as well as risky behavioral actions undertaken by the legitimate user of the device. Existing mobile security management solutions offer a wide range of configuration, tracking, and management features via device and container management, policy-based configuration, single sign-on, application whitelisting and/or blacklisting, as well as reputation and anti-malware services. A primary feature that none of the existing solutions has is emph{context-aware anomaly detection}. We propose a novel cognitive solution for mobile security based on context awareness. Our solution focuses on mobile management tools that understand long-term context-aware behavior anomalies on multiple devices.
{"title":"Cognitive Mobile Security: Invited Conference Keynote","authors":"D. Lubensky, Marco Pistoia, Ching-Yung Lin, Omer Tripp","doi":"10.1145/2897073.2897077","DOIUrl":"https://doi.org/10.1145/2897073.2897077","url":null,"abstract":"Mobile devices carry a number of vulnerabilities that, when exploited, can result in proprietary-data leakage, data alteration, fraudulent transactions and, in extreme cases, physical damage to the user and surroundings. Such attacks can be instigated by both outsiders and insiders, and can leverage vulnerabilities embedded in the hardware and software components of the device, as well as risky behavioral actions undertaken by the legitimate user of the device. Existing mobile security management solutions offer a wide range of configuration, tracking, and management features via device and container management, policy-based configuration, single sign-on, application whitelisting and/or blacklisting, as well as reputation and anti-malware services. A primary feature that none of the existing solutions has is emph{context-aware anomaly detection}. We propose a novel cognitive solution for mobile security based on context awareness. Our solution focuses on mobile management tools that understand long-term context-aware behavior anomalies on multiple devices.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133486138","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}
Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.
{"title":"A Pro-active and Dynamic Prediction Assistance Using BaranC Framework","authors":"Mohammad Hashemi, J. Herbert","doi":"10.1145/2897073.2897759","DOIUrl":"https://doi.org/10.1145/2897073.2897759","url":null,"abstract":"Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122767909","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}
This paper reviews the challenges faced when securing data on mobile devices. After a discussion of the state-of-the-art of secure storage for iOS and Android, the paper introduces an attack which demonstrates how Full Disk Encryption (FDE) on Android can be ineffective in practice.
{"title":"Faux Disk Encryption: Realities of Secure Storage on Mobile Devices","authors":"Drew Suarez, D. Mayer","doi":"10.1145/2897073.2897711","DOIUrl":"https://doi.org/10.1145/2897073.2897711","url":null,"abstract":"This paper reviews the challenges faced when securing data on mobile devices. After a discussion of the state-of-the-art of secure storage for iOS and Android, the paper introduces an attack which demonstrates how Full Disk Encryption (FDE) on Android can be ineffective in practice.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125119462","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}
Recently, mobile devices have become equipped with sophisticated hardware components such as a heterogeneous multi-core SoC that consists of a CPU, GPU, and DSP. This provides opportunities to realize computationally-intensive computer vision applications using General Purpose GPU (GPGPU) programming tools such as Open Graphics Library for Embedded System (OpenGL ES) and Open Computing Language (OpenCL). As a case study, the aim of this research was to accelerate the Viola-Jones face detection algorithm which is computationally expensive and limited in use on mobile devices due to irregular memory access and imbalanced workloads resulting in low performance regarding the processing time. To solve the above challenges, the proposed method of this study adapted CPU–GPU task parallelism, sliding window parallelism, scale image parallelism, dynamic allocation of threads, and local memory optimization to improve the computational time. The experimental results show that the proposed method achieved a 3.3~6.29 times increased computational time compared to the well-optimized OpenCV implementation on a CPU. The proposed method can be adapted to other applications using mobile GPUs and CPUs.
{"title":"Accelerating a Computer Vision Algorithm on a Mobile SoC Using CPU-GPU Co-processing - A Case Study on Face Detection","authors":"Youngwan Lee, Cheolyong Jang, Hakil Kim","doi":"10.1145/2897073.2897081","DOIUrl":"https://doi.org/10.1145/2897073.2897081","url":null,"abstract":"Recently, mobile devices have become equipped with sophisticated hardware components such as a heterogeneous multi-core SoC that consists of a CPU, GPU, and DSP. This provides opportunities to realize computationally-intensive computer vision applications using General Purpose GPU (GPGPU) programming tools such as Open Graphics Library for Embedded System (OpenGL ES) and Open Computing Language (OpenCL). As a case study, the aim of this research was to accelerate the Viola-Jones face detection algorithm which is computationally expensive and limited in use on mobile devices due to irregular memory access and imbalanced workloads resulting in low performance regarding the processing time. To solve the above challenges, the proposed method of this study adapted CPU–GPU task parallelism, sliding window parallelism, scale image parallelism, dynamic allocation of threads, and local memory optimization to improve the computational time. The experimental results show that the proposed method achieved a 3.3~6.29 times increased computational time compared to the well-optimized OpenCV implementation on a CPU. The proposed method can be adapted to other applications using mobile GPUs and CPUs.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663375","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 this paper, we demonstrate a system for automated trans- formation rule application in mobile system development. Our application enables correctness and consistency of the automated transformation rules, by leveraging Egyed's work [2] on automated model transformation. Our approach is im- plemented viafinite state automation generation, extending from our previous work.
{"title":"Model Under Design and Over Design on Mobile Applications","authors":"Yucong Duan, Xiaobing Sun, N. Narendra, Q. Duan, Guohua Fu, Ruomeng Xu","doi":"10.1145/2897073.2897709","DOIUrl":"https://doi.org/10.1145/2897073.2897709","url":null,"abstract":"In this paper, we demonstrate a system for automated trans- formation rule application in mobile system development. Our application enables correctness and consistency of the automated transformation rules, by leveraging Egyed's work [2] on automated model transformation. Our approach is im- plemented viafinite state automation generation, extending from our previous work.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053245","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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