Pub Date : 2017-05-20DOI: 10.1109/MOBILESoft.2017.10
G. Avellis, Julian Harty, Y. Yu
Requirements of mobile apps are often hard to elicit from massive numbers of users, although it is important for the solution architecture to meet them. Mobile Twin Peaks approach is proposed as a process of developing apps concurrently and iteratively that incorporates bidirectional communications within a mobile app. The communications allow both requirements engineers and software architects to reach a consensus on functionalities and quality constraints and to adapt architectural design decisions appropriately. To recommend architectural design decisions to the developers, we aim to obtain architecture-critical requirements from a set of general apps by combining, for example, analytics, ethnographic study, and information retrieval. We argue that the effectiveness of these techniques could be evaluated by experimental case studies and by engaging with industry partners to perform action research.
{"title":"Towards Mobile Twin Peaks for App Development","authors":"G. Avellis, Julian Harty, Y. Yu","doi":"10.1109/MOBILESoft.2017.10","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.10","url":null,"abstract":"Requirements of mobile apps are often hard to elicit from massive numbers of users, although it is important for the solution architecture to meet them. Mobile Twin Peaks approach is proposed as a process of developing apps concurrently and iteratively that incorporates bidirectional communications within a mobile app. The communications allow both requirements engineers and software architects to reach a consensus on functionalities and quality constraints and to adapt architectural design decisions appropriately. To recommend architectural design decisions to the developers, we aim to obtain architecture-critical requirements from a set of general apps by combining, for example, analytics, ethnographic study, and information retrieval. We argue that the effectiveness of these techniques could be evaluated by experimental case studies and by engaging with industry partners to perform action research.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116235023","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.58
Gemma Catolino
Bug prediction allows developers to focus testing eorts onspecic areas of software systems. While this topic has beenextensively studied for traditional applications, investiga-tions on mobile apps are still missing. In this paper wepreliminarily study the eectiveness of a previously denedJust-In-Time bug prediction model applied onve mobileapps. Key results indicate the poor performance of the modeland the need of further research on the topic.
{"title":"Just-In-Time Bug Prediction in Mobile Applications: The Domain Matters!","authors":"Gemma Catolino","doi":"10.1109/MOBILESoft.2017.58","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.58","url":null,"abstract":"Bug prediction allows developers to focus testing eorts onspecic areas of software systems. While this topic has beenextensively studied for traditional applications, investiga-tions on mobile apps are still missing. In this paper wepreliminarily study the eectiveness of a previously denedJust-In-Time bug prediction model applied onve mobileapps. Key results indicate the poor performance of the modeland the need of further research on the topic.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122485512","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.15
Carlo Bernaschina, S. Comai, P. Fraternali
We demonstrate IFMLEdit.org, an online tool for the rapid prototyping of web and mobile applications.IFMLEdit.org is based on the Interaction Flow Modeling Language (IFML), an OMG standard for the description of the interaction between users and applications by means of flows of information in reaction to user events. In the demo, attendees will be able to edit IFML specifications with IFMLEdit.org, investigate their properties by transforming them into Place Chart Nets, a variant of Petri Nets, and generate the code of web and mobile applications from the validated IFML model.Video: https://youtu.be/y_hDVeUbi7g.
{"title":"IFMLEdit.org: Model Driven Rapid Prototyping of Mobile Apps","authors":"Carlo Bernaschina, S. Comai, P. Fraternali","doi":"10.1109/MOBILESoft.2017.15","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.15","url":null,"abstract":"We demonstrate IFMLEdit.org, an online tool for the rapid prototyping of web and mobile applications.IFMLEdit.org is based on the Interaction Flow Modeling Language (IFML), an OMG standard for the description of the interaction between users and applications by means of flows of information in reaction to user events. In the demo, attendees will be able to edit IFML specifications with IFMLEdit.org, investigate their properties by transforming them into Place Chart Nets, a variant of Petri Nets, and generate the code of web and mobile applications from the validated IFML model.Video: https://youtu.be/y_hDVeUbi7g.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129248018","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 : 2017-05-20DOI: 10.1109/MOBILESOFT.2017.2
Steven Arzt, Siegfried Rasthofer, E. Bodden
Due to the quality and security requirements that come with an always-on mobile device processing large amounts of highly sensitive information, Android apps are an important target for automated program analysis. Yet, research on new approaches in this field often requires a significant amount of work to be spent on engineering tasks that are not central to the concrete research question at hand. These programming and debugging tasks can significantly delay the progress of the field. We therefore argue that research in the field greatly benefits from having a universal platform of readily usable components and well-tested fundamental algorithms on top of which researchers can build their own prototypes. Besides decreasing the required engineering effort for each new piece of research, such a platform also provides a base for comparing different approaches within one uniform framework, thereby fostering comparability and reproducibility. In this paper, we present the Soot framework for program analysis and various highly integrated open-source tools and components built on top of it that were designed with re-usability in mind. These artifacts are already at the core of many research and commercial projects worldwide. Due to the shared platform, results from one tool can not only be used as inputs for the others, but individual data objects can be passed around to form one large API with which one can build new research prototypes with ease.
{"title":"The Soot-Based Toolchain for Analyzing Android Apps","authors":"Steven Arzt, Siegfried Rasthofer, E. Bodden","doi":"10.1109/MOBILESOFT.2017.2","DOIUrl":"https://doi.org/10.1109/MOBILESOFT.2017.2","url":null,"abstract":"Due to the quality and security requirements that come with an always-on mobile device processing large amounts of highly sensitive information, Android apps are an important target for automated program analysis. Yet, research on new approaches in this field often requires a significant amount of work to be spent on engineering tasks that are not central to the concrete research question at hand. These programming and debugging tasks can significantly delay the progress of the field. We therefore argue that research in the field greatly benefits from having a universal platform of readily usable components and well-tested fundamental algorithms on top of which researchers can build their own prototypes. Besides decreasing the required engineering effort for each new piece of research, such a platform also provides a base for comparing different approaches within one uniform framework, thereby fostering comparability and reproducibility. In this paper, we present the Soot framework for program analysis and various highly integrated open-source tools and components built on top of it that were designed with re-usability in mind. These artifacts are already at the core of many research and commercial projects worldwide. Due to the shared platform, results from one tool can not only be used as inputs for the others, but individual data objects can be passed around to form one large API with which one can build new research prototypes with ease.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122142081","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.5
Daniel E. Krutz, Nuthan Munaiah, Anthony S Peruma, Mohamed Wiem Mkaouer
Android applications rely on a permission-based model to carry out core functionality. Appropriate permission usage is imperative for ensuring device security and protecting the user's desired privacy levels. But who is making the important decisions of which permissions the app should request? Are they experienced developers with the appropriate project knowledge to make such important decisions, or are these crucial choices being made by those with relatively minor amounts of contributions to the project? When are these permission-related decisions being made in the app's development life cycle? We examined 1,402 Android version control repositories containing over 331,318 commits including 18,751 AndroidManifest.xml versions to better understand when, why, and who is adding permissions to apps. We found that (I) developers with more experience are more likely to make permission-based changes (II) permissions are typically added earlier in apps' commit lifetime, but their removal is more sustained throughout the commit lifetime (III) developers reverting permission-based changes are typically more experienced than developers who initially made the change being reverted.
{"title":"Who Added That Permission to My App? An Analysis of Developer Permission Changes in Open Source Android Apps","authors":"Daniel E. Krutz, Nuthan Munaiah, Anthony S Peruma, Mohamed Wiem Mkaouer","doi":"10.1109/MOBILESoft.2017.5","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.5","url":null,"abstract":"Android applications rely on a permission-based model to carry out core functionality. Appropriate permission usage is imperative for ensuring device security and protecting the user's desired privacy levels. But who is making the important decisions of which permissions the app should request? Are they experienced developers with the appropriate project knowledge to make such important decisions, or are these crucial choices being made by those with relatively minor amounts of contributions to the project? When are these permission-related decisions being made in the app's development life cycle? We examined 1,402 Android version control repositories containing over 331,318 commits including 18,751 AndroidManifest.xml versions to better understand when, why, and who is adding permissions to apps. We found that (I) developers with more experience are more likely to make permission-based changes (II) permissions are typically added earlier in apps' commit lifetime, but their removal is more sustained throughout the commit lifetime (III) developers reverting permission-based changes are typically more experienced than developers who initially made the change being reverted.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130526209","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.38
Iqbal H. Sarker, M. A. Kabir, A. Colman, Jun Han
Due to the popularity of context-aware computingand the rapid growth of the smart phone devices, modeling anindividual's phone call response behavior may assist them intheir daily activities for managing call interruptions. A key stepof such modeling is to discovering call response behavioral rulesbased on multi-dimensional contexts related to individual'sbehavior. Currently, researchers use classification rule learnersfor modeling individual's mobile phone behavior. However, theproblem is that such learning techniques produce only rulesthat include maximal number of contexts albeit ordered byrelevance. This results in many rules with low-reliability thatdecrease the accuracy of the modeling approach. In this paper, we propose an approach (Tmodel) to modeling individual'sphone call response behavior utilizing mobile phone data. Thisapproach produces not only general rules that capture individual'sbehavior at a particular level of confidence with a minimalnumber of contexts, but also produce rules that express specificexceptions to the general rules when more context-dimensionsare taken into account. Experimental evaluation shows thatour approach outperforms existing approaches to modelingindividual's phone call response behavior based on multidimensional contexts.
{"title":"An Approach to Modeling Call Response Behavior on Mobile Phones Based on Multi-Dimensional Contexts","authors":"Iqbal H. Sarker, M. A. Kabir, A. Colman, Jun Han","doi":"10.1109/MOBILESoft.2017.38","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.38","url":null,"abstract":"Due to the popularity of context-aware computingand the rapid growth of the smart phone devices, modeling anindividual's phone call response behavior may assist them intheir daily activities for managing call interruptions. A key stepof such modeling is to discovering call response behavioral rulesbased on multi-dimensional contexts related to individual'sbehavior. Currently, researchers use classification rule learnersfor modeling individual's mobile phone behavior. However, theproblem is that such learning techniques produce only rulesthat include maximal number of contexts albeit ordered byrelevance. This results in many rules with low-reliability thatdecrease the accuracy of the modeling approach. In this paper, we propose an approach (Tmodel) to modeling individual'sphone call response behavior utilizing mobile phone data. Thisapproach produces not only general rules that capture individual'sbehavior at a particular level of confidence with a minimalnumber of contexts, but also produce rules that express specificexceptions to the general rules when more context-dimensionsare taken into account. Experimental evaluation shows thatour approach outperforms existing approaches to modelingindividual's phone call response behavior based on multidimensional contexts.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130219071","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.27
Guillermo Valenzuela, H. A. Neyem, J. I. Benedetto, Jaime C. Navón, Pablo Sanabria, Juan A. Karmy, Felipe Balbontin
In this paper, we present a real-life case study to show the advantages of a new code offloading solution focused on improving both performance and energy consumption for image processing mobile applications. From our experiments, we found that offloading an image processing task would allow up to 5.7x speedup and 85% of reduction in energy consumption for low-end devices, and 1.7x speedup and 64.3% of reduction in energy consumption for high-end devices.
{"title":"Towards Native Code Offloading Platforms for Image Processing in Mobile Applications: A Case Study","authors":"Guillermo Valenzuela, H. A. Neyem, J. I. Benedetto, Jaime C. Navón, Pablo Sanabria, Juan A. Karmy, Felipe Balbontin","doi":"10.1109/MOBILESoft.2017.27","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.27","url":null,"abstract":"In this paper, we present a real-life case study to show the advantages of a new code offloading solution focused on improving both performance and energy consumption for image processing mobile applications. From our experiments, we found that offloading an image processing task would allow up to 5.7x speedup and 85% of reduction in energy consumption for low-end devices, and 1.7x speedup and 64.3% of reduction in energy consumption for high-end devices.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"50 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120908163","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.9
Marriette Katarahweire, Engineer Bainomugisha, K. Mughal
Mobile data collection systems (MDCS) in the health sector are of great benefit to health care providers and community workers especially in low-resource settings. MDCS enable the extension and provision of health services closer to the community by enabling data collection and diagnosis without the patient being in a hospital setting. MDCS, however, face a number security challenges including authentication and authorization of users, secure communication between a mobile client and the server, and secure application deployment. This paper provides a criteria and guidelines for evaluating an authentication model for MDCS. The criteria encompass key authentication dimensions including proper local and remote authentication, password management and recovery especially with no Internet connectivity. We assess the authentication models using two reference systems that are widely used in low-resource settings, namely, District Health Information Software (DHIS 2) and mUzima. The findings reveal gaps in the authentication model of the reference systems including insecure authentication, insecure storage of user credentials on the mobile device and no proper automatic logouts, among others.
{"title":"Authentication in Selected Mobile Data Collection Systems: Current State, Challenges, Solutions and Gaps","authors":"Marriette Katarahweire, Engineer Bainomugisha, K. Mughal","doi":"10.1109/MOBILESoft.2017.9","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.9","url":null,"abstract":"Mobile data collection systems (MDCS) in the health sector are of great benefit to health care providers and community workers especially in low-resource settings. MDCS enable the extension and provision of health services closer to the community by enabling data collection and diagnosis without the patient being in a hospital setting. MDCS, however, face a number security challenges including authentication and authorization of users, secure communication between a mobile client and the server, and secure application deployment. This paper provides a criteria and guidelines for evaluating an authentication model for MDCS. The criteria encompass key authentication dimensions including proper local and remote authentication, password management and recovery especially with no Internet connectivity. We assess the authentication models using two reference systems that are widely used in low-resource settings, namely, District Health Information Software (DHIS 2) and mUzima. The findings reveal gaps in the authentication model of the reference systems including insecure authentication, insecure storage of user credentials on the mobile device and no proper automatic logouts, among others.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124261557","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.29
M. Kessentini, Ali Ouni
The evolution rate of mobile applications is much higher than regular software applications having shorter release deadlines and smaller code base. Mobile applications tend to be evolved quickly by developers to meet several new customer requirements and fix discovered bugs. However, evolving the existing features and design may introduce bad design practices, also called code smells, which can highly decrease the maintainability and performance of these mobile applications. However, unlike the area of object-oriented software systems, the detection of code smells in mobile applications received a very little of attention. Recent, few studies defined a set of quality metrics for Android applications and proposed a support to manually write a set of rules to detect code smells by combining these quality metrics. However, finding the best combination of metrics and their thresholds to identify code smells is left to the developer as a manual process. In this paper, we propose to automatically generate rules for the detection of code smells in Android applications using a multi-objective genetic programming algorithm (MOGP). The MOGP algorithm aims at finding the best set of rules that cover a set of code smell examples of Android applications based on two conflicting objective functions of precision and recall. We evaluate our approach on 184 Android projects with source code hosted in GitHub. The statistical test of our results show that the generated detection rules identified 10 Android smell types on these mobile applications with an average correctness higher than 82% and an average relevance of 77% based on the feedback of active developers of mobile apps.
{"title":"Detecting Android Smells Using Multi-Objective Genetic Programming","authors":"M. Kessentini, Ali Ouni","doi":"10.1109/MOBILESoft.2017.29","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.29","url":null,"abstract":"The evolution rate of mobile applications is much higher than regular software applications having shorter release deadlines and smaller code base. Mobile applications tend to be evolved quickly by developers to meet several new customer requirements and fix discovered bugs. However, evolving the existing features and design may introduce bad design practices, also called code smells, which can highly decrease the maintainability and performance of these mobile applications. However, unlike the area of object-oriented software systems, the detection of code smells in mobile applications received a very little of attention. Recent, few studies defined a set of quality metrics for Android applications and proposed a support to manually write a set of rules to detect code smells by combining these quality metrics. However, finding the best combination of metrics and their thresholds to identify code smells is left to the developer as a manual process. In this paper, we propose to automatically generate rules for the detection of code smells in Android applications using a multi-objective genetic programming algorithm (MOGP). The MOGP algorithm aims at finding the best set of rules that cover a set of code smell examples of Android applications based on two conflicting objective functions of precision and recall. We evaluate our approach on 184 Android projects with source code hosted in GitHub. The statistical test of our results show that the generated detection rules identified 10 Android smell types on these mobile applications with an average correctness higher than 82% and an average relevance of 77% based on the feedback of active developers of mobile apps.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"645 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116475904","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 : 2017-05-20DOI: 10.1109/MOBILESoft.2017.8
John Wamburu, David Kaguma, Michiaki Tatsubori, Aisha Walcott-Bryant, R. Bryant, Komminist Weldemariam
Many intelligent transportation systems (ITS) in cities with developed economies are making use of mobile technology as data sources (e.g., many crowd-sourced traffic-related applications) to improve the quality and efficiency of transportation networks. Often, these data sources are used to supplement existing traffic monitoring equipment (e.g., ground-loop detectors, traffic cameras), to provide greater insights into roadway infrastructure and traffic dynamics. For cities with emerging economies where traditional traffic monitoring equipment is cost prohibitive, the rise in mobile technology presents a unique opportunity to leverage smartphone sensors as an alternative data source for ITS. There are, however, challenges to using these sensors particularly with the cost of mobile data, network consistency, and on-device resources. In this paper, we present a mobile system that instruments roads under resource constraint while a vehicle is in motion. It determines when and what data to collect and/or upload using a number of on-device valuation and optimisation functions, by prioritising data collection over uploading or vis-versa. We deployed our mobile system on a fleet of heavy-duty waste-collection trucks in Nairobi, Kenya to collect a large volume of real-word road infrastructure and mobility data. Results show that a 42 % reduction in wireless transmissions costs can be achieved with minimal impact to the time in which important data are collected, uploaded and harmonized into a frequently updated map of road infrastructure and traffic.
{"title":"Roaming Nairobi Roads: Instrumenting Roads under Resource Constraints","authors":"John Wamburu, David Kaguma, Michiaki Tatsubori, Aisha Walcott-Bryant, R. Bryant, Komminist Weldemariam","doi":"10.1109/MOBILESoft.2017.8","DOIUrl":"https://doi.org/10.1109/MOBILESoft.2017.8","url":null,"abstract":"Many intelligent transportation systems (ITS) in cities with developed economies are making use of mobile technology as data sources (e.g., many crowd-sourced traffic-related applications) to improve the quality and efficiency of transportation networks. Often, these data sources are used to supplement existing traffic monitoring equipment (e.g., ground-loop detectors, traffic cameras), to provide greater insights into roadway infrastructure and traffic dynamics. For cities with emerging economies where traditional traffic monitoring equipment is cost prohibitive, the rise in mobile technology presents a unique opportunity to leverage smartphone sensors as an alternative data source for ITS. There are, however, challenges to using these sensors particularly with the cost of mobile data, network consistency, and on-device resources. In this paper, we present a mobile system that instruments roads under resource constraint while a vehicle is in motion. It determines when and what data to collect and/or upload using a number of on-device valuation and optimisation functions, by prioritising data collection over uploading or vis-versa. We deployed our mobile system on a fleet of heavy-duty waste-collection trucks in Nairobi, Kenya to collect a large volume of real-word road infrastructure and mobility data. Results show that a 42 % reduction in wireless transmissions costs can be achieved with minimal impact to the time in which important data are collected, uploaded and harmonized into a frequently updated map of road infrastructure and traffic.","PeriodicalId":281934,"journal":{"name":"2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126201038","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：