Andreas Achtzehn, Janne Riihijärvi, Irving Antonio Barriía Castillo, M. Petrova, P. Mähönen
High-speed mobile broadband connections have opened exciting new opportunities to collect sensor data from thousands or even millions of distributed mobile devices for the purpose of crowdsourced decision making. In this paper, we propose CrowdREM (crowdsourced radio environment mapping), a framework with the specific aim of monitoring and modelling wireless cellular networks. CrowdREM enables operator-independent and highly efficient collection of network performance data along all layers of the communications protocol stack. Such extensive information on network load, spectrum usage, or local coverage can help operators to optimize their networks and service quality and enable improved consumer decision making. In this paper, we introduce the mbox{CrowdREM} mobile architecture and show first results from a prototype implementation on open-source mobile phones. We demonstrate the versatility of using commodity devices for network and spectrum monitoring, and present the challenges originating from the use of uncalibrated and low-precision measurement equipment. We have acquired an extensive data set from using our prototype implementation in a 21-day measurement campaign covering more than 1,000 hours of measurement data. From this we present and discuss the potential derivation of tangible and relevant network performance and signal quality indicators, which could, e.g., be conducted by independent parties.
{"title":"CrowdREM: Harnessing the Power of the Mobile Crowd for Flexible Wireless Network Monitoring","authors":"Andreas Achtzehn, Janne Riihijärvi, Irving Antonio Barriía Castillo, M. Petrova, P. Mähönen","doi":"10.1145/2699343.2699348","DOIUrl":"https://doi.org/10.1145/2699343.2699348","url":null,"abstract":"High-speed mobile broadband connections have opened exciting new opportunities to collect sensor data from thousands or even millions of distributed mobile devices for the purpose of crowdsourced decision making. In this paper, we propose CrowdREM (crowdsourced radio environment mapping), a framework with the specific aim of monitoring and modelling wireless cellular networks. CrowdREM enables operator-independent and highly efficient collection of network performance data along all layers of the communications protocol stack. Such extensive information on network load, spectrum usage, or local coverage can help operators to optimize their networks and service quality and enable improved consumer decision making. In this paper, we introduce the mbox{CrowdREM} mobile architecture and show first results from a prototype implementation on open-source mobile phones. We demonstrate the versatility of using commodity devices for network and spectrum monitoring, and present the challenges originating from the use of uncalibrated and low-precision measurement equipment. We have acquired an extensive data set from using our prototype implementation in a 21-day measurement campaign covering more than 1,000 hours of measurement data. From this we present and discuss the potential derivation of tangible and relevant network performance and signal quality indicators, which could, e.g., be conducted by independent parties.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133541491","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}
X. Zou, Jeffrey Erman, V. Gopalakrishnan, Emir Halepovic, R. Jana, Xin Jin, J. Rexford, R. Sinha
Existing video streaming algorithms use various estimation approaches to infer the inherently variable bandwidth in cellular networks, which often leads to reduced quality of experience (QoE). We ask the question: "If accurate bandwidth prediction were possible in a cellular network, how much can we improve video QoE?". Assuming we know the bandwidth for the entire video session, we show that existing streaming algorithms only achieve between 69%-86% of optimal quality. Since such knowledge may be impractical, we study algorithms that know the available bandwidth for a few seconds into the future. We observe that prediction alone is not sufficient and can in fact lead to degraded QoE. However, when combined with rate stabilization functions, prediction outperforms existing algorithms and reduces the gap with optimal to 4%. Our results lead us to believe that cellular operators and content providers can tremendously improve video QoE by predicting available bandwidth and sharing it through APIs.
{"title":"Can Accurate Predictions Improve Video Streaming in Cellular Networks?","authors":"X. Zou, Jeffrey Erman, V. Gopalakrishnan, Emir Halepovic, R. Jana, Xin Jin, J. Rexford, R. Sinha","doi":"10.1145/2699343.2699359","DOIUrl":"https://doi.org/10.1145/2699343.2699359","url":null,"abstract":"Existing video streaming algorithms use various estimation approaches to infer the inherently variable bandwidth in cellular networks, which often leads to reduced quality of experience (QoE). We ask the question: \"If accurate bandwidth prediction were possible in a cellular network, how much can we improve video QoE?\". Assuming we know the bandwidth for the entire video session, we show that existing streaming algorithms only achieve between 69%-86% of optimal quality. Since such knowledge may be impractical, we study algorithms that know the available bandwidth for a few seconds into the future. We observe that prediction alone is not sufficient and can in fact lead to degraded QoE. However, when combined with rate stabilization functions, prediction outperforms existing algorithms and reduces the gap with optimal to 4%. Our results lead us to believe that cellular operators and content providers can tremendously improve video QoE by predicting available bandwidth and sharing it through APIs.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115727437","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}
Given the number of choices of platforms and apps with similar functionalities, this paper describes the challenges and identifies the gaps toward comparing mobile platforms and apps for energy efficiency. In addition, based on case studies that focus on energy efficiency comparison of different app categories on the most popular platforms, the paper discusses insights related to the major OS providers, energy-efficient app design, and app developers common practices.
{"title":"Energy-Efficiency Comparison of Mobile Platforms and Applications: A Quantitative Approach","authors":"Grace Metri, Weisong Shi, M. Brockmeyer","doi":"10.1145/2699343.2699358","DOIUrl":"https://doi.org/10.1145/2699343.2699358","url":null,"abstract":"Given the number of choices of platforms and apps with similar functionalities, this paper describes the challenges and identifies the gaps toward comparing mobile platforms and apps for energy efficiency. In addition, based on case studies that focus on energy efficiency comparison of different app categories on the most popular platforms, the paper discusses insights related to the major OS providers, energy-efficient app design, and app developers common practices.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114764073","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}
Owners of multiple personal computing devices, such as mobile phones, tablet PCs, laptops, or desktop PCs, may frequently want to transfer information from one device to another.Whereas a drag-and-drop function on the same computing device is easy to achieve, it becomes cumbersome in an environment with multiple computing devices. We have to first locate and then select the target device from a list of devices on a network, even when the device is right in front of us. In this paper, a novel direct manipulation technique for executing drag-and-drop operations between multi-touch devices is proposed.Under our interface concept, dubbed "Memory Stones," a user can "pick up" a data object displayed on one device screen, "carry" it to another device screen, and "put it down" on that device using only their fingers.During this drag-and-drop operation, the user is invited to pantomime the act of carrying a tangible object (a "stone") while keeping their fingertip positions unchanged. The system identifies both the source and target devices by matching the shape of the polygon formed by the fingertips when touching each respective screen. We have developed a prototype system for small-to-large sized multi-touch computers including smartphones, tablet PCs, laptops, and desktop PCs, and have carried out a preliminary evaluation of its feasibility.
{"title":"Memory Stones: An Intuitive Information Transfer Technique between Multi-touch Computers","authors":"Kaori Ikematsu, I. Siio","doi":"10.1145/2699343.2699352","DOIUrl":"https://doi.org/10.1145/2699343.2699352","url":null,"abstract":"Owners of multiple personal computing devices, such as mobile phones, tablet PCs, laptops, or desktop PCs, may frequently want to transfer information from one device to another.Whereas a drag-and-drop function on the same computing device is easy to achieve, it becomes cumbersome in an environment with multiple computing devices. We have to first locate and then select the target device from a list of devices on a network, even when the device is right in front of us. In this paper, a novel direct manipulation technique for executing drag-and-drop operations between multi-touch devices is proposed.Under our interface concept, dubbed \"Memory Stones,\" a user can \"pick up\" a data object displayed on one device screen, \"carry\" it to another device screen, and \"put it down\" on that device using only their fingers.During this drag-and-drop operation, the user is invited to pantomime the act of carrying a tangible object (a \"stone\") while keeping their fingertip positions unchanged. The system identifies both the source and target devices by matching the shape of the polygon formed by the fingertips when touching each respective screen. We have developed a prototype system for small-to-large sized multi-touch computers including smartphones, tablet PCs, laptops, and desktop PCs, and have carried out a preliminary evaluation of its feasibility.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128618117","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}
Yibo Zhu, Yanzi Zhu, Zengbin Zhang, Ben Y. Zhao, Haitao Zheng
Mobile computing is undergoing a significant shift. Where traditional mobile networks revolved around users and their movements, new networks often center around autonomous mobile agents. These include semi-autonomous drones on military missions, vacuum robots search for dirt at home, intelligent cars that deliver us to work, and first responder robots that find and rescue victims in disasters. A critical challenge limiting these autonomous devices is the lack of accurate sensing systems, e.g. a mobile imaging system that captures the position, shape and surface material of nearby objects. These devices often require high levels of accuracy, and operate under tight constraints: in low-light conditions or moving at moderate speeds. These constraints dramatically reduce the set of possible solutions, eliminating traditional imaging systems that rely on visible light or specialized hardware. In this paper, we present early results in our efforts to design and evaluate a digital imaging radar system using reflections from 60GHz wireless beams. By using user mobility to emulate a virtual antenna array with large aperture, we build virtual antennas with large aperture and high precision. We describe details of our design, including mechanisms for object detection, object imaging, and controlling precision. Our experiments on a real 60GHz testbed show that we can achieve high precision (~1 cm) imaging with as little user movement as half a meter, as well as added potential for using loss profiles to infer the surface material on detected objects.
{"title":"60GHz Mobile Imaging Radar","authors":"Yibo Zhu, Yanzi Zhu, Zengbin Zhang, Ben Y. Zhao, Haitao Zheng","doi":"10.1145/2699343.2699363","DOIUrl":"https://doi.org/10.1145/2699343.2699363","url":null,"abstract":"Mobile computing is undergoing a significant shift. Where traditional mobile networks revolved around users and their movements, new networks often center around autonomous mobile agents. These include semi-autonomous drones on military missions, vacuum robots search for dirt at home, intelligent cars that deliver us to work, and first responder robots that find and rescue victims in disasters. A critical challenge limiting these autonomous devices is the lack of accurate sensing systems, e.g. a mobile imaging system that captures the position, shape and surface material of nearby objects. These devices often require high levels of accuracy, and operate under tight constraints: in low-light conditions or moving at moderate speeds. These constraints dramatically reduce the set of possible solutions, eliminating traditional imaging systems that rely on visible light or specialized hardware. In this paper, we present early results in our efforts to design and evaluate a digital imaging radar system using reflections from 60GHz wireless beams. By using user mobility to emulate a virtual antenna array with large aperture, we build virtual antennas with large aperture and high precision. We describe details of our design, including mechanisms for object detection, object imaging, and controlling precision. Our experiments on a real 60GHz testbed show that we can achieve high precision (~1 cm) imaging with as little user movement as half a meter, as well as added potential for using loss profiles to infer the surface material on detected objects.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122427402","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}
From wearable displays to smart watches to in-vehicle infotainment systems, mobile computers are increasingly integrated with our day-to-day activities. Interactions are commonly driven by applications that run in the background and notify users when their attention is needed. In this paper, we argue that existing mobile operating systems should manage user attention as a resource. In contrast to permission-based models that either allow applications to interrupt the user continuously or deny all access, the OS should instead pre- dict the importance and complexity of new interactions and compare the demand for attention to the attention available after accounting for the user's current activities. This will allow the OS to initiate appropriate interactions at the right time using the right modality. We describe one design for such a system, and we outline key challenges that must be met to realize this vision.
{"title":"The Case for Operating System Management of User Attention","authors":"Kyungmin Lee, J. Flinn, Brian D. Noble","doi":"10.1145/2699343.2699362","DOIUrl":"https://doi.org/10.1145/2699343.2699362","url":null,"abstract":"From wearable displays to smart watches to in-vehicle infotainment systems, mobile computers are increasingly integrated with our day-to-day activities. Interactions are commonly driven by applications that run in the background and notify users when their attention is needed. In this paper, we argue that existing mobile operating systems should manage user attention as a resource. In contrast to permission-based models that either allow applications to interrupt the user continuously or deny all access, the OS should instead pre- dict the importance and complexity of new interactions and compare the demand for attention to the attention available after accounting for the user's current activities. This will allow the OS to initiate appropriate interactions at the right time using the right modality. We describe one design for such a system, and we outline key challenges that must be met to realize this vision.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132775026","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}
Wenlu Hu, Brandon Amos, Zhuo Chen, Kiryong Ha, Wolfgang Richter, P. Pillai, Benjamin Gilbert, J. Harkes, M. Satyanarayanan
When offloading computation from a mobile device, we show that it can pay to perform additional on-device work in order to reduce the offloading workload. We call this offload shaping, and demonstrate its application at many different levels of abstraction using a variety of techniques. We show that offload shaping can produce significant reduction in resource demand, with little loss of application-level fidelity.
{"title":"The Case for Offload Shaping","authors":"Wenlu Hu, Brandon Amos, Zhuo Chen, Kiryong Ha, Wolfgang Richter, P. Pillai, Benjamin Gilbert, J. Harkes, M. Satyanarayanan","doi":"10.1145/2699343.2699351","DOIUrl":"https://doi.org/10.1145/2699343.2699351","url":null,"abstract":"When offloading computation from a mobile device, we show that it can pay to perform additional on-device work in order to reduce the offloading workload. We call this offload shaping, and demonstrate its application at many different levels of abstraction using a variety of techniques. We show that offload shaping can produce significant reduction in resource demand, with little loss of application-level fidelity.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131623617","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}
Feng Lu, Hao Du, Ankur Jain, G. Voelker, A. Snoeren, A. Terzis
With the advent of high-speed cellular access and the overwhelming popularity of smartphones, a large percent of today's Internet content is being delivered via cellular links. Due to the nature of long-range wireless signal propagation, the capacity of the last hop cellular link can vary by orders of magnitude within a short period of time (e.g., a few seconds). Unfortunately, TCP does not perform well in such fast-changing environments, potentially leading to poor spectrum utilization and high end-to-end packet delay. In this paper we revisit seminal work in cross-layer optimization in the context of 4G cellular networks. Specifically, we leverage the rich physical layer information exchanged between base stations (NodeB) and mobile phones (UE) to predict the capacity of the underlying cellular link, and propose nCQIC, a cross-layer congestion control design. Experiments on real cellular networks confirm that our capacity estimation method is both accurate and precise. A CQIC sender uses these capacity estimates to adjust its packet sending behavior. Our preliminary evaluation reveals that CQIC improves throughput over TCP by 1.08-2.89x for small and medium flows. For large flows, CQIC attains throughput comparable to TCP while reducing the average RTT by 2.38-2.65x.
{"title":"CQIC: Revisiting Cross-Layer Congestion Control for Cellular Networks","authors":"Feng Lu, Hao Du, Ankur Jain, G. Voelker, A. Snoeren, A. Terzis","doi":"10.1145/2699343.2699345","DOIUrl":"https://doi.org/10.1145/2699343.2699345","url":null,"abstract":"With the advent of high-speed cellular access and the overwhelming popularity of smartphones, a large percent of today's Internet content is being delivered via cellular links. Due to the nature of long-range wireless signal propagation, the capacity of the last hop cellular link can vary by orders of magnitude within a short period of time (e.g., a few seconds). Unfortunately, TCP does not perform well in such fast-changing environments, potentially leading to poor spectrum utilization and high end-to-end packet delay. In this paper we revisit seminal work in cross-layer optimization in the context of 4G cellular networks. Specifically, we leverage the rich physical layer information exchanged between base stations (NodeB) and mobile phones (UE) to predict the capacity of the underlying cellular link, and propose nCQIC, a cross-layer congestion control design. Experiments on real cellular networks confirm that our capacity estimation method is both accurate and precise. A CQIC sender uses these capacity estimates to adjust its packet sending behavior. Our preliminary evaluation reveals that CQIC improves throughput over TCP by 1.08-2.89x for small and medium flows. For large flows, CQIC attains throughput comparable to TCP while reducing the average RTT by 2.38-2.65x.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121949363","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}
Sumeet Kumar, Le T. Nguyen, Mingzhi Zeng, K. Liu, J. Zhang
Sound provides valuable information about a mobile user's activity and environment. With the increasing large market penetration of smart phones, recording sound from mobile phones' microphones and processing the sound information either on mobile devices or in the cloud opens a window to a large variety of mobile applications that are context-aware and behavior-aware. On the other hand, sound sensing has the potential risk of compromising users' privacy. Security attacks by malicious software running on smart phones can obtain in-band and out-of-band sound information to infer the content of users' conversation. In this paper, we propose two simple yet highly effective methods called {em sound shredding} and {em sound subsampling}. Sound shredding mutates the raw sound frames randomly just like paper shredding and sound subsampling randomly drops sound frames without storing them. The resulting mutated sound recording makes it difficult to recover the text content of the original sound recording, yet we show that some acoustic features are preserved which retains the accuracy of context recognition.
{"title":"Sound Shredding: Privacy Preserved Audio Sensing","authors":"Sumeet Kumar, Le T. Nguyen, Mingzhi Zeng, K. Liu, J. Zhang","doi":"10.1145/2699343.2699366","DOIUrl":"https://doi.org/10.1145/2699343.2699366","url":null,"abstract":"Sound provides valuable information about a mobile user's activity and environment. With the increasing large market penetration of smart phones, recording sound from mobile phones' microphones and processing the sound information either on mobile devices or in the cloud opens a window to a large variety of mobile applications that are context-aware and behavior-aware. On the other hand, sound sensing has the potential risk of compromising users' privacy. Security attacks by malicious software running on smart phones can obtain in-band and out-of-band sound information to infer the content of users' conversation. In this paper, we propose two simple yet highly effective methods called {em sound shredding} and {em sound subsampling}. Sound shredding mutates the raw sound frames randomly just like paper shredding and sound subsampling randomly drops sound frames without storing them. The resulting mutated sound recording makes it difficult to recover the text content of the original sound recording, yet we show that some acoustic features are preserved which retains the accuracy of context recognition.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116046698","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}
While great strides have been made in measuring energy consumption, these measures alone are not sufficient to enable effective energy management on battery-constrained mobile devices. What is urgently needed is a way to put energy consumption into context by measuring the value delivered by mobile apps. While difficult to compute, an accurate value measure would enable cross-app comparison, app improvement, energy inefficient app detection, and effective runtime energy allocation and prioritization. Our paper motivates the problem, describes requirements for a value measure, discusses and evaluates several possible inputs to such a measure, and presents results from a preliminary (unsuccessful) attempt to formulate one.
{"title":"The Missing Numerator: Toward a Value Measure for Smartphone Apps","authors":"Anudipa Maiti, Geoffrey Challen","doi":"10.1145/2699343.2699360","DOIUrl":"https://doi.org/10.1145/2699343.2699360","url":null,"abstract":"While great strides have been made in measuring energy consumption, these measures alone are not sufficient to enable effective energy management on battery-constrained mobile devices. What is urgently needed is a way to put energy consumption into context by measuring the value delivered by mobile apps. While difficult to compute, an accurate value measure would enable cross-app comparison, app improvement, energy inefficient app detection, and effective runtime energy allocation and prioritization. Our paper motivates the problem, describes requirements for a value measure, discusses and evaluates several possible inputs to such a measure, and presents results from a preliminary (unsuccessful) attempt to formulate one.","PeriodicalId":252231,"journal":{"name":"Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131134920","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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