TIDE: A User-Centric Tool for Identifying Energy Hungry Applications on Smartphones

Dao, Tuan; Singh, Indrajeet; Madhyastha, Harsha V.; Krishnamurthy, Srikanth V.; Mohapatra, Prasant

doi:10.1109/tnet.2016.2639061

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…Although resource-efficiency in mobile computing has been on the research agenda for decades, MPS poses new questions. This is due to the combined effect of continuous monitoring and resource consumption that become dependent on overall user activities [8,32].…”

Section: Introductionmentioning

confidence: 99%

Dos and Don'ts in Mobile Phone Sensing Middleware

Issarny¹,

Mallet²,

Nguyen³

et al. 2016

Proceedings of the 17th International Middleware Conference

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Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a verylarge crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowdsensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware. CCS Concepts •Information systems → Mobile information processing systems; Sensor networks; •Human-centered computing → Ubiquitous and mobile computing systems and tools; Empirical studies in ubiquitous and mo

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Section: Introductionmentioning

confidence: 99%

Dos and Don'ts in Mobile Phone Sensing Middleware

Issarny¹,

Mallet²,

Nguyen³

et al. 2016

Proceedings of the 17th International Middleware Conference

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“…Different methods are used to validate the working and behaviors of Android apps to prevent them from misbehaving or risking user privacy and device security. Misalignments in their descriptions and permission requirements, unnecessary use of Android permissions and sensitive Application Programming Interfaces (APIs), app collusions, and use of framing effect are some of the known indicators for intrusive apps [31,[39][40][41][42][43][44][45][46][47]. Apps should also put adequate privacy protection and security controls in place [48].…”

Section: Transparency and Accountability In Android Appsmentioning

confidence: 99%

On Transparency and Accountability of Smart Assistants in Smart Cities

et al. 2019

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Smart Assistants have rapidly emerged in smartphones, vehicles, and many smart home devices. Establishing comfortable personal spaces in smart cities requires that these smart assistants are transparent in design and implementation—a fundamental trait required for their validation and accountability. In this article, we take the case of Google Assistant (GA), a state-of-the-art smart assistant, and perform its diagnostic analysis from the transparency and accountability perspectives. We compare our discoveries from the analysis of GA with those of four leading smart assistants. We use two online user studies (N = 100 and N = 210) conducted with students from four universities in three countries (China, Italy, and Pakistan) to learn whether risk communication in GA is transparent to its potential users and how it affects them. Our research discovered that GA has unusual permission requirements and sensitive Application Programming Interface (API) usage, and its privacy requirements are not transparent to smartphone users. The findings suggest that this lack of transparency makes the risk assessment and accountability of GA difficult posing risks to establishing private and secure personal spaces in a smart city. Following the separation of concerns principle, we suggest that autonomous bodies should develop standards for the design and development of smart city products and services.

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“…Zou et al [15] showed that the battery power of smartphones could be one factor impacting data throughput in cellular networks. Tuan et al [16] also showed that network type, transfer packet size, and link quality impact smartphones' battery consumption. A study by Zou et al [17] showed that it is possible to get accurate predictions of throughput (up to 98% accuracy) for short time periods by observing network performance on a stationary client device.…”

Section: Related Workmentioning

confidence: 99%

Analyzing throughput and stability in cellular networks

Walelgne

Manner

Bajpai

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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The throughput of a cellular network depends on a number of factors such as radio technology, limitations of device hardware (e.g., chipsets, antennae), physical layer effects (interference, fading, etc.), node density and demand, user mobility, and the infrastructure of Mobile Network Operators (MNO). Therefore, understanding and identifying the key factors of cellular network performance that affect end-users experience is a challenging task. We use a dataset collected using netradar, a platform that measures cellular network performance crowdsourced from mobile user devices. Using this dataset we develop a methodology (a classifier using a machine learning approach) for understanding cellular network performance. We examine key characteristics of cellular networks related to throughput from the perspective of mobile user activity, MNO, smartphone models, link stability, location and time of day. We perform a network-wide correlation and statistical analysis to obtain a basic understanding of the influence of individual factors. We use a machine learning approach to identify the important features and to understand the relationship between different ones. These features are then used to build a model to classify the stability of cellular network based on the data reception characteristics of the user. We show that it is possible to classify reasons for network instability using minimal cellular network metrics with up to 90% of accuracy.

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TIDE: A User-Centric Tool for Identifying Energy Hungry Applications on Smartphones

Cited by 13 publications

References 12 publications

Dos and Don'ts in Mobile Phone Sensing Middleware

Dos and Don'ts in Mobile Phone Sensing Middleware

On Transparency and Accountability of Smart Assistants in Smart Cities

Analyzing throughput and stability in cellular networks

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