Real-time android with RTDroid

Yan, Yin; Cosgrove, Shaun; Anand, Varun; Kulkarni, Amit; Konduri, Sree Harsha; Ko, Steven Y.; Ziarek, Lukasz

doi:10.1145/2594368.2594381

Cited by 34 publications

(19 citation statements)

References 15 publications

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“…All the three R1, R2, and R3 requirements are not yet supported on the current Android platform and its variation with real-time support, RTDroid [10]. The R1 requirement indicates that many audio requests are very time-sensitive.…”

Section: B Challenges In Audio Request Scheduling and Dispatchingmentioning

confidence: 99%

“…It is worth to note that a periodic request can be converted to a number of one-time requests. Each request A i gets randomly generated start time, playback duration, and deadline such that S i ∈ [0, 3000], C i ∈ [10,40], and D i ∈ [C i +100, C i +1000], and each parameter is uniformly chosen within given intervals. Some requests have tight deadline to model on-time audio requests and high rate sensing requests.…”

Section: A Simulationmentioning

confidence: 99%

“…RTDroid [10] explores core components of Android including Alarm Manager, Message Handler, and Sensor Manager for real-time support on commodity mobile devices. It then provides a novel architecture for priority-based scheduling of requests on various kinds of resources and functionalities.…”

Section: Related Workmentioning

confidence: 99%

“…While mobile sound applications impose various timing requirements, many of such requirements are not properly supported on Android, one of the most popular mobile platforms. There is a recent work, called RTDroid [10], designed to enable fixed-priority scheduling for real-time support on Android. However, the fixed-priority scheduling is not enough to satisfy well the tight individual timing requirements imposed by diverse sound applications.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SounDroid: Supporting Real-Time Sound Applications on Commodity Mobile Devices

Kim

Lee

Han

et al. 2015

2015 IEEE Real-Time Systems Symposium

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A variety of advantages from sounds such as measurement and accessibility introduces a new opportunity for mobile applications to offer broad types of interesting, valuable functionalities, supporting a richer user experience. However, in spite of the growing interests on mobile sound applications, few or no works have been done in focusing on managing an audio device effectively. More specifically, their low level of real-time capability for audio resources makes it challenging to satisfy tight timing requirements of mobile sound applications, e.g., a high sensing rate of acoustic sensing applications. To address this problem, this work presents the SounDroid framework, an audio device management framework for real-time audio requests from mobile sound applications. The design of SounDroid is based on the requirement analysis of audio requests as well as an understanding of the audio playback procedure including the audio request scheduling and dispatching on Android. It then incorporates both real-time audio request scheduling algorithms, called EDF-V and AFDS, and dispatching optimization techniques into mobile platforms, and thus improves the quality-ofservice of mobile sound applications. Our experimental results with the prototype implementation of SounDroid demonstrate that it is able to enhance scheduling performance for audio requests, compared to traditional mechanisms (by up to 40% improvement), while allowing deterministic dispatching latency.

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Section: B Challenges In Audio Request Scheduling and Dispatchingmentioning

confidence: 99%

Section: A Simulationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SounDroid: Supporting Real-Time Sound Applications on Commodity Mobile Devices

Kim

Lee

Han

et al. 2015

2015 IEEE Real-Time Systems Symposium

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“…RTDroid [5] is a variation of Android that provides a platform for the development of apps to run under real-time constraints. It is based on Fiji real-time VM running on real-time OS.…”

Section: Related Workmentioning

confidence: 99%

A Self-Adaptive Context-Aware Model for Mobile Commerce

Naveed

2015

Lecture Notes in Computer Science

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Abstract. Use of mobile devices for the online shopping is growing ever. This paper addresses the problem of querying the contents relevant to the current context of the mobile node. We present a context-aware model that can incrementally learn the user preferences and location-based content retrieval for the purpose of one-to-one marking strategy. The model is based on Monte-Carlo sampling and tree induction method. Monte-Carlo sampling is used to construct the synopsis structure while tree induction is used to predict the user preferences in the current context. The model is evaluated using two benchmark datasets for offline testing and an application is developed to test the model online. The results show an obvious advantage of using the Monte-Carlo based tree induction method as compare to its state-of-the-art rivals.

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You Deserve Better Properties (From Your Smart Devices)

2017

Fog for 5G and IoT

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Real-time android with RTDroid

Cited by 34 publications

References 15 publications

SounDroid: Supporting Real-Time Sound Applications on Commodity Mobile Devices

SounDroid: Supporting Real-Time Sound Applications on Commodity Mobile Devices

A Self-Adaptive Context-Aware Model for Mobile Commerce

You Deserve Better Properties (From Your Smart Devices)

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