Power Saving in Mobile Devices Using Context-Aware Resource Control

Nishihara, Kosuke; Ishizaka, Kazuhisa; Sakai, Junji

doi:10.1109/ic-nc.2010.50

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…3. Battery usage optimization: to address the battery usage and response time challenges, through a context-aware resource control mechanism [22], AICOM utilizes energy-constrained quantization optimization (EQO) technique to achieve lower energy consumption, resulting in prolongation of mobile device usage time while maintaining consistent diagnostic and screening performance. AICOM has also achieved less than 1 second execution time to allow the application to be interactive and responsive.…”

Section: The Aicom Frameworkmentioning

confidence: 99%

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

Yang,

et al. 2024

Preprint

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This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health is fundamental to human capital and social and economic development. The core motivation for the AICOM project is the fact that over 80% of the people in the least developed countries (LDCs) own a mobile phone, even though less than 40% of these people have internet access. Hence, the key to maximize health care access is to empower health AI on resource-constrained mobile devices without connectivity. We have evaluated AICOM’s public health benefits through analyzing data from the World Bank. For technical development, multiple technologies, including model size shrinking, compute optimization, battery usage optimization have been developed and integrated into the AICOM framework to empower AI-based disease diagnostics and screening capability on resource-constrained mobile phones without connectivity.

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Section: The Aicom Frameworkmentioning

confidence: 99%

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

Yang,

et al. 2024

Preprint

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“…Context-aware computing is a framework through which a system can comprehend the environment automatically and use the information to provide a better service [8,9] to the user. Nishihara et al [10] developed a system consisting of three components: Context Recognition Module, QoS Manager, and Resource Control Module.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time Context-aware Learning System for IoT Applications

Das¹,

Almhana²

2018

Preprint

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We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in real-time and manage IoT devices accordingly. However, the computational power of the mobile devices makes it challenging to run machine learning algorithms with acceptable accuracy. To solve this issue, some authors have run machine learning algorithms on the server and transmitted the results to the mobile devices. Although the context-aware predictions made by the server are more accurate than their mobile counterpart, it heavily depends on the network connection for the delivery of the results to the devices, which negatively affects real-time context-learning.Therefore, in this work, we describe a context-learning algorithm for mobile devices which is less demanding on the computational resources and maintains the accuracy of the prediction by updating itself from the learning parameters obtained from the server periodically. Experimental results

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“…Lee et al [38] propose a Variable Rate Logging (VRL) mechanism that disables location logging or reduces the GPS logging rate by detecting if the user is standing still or indoors. Nishihara et al [39] propose a context-aware method to determine the minimum set of resources (processors and peripherals) that result in meeting a given level of performance. A framework for mobile sensing that efficiently recognizes contextual user states is proposed by Wang et al [10].…”

Section: Related Workmentioning

confidence: 99%

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Pasricha

Donohoo²,

Ohlsen

2015

Pervasive and Mobile Computing

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Pasricha, Sudeep; Donohoo, Brad K.; and Ohlsen, Chris, "A middleware framework for application-aware and user-specific energy optimization in smart mobile devices" (2015 Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated ''smart'' mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.

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Power Saving in Mobile Devices Using Context-Aware Resource Control

Cited by 13 publications

References 11 publications

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

Real-time Context-aware Learning System for IoT Applications

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

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