Context-aware Battery Management for Mobile Phones

Ravi, Nishkam; Scott, James R.; Han, Lu; Iftode, Liviu

doi:10.1109/percom.2008.108

Cited by 108 publications

(62 citation statements)

References 8 publications

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“…Thus, having perfect knowledge of the incoming traffic permits to save up to 60% power, meaning that this methodology can be improved by a model considering usage patterns. Finally, a Battery Lifetime Predictor is presented in [33]. The approach compared the device actual discharge with a measured base curve, obtained when the device was in idle, i.e.…”

Section: User-level Consumptionmentioning

confidence: 99%

On Power and Energy Consumption Modeling for Smart Mobile Devices

Ferroni

Cazzola

Trovò

et al. 2014

2014 12th IEEE International Conference on Embedded and Ubiquitous Computing

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Abstract-In nowadays life, mobile phones are becoming a cheaper and smaller alternative to laptops for simple, everyday tasks. They experienced an astonishing growth in functionalities and, because of their constant presence in our life, mobile phones became fundamental for the interaction with information coming from the environment. Nevertheless, their resources are limited, both in terms of performance and power, and their availability can greatly vary over time. Especially when dealing with power consumption, mobile devices cannot disregard environment conditions and user habits. Both internal and external conditions are rapidly changing and may influence the response of the entire system, e.g., switching between network types may causes an unpredictable power consumption. In order to puzzle out all these issues, we regard the definition of a power/energy model for mobile devices as a first mandatory step. In literature, several attempts to do so are present, basing their approaches on techniques coming from different computer science fields. They differ in the way they consider hardware components, in the operating system they are suitable for and in the scope of their tests and experiments. Within this paper, we categorize techniques presented in the major works in the field, in order to be able to compare different methods, highlight open issues and give suggestions on future works.

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Section: User-level Consumptionmentioning

confidence: 99%

On Power and Energy Consumption Modeling for Smart Mobile Devices

Ferroni

Cazzola

Trovò

et al. 2014

2014 12th IEEE International Conference on Embedded and Ubiquitous Computing

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“…During the past decade, mobile technology has developed even faster than was expected during the ICT boom at the turn of the new millennium (CISCO 2014). The requirements concerning the physical size and capacity of batteries have become stricter due to the size miniaturization and growing performance requirements, while at the same, time more and more advanced hardware and software require more energy (Ravi et al 2008). Since the battery technology has not developed at the same pace, the average battery life of a typical high-end smartphone, for instance, has significantly reduced during the past fifteen years.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient peer-to-peer networking for constrained-capacity mobile environments

Harjula

Ojala

Ylianttila

2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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Energy efficiency is a powerful measure for promoting sustainability in technological evolution and ensuring feasible battery life of end-user devices in mobile computing. Peer-to-peer technology provides decentralized and self-organizing architecture for distributing content between devices in networks that scale up almost infinitely. However, peer-to-peer networking may require lots of resources from peer nodes, which in turn may lead to increased energy consumption on mobile devices. For this reason, peer-to-peer networking has so far been considered unfeasible for mobile environment.This thesis makes several contributions towards enabling energy-aware peer-to-peer networking in mobile environments. First, an empirical study is conducted to understand the energy consumption characteristics of radio interfaces and typical composition of traffic in structured peer-to-peer networks. This is done in order to identify the most essential obstacles for utilizing peer-to-peer technology in mobile environments. Second, the e-Aware model for estimating the energy consumption of a mobile device is developed and empirically verified to achieve 3-21% error in comparison to real-life measurements. Third, the e-Mon model for the energy-aware load monitoring of peer nodes is developed and demonstrated to improve the battery life of mobile peer nodes up to 470%. Fourth, the ADHT concept of mobile agent based virtual peers is proposed for sharing the peer responsibilities between peer nodes in a subnet so that they can participate in a peer-to-peer overlay without compromising their battery life.The results give valuable insight into implementing energy-efficient peer-to-peer systems in mobile environments. The e-Aware energy consumption model accelerates the development of energy-efficient networking solutions by reducing the need for time-consuming iterations between system development and evaluations with real-life networks and devices. The e-Mon load monitoring model facilitates the participation of battery-powered devices in peer-to-peer and other distributed networks by enabling energy-aware load balancing where energy-critical mobile nodes carry less load than other nodes. The ADHT facilitates the participation of constrained-capacity wireless devices, such as machine-to-machine nodes, in a peer-to-peer network by allowing them to sleep for most of their time.Keywords: battery life, constrained-capacity devices, energy-efficiency, green computing, internet of things, machine-to-machine networks, mobile networks, peer-topeer networks Harjula, Erkki, Energiatehokkaat vertaisverkkoratkaisut rajoitetun kapasiteetin mobiiliympäristöissä.Oulun yliopiston tutkijakoulu; Oulun yliopisto, Tieto-ja sähkötekniikan tiedekunta; Centre for Wireless Communications; Infotech Oulu Acta Univ. Oul. C 573, 2016 Oulun yliopisto, PL 8000, 90014 Oulun yliopisto TiivistelmäEnergiatehokkuus on kustannustehokas tapa vähentää päätelaitteiden käytön aiheuttamia kasvihuonepäästöjä sekä parantaa niiden akunkestoa. Vertaisverkkoteknologia ta...

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“…Experiments relating to energy measurement could be at various levels: the hardware level; energy efficiency directive level (Simunic, et al 2000); operating system (Sagahyroon, 2006); software application or data and user levels (Ravi, et al 2008). Energy conservation is made possible through the use of different techniques which estimate or forecast energy consumption at the device and application level (Krintz, et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Applications, energy consumption, and measurement

Kor

Pattinson

Imam

et al. 2015

2015 International Conference on Information and Digital Technologies

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Abstract-The task of reducing the energy footprint of IT devices and software has been a challenge for Green IT research. Monitoring approaches have primarily focused on measuring the energy consumption of the hardware components of computing devices. The use of applications or software on our computer systems consumes energy and it also affects how various hardware components and system resources consume energy. Consequently, running web browsers applications will utilise considerable energy and battery consumption. In this research, we have run different types of experiments which involve the use of several measuring tools. Firsly, a joulemeter is used to monitor (and measure) the power consumed by the hardware and software while running web-based and stand-alone applications on several devices. Additionally, the tablet in-built battery status checker is used to measure the battery consumption when webbased applications are run on the device.

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Context-aware Battery Management for Mobile Phones

Abstract: In this paper, we propose a system for contextaware

Cited by 108 publications

References 8 publications

On Power and Energy Consumption Modeling for Smart Mobile Devices

On Power and Energy Consumption Modeling for Smart Mobile Devices

Energy-efficient peer-to-peer networking for constrained-capacity mobile environments

Applications, energy consumption, and measurement

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