Impact of capacity and discharging rate on battery life time: A stochastic model to support mobile device autonomy planning (original) (raw)

Exhausting battery statistics: understanding the energy demands on mobile handsets

Proceedings of the …, 2010

Despite the advances in battery technologies, mobile phones still suffer from severe energy limitations. Modern handsets are rich devices that can support multitasking thanks to their high processing power and provide a wide range of resources such as sensors and network interfaces with different energy demands. There have been multiple attempts to characterise those energy demands; both to save or to allocate energy to the applications on the handset. However, there is still little understanding on how the interdependencies between resources (interdependencies caused by the applications and users' behaviour) affect the battery life. In this paper, we demonstrate the necessity of considering all those dynamics in order to characterise the energy demands of the system accurately. These results indicate that simple algorithmic and rulebased scheduling techniques are not the most appropriate way of managing the resources since their usage can be affected by contextual factors, making necessary to find customised solutions that consider each user's behaviour and handset features.

User-Centric Prediction for Battery Lifetime of Mobile Devices

2008

Today, mobile devices are being used for various applications such as making voice/video calls, browsing Internet and so on. The operating time and battery consumption spent in those activities affect the battery life of mobile devices. In this paper, we propose a method for predicting the battery lifetime of mobile devices based on usage patterns. We define the possible states of mobile devices based on their operating functions and develop a method of predicting battery lifetime based on average battery consumption and duration of each state.

Battery Life Estimation of Mobile Embedded Systems

2001

Since battery life directly impacts the extent and duration of mobility, one of the key considerations in the design of a mobile embedded system should be to maximize the energy delivered by the battery, and hence the battery lifetime. To facilitate exploration of alternative implementations for a mobile embedded system, in this paper we address the issue of developing a fast and accurate battery model, and providing a framework for battery life estimation of Hardware/Software (HW/SW) embedded systems.

Understanding human-smartphone concerns: a study of battery life

2011

This paper presents a large, 4-week study of more than 4000 people to assess their smartphone charging habits to identify timeslots suitable for opportunistic data uploading and power intensive operations on such devices, as well as opportunities to provide interventions to support better charging behavior. The paper provides an overview of our study and how it was conducted using an online appstore as a software deployment mechanism, and what battery information was collected.

Extending the lifetime of a network of battery-powered mobile devices by remote processing: a Markovian decision-based approach

Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451)

This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This problem is solved by first constructing a stochastic model of the client-server system based on the theory of continuous-time Markovian decision processes. Next the dynamic power management problem with task migration is formulated as a policy optimization problem and solved exactly by using a linear programming approach. Based on the off-line optimal policy derived in this way, an on-line adaptive policy is proposed, which dynamically monitors the channel conditions and the server behavior and adopts a client-side power management policy with task migration that results in optimum energy consumption in the client. Experimental results demonstrate that the proposed method outperforms existing heuristic methods by as much as 35% in terms of the overall energy savings.

Power Management for a Distributed Wireless Health Management Architecture

2009

Distributed wireless architectures for prognostics is an important enabling step in prognostic research in order to achieve feasible real-time system health management. A significant problem encountered in implementation of such architectures is power management. In this paper, we present robust power management techniques for a generic health management architecture that involves diagnostics and prognostics for a system comprising multiple heterogeneous components. Our power management techniques are based on online dynamic monitoring of the sensor battery discharge profile which enables accurate predictions of when the device should be put into low power modes. In our architecture, low power mode is achieved by run-time sampling rate modification through sleep states. Our experiments with a cluster of smart sensors for a hybrid diagnostics and prognostics architecture show significant gains in power management without severe loss in performance. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Understanding human-battery interaction on mobile phones

2007

Mobile phone users have to deal with limited battery lifetime through a reciprocal process we call human-battery interaction (HBI). We conducted three user studies in order to understand HBI and discover the problems in existing mobile phone designs. The studies include a large-scale international survey, a onemonth field data collection including quantitative battery logging and qualitative inquiries from ten mobile phone users, and structured interviews with twenty additional mobile phone users. We evaluated various aspects of HBI, including charging behavior, battery indicators, user interfaces for power-saving settings, user knowledge, and user reaction. We find that mobile phone users can be categorized into two types regarding HBI and often have inadequate knowledge regarding phone power characteristics. We provide qualitative and quantitative evidence that problems in state-of-the-art user interfaces has led to underutilized power-saving settings, under-utilized battery energy, and dissatisfied users. Our findings provide insights into improving mobile phone design for users to effectively deal with the limited battery lifetime. Our work is the first to systematically address HBI on mobile phones and is complementary to the extensive research on energy-efficient design for a longer battery lifetime.

A Markovian Decision-Based Approach for Extending the Lifetime of a Network of Battery-Powered Mobile Devices by Remote Processing

Journal of Low Power Electronics, 2010

This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This problem is solved by first constructing a stochastic model of the client-server system based on the theory of continuous-time Markovian decision processes. Next the dynamic power management problem with task migration is formulated as a policy optimization problem and solved exactly by using a linear programming approach. Based on the off-line optimal policy derived in this way, an online adaptive policy is proposed, which dynamically monitors the channel conditions and the server behavior, takes into account real-time constraints, and adopts a client-side power management policy with task migration that results in optimum energy consumption in the client. Experimental results demonstrate that the proposed method outperforms existing heuristic methods by as much as 35% in terms of the overall energy savings.

Battery Consumption on Smartphones: A survey

Smartphones began as the new significant device to many. A cellphone can combine some or all functionalities of several different devices which include a secretive notebook, mobile, private reformation console, song player, walkie-talkie, and/or GPS. Most of the technologies listed on top of that may be actuated as mandatory, one smartphone is commonly on. Seeing that a smartphone will perform activities within the background even at some purpose in idle mode and since it's restricted to battery life, it'll be essential to understanding what really happens within the background and the way it affects the approach to life of the battery and, therefore, a way to improve it. For this termination, we have analyzed two telephone structures, in particular in search of how the recruitment of force varies according to past historical applications and the type of network connection. For example, we show you that you can increase the power of an iPhone up to 59% while you transmit the song in Wi-Fi instead of 3G, we show that network programs going for walks within the history can lessen the electricity efficiency of an iPhone with the aid of up to 72% when in comparison to real idle kingdom. Our focus commentary sheds light on eating up the battery existence of a smartphone and led us to offer optimization strategies to increase the battery lifestyles.