Users and Batteries: Interactions and Adaptive Energy Management in Mobile Systems

Banerjee, Nilanjan; Rahmati, Ahmad; Corner, Mark; Rollins, Sami; Lin, Zhouchen

doi:10.1007/978-3-540-74853-3_13

Cited by 77 publications

(70 citation statements)

References 16 publications

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“…To the best of our knowledge, ECOSystem [13] is the only piece of work that takes this approach. Banerjee et al [4] show through user studies that a considerable portion of battery recharges are driven by context but do not solve the problem of associating context with recharges. On mobile devices, the interfaces that inform the user of the battery levels (such as ACPI [2]) have not kept up with the evolution of the capabilities of these devices for context-sensing, and the always-on multi-functional roles they promise to play in the near future.…”

Section: Related Workmentioning

confidence: 99%

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Context-aware Battery Management for Mobile Phones

Ravi

Scott

Han

et al. 2008

2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom)

108

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Section: Related Workmentioning

confidence: 99%

“…The second observation is that context information (location and time) determines battery recharge schedules [4].…”

Section: Introductionmentioning

confidence: 99%

Context-aware Battery Management for Mobile Phones

Ravi

Scott

Han

et al. 2008

2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom)

108

View full text Add to dashboard Cite

show abstract

“…Banerjee et al [9] presented the Llama system to manage battery energy for a mobile phone. User behavior was studied carefully in the aspect of battery usage and recharging behavior.…”

Section: Previous Workmentioning

confidence: 99%

Two Machine Learning Models for Mobile Phone Battery Discharge Rate Prediction Based on Usage Patterns

2016

J Inf Process Syst

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This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

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“…Context has additionally been widely misused to give improved framework proficiency and execution, for example, for energy administration [8], [9] and system choice [10]. These outlines misuse the context dependency of mobile use and mobile assets for particular purposes, and show noteworthy, evaluated, execution picks up.…”

Section: Introductionmentioning

confidence: 99%

Functional Context Awareness: Measuring and Utilizing the Context Dependency of Mobile Usage

2016

IJSR

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Context data brings new open doors for productive and compelling applications and services on mobile devices. An extensive variety of examination has abused context dependency, i.e. the relations in the middle of context(s) and the result, to accomplish huge, evaluated, execution picks up for an assortment of applications and services. These works ordinarily need to manage the difficulties of numerous context sources prompting an inadequate preparing dataset, and the difficulties of energy hungry context sensors. Regularly, they address these difficulties in an application particular and specially appointed way. We free mobile application planners and analysts from these weights by giving a systematic way to deal with these difficulties. Specifically, we 1) characterize and measure the context-dependency of three central sorts of mobile use (went by sites, telephone calls, and application use) in an application freethinker yet viable way, giving understanding into the execution of potential application. 2) Address the test of information meager condition when managing various context sources in an efficient way. 3) Present SmartContext to address the energy challenge via consequently selecting among context sources while guaranteeing a base precision for every estimation. Our examination and discoveries depend on one year of utilization and context follows gathered, in actuality, settings from 24 iPhone clients. We show discoveries with respect to the context dependency of three sorts of mobile utilization from 24 clients, yet our philosophy and the lessons we learn can be promptly stretched out to different sorts of use and additionally framework assets. Our discoveries control the improvement of context mindful frameworks, and highlight the difficulties and assumptions in regards to the context dependency of mobile use.

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