Predicting smartphone battery usage using cell tower ID monitoring

Yohan Chon, Wanchang Ryu, Hojung Cha

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Personalized power management in mobile devices is a critical issue in handling the diversity of smartphone usage. In particular, usage prediction is important for the efficient use of remaining battery capacity. In this paper, we propose a system that predicts the amount of battery usage required in the future. Our key insight is that a high degree of correlation exists between battery usage and a user's movements. We design an everyday location monitoring system that only uses cell-tower connections, without additional energy consumption. The technical challenge is eliminating the ping-pong effect in a series of cell-tower transitions to determine the mobility status, especially with limited access to the list of neighboring cell towers. We construct a graph from the sequence of recorded cell towers and recognize the points of interest using a partial clique graph. We use the Markov predictor to estimate the required battery level depending on the user's movements. We demonstrate the accuracy of battery usage prediction using real traces of participants collected over a period of four weeks. The result shows that the proposed system correctly predicts the battery usage of smartphones, with an 8.1±7.5% margin of error.

Original languageEnglish
Pages (from-to)99-110
Number of pages12
JournalPervasive and Mobile Computing
Volume13
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Smartphones
Battery
Towers
Monitoring
Cell
Mobile devices
Clique Graphs
Predict
Power Management
Energy utilization
Prediction
Monitoring System
Mobile Devices
Margin
Energy Consumption
Predictors
Trace
Partial
Series
Graph in graph theory

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Applied Mathematics

Cite this

@article{eb73dfa65e9641b894a1fead75501622,
title = "Predicting smartphone battery usage using cell tower ID monitoring",
abstract = "Personalized power management in mobile devices is a critical issue in handling the diversity of smartphone usage. In particular, usage prediction is important for the efficient use of remaining battery capacity. In this paper, we propose a system that predicts the amount of battery usage required in the future. Our key insight is that a high degree of correlation exists between battery usage and a user's movements. We design an everyday location monitoring system that only uses cell-tower connections, without additional energy consumption. The technical challenge is eliminating the ping-pong effect in a series of cell-tower transitions to determine the mobility status, especially with limited access to the list of neighboring cell towers. We construct a graph from the sequence of recorded cell towers and recognize the points of interest using a partial clique graph. We use the Markov predictor to estimate the required battery level depending on the user's movements. We demonstrate the accuracy of battery usage prediction using real traces of participants collected over a period of four weeks. The result shows that the proposed system correctly predicts the battery usage of smartphones, with an 8.1±7.5{\%} margin of error.",
author = "Yohan Chon and Wanchang Ryu and Hojung Cha",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.pmcj.2013.06.003",
language = "English",
volume = "13",
pages = "99--110",
journal = "Pervasive and Mobile Computing",
issn = "1574-1192",
publisher = "Elsevier",

}

Predicting smartphone battery usage using cell tower ID monitoring. / Chon, Yohan; Ryu, Wanchang; Cha, Hojung.

In: Pervasive and Mobile Computing, Vol. 13, 01.01.2014, p. 99-110.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Predicting smartphone battery usage using cell tower ID monitoring

AU - Chon, Yohan

AU - Ryu, Wanchang

AU - Cha, Hojung

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Personalized power management in mobile devices is a critical issue in handling the diversity of smartphone usage. In particular, usage prediction is important for the efficient use of remaining battery capacity. In this paper, we propose a system that predicts the amount of battery usage required in the future. Our key insight is that a high degree of correlation exists between battery usage and a user's movements. We design an everyday location monitoring system that only uses cell-tower connections, without additional energy consumption. The technical challenge is eliminating the ping-pong effect in a series of cell-tower transitions to determine the mobility status, especially with limited access to the list of neighboring cell towers. We construct a graph from the sequence of recorded cell towers and recognize the points of interest using a partial clique graph. We use the Markov predictor to estimate the required battery level depending on the user's movements. We demonstrate the accuracy of battery usage prediction using real traces of participants collected over a period of four weeks. The result shows that the proposed system correctly predicts the battery usage of smartphones, with an 8.1±7.5% margin of error.

AB - Personalized power management in mobile devices is a critical issue in handling the diversity of smartphone usage. In particular, usage prediction is important for the efficient use of remaining battery capacity. In this paper, we propose a system that predicts the amount of battery usage required in the future. Our key insight is that a high degree of correlation exists between battery usage and a user's movements. We design an everyday location monitoring system that only uses cell-tower connections, without additional energy consumption. The technical challenge is eliminating the ping-pong effect in a series of cell-tower transitions to determine the mobility status, especially with limited access to the list of neighboring cell towers. We construct a graph from the sequence of recorded cell towers and recognize the points of interest using a partial clique graph. We use the Markov predictor to estimate the required battery level depending on the user's movements. We demonstrate the accuracy of battery usage prediction using real traces of participants collected over a period of four weeks. The result shows that the proposed system correctly predicts the battery usage of smartphones, with an 8.1±7.5% margin of error.

UR - http://www.scopus.com/inward/record.url?scp=84904678411&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904678411&partnerID=8YFLogxK

U2 - 10.1016/j.pmcj.2013.06.003

DO - 10.1016/j.pmcj.2013.06.003

M3 - Article

VL - 13

SP - 99

EP - 110

JO - Pervasive and Mobile Computing

JF - Pervasive and Mobile Computing

SN - 1574-1192

ER -