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SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring
March 2014 (vol. 13 no. 3)
pp. 512-525
Yohan Chon, Yonsei University, Seoul
Elmurod Talipov, Yonsei University, Seoul
Hyojeong Shin, Yonsei University, Seoul
Hojung Cha, Yonsei University, Seoul
Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with predictions of temporal behavior in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81 percent less energy than the periodic sensing schemes, and 87 percent less energy than a scheme employing context-aware sensing, yet it still correctly monitors 90 percent of a user's location changes within a 160-second delay.
Index Terms:
Sensors,Energy consumption,Global Positioning System,Monitoring,IEEE 802.11 Standards,Accuracy,Humans,energy efficient,Location,mobility learning,mobility prediction,adaptive sensing
Citation:
Yohan Chon, Elmurod Talipov, Hyojeong Shin, Hojung Cha, "SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring," IEEE Transactions on Mobile Computing, vol. 13, no. 3, pp. 512-525, March 2014, doi:10.1109/TMC.2013.14
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