The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2014 vol.13)
pp: 512-525
Yohan Chon , Yonsei University, Seoul
Elmurod Talipov , Yonsei University, Seoul
Hyojeong Shin , Yonsei University, Seoul
Hojung Cha , Yonsei University, Seoul
ABSTRACT
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
REFERENCES
[1] Y. Chon, E. Talipov, H. Shin, and H. Cha, "Mobility Prediction-Based Smartphone Energy Optimization for Everyday Location Monitoring," Proc. Ninth ACM Conf. Embedded Networked Sensor Systems, pp. 82-95, 2011.
[2] M.C. Gonzalez, C. Hidalgo, and A.-L. Barabasi, "Understanding Individual Human Mobility Patterns," Nature, vol. 453, no. 7196, pp. 779-782, June 2008.
[3] C. Song, Z. Qu, N. Blumm, and A.-L. Barabasi, "Limits of Predictability in Human Mobility," Science, vol. 327, no. 5968, pp. 1018-1021, 2010.
[4] L. Song, D. Kotz, R. Jain, and X. He, "Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data," IEEE Trans. Mobile Computing, vol. 5, no. 12, pp. 1633-1649, Dec. 2006.
[5] P. Wang, M.C.G. amd C.A. Hidalgo, and A.-L. Barabasi, "Understanding the Spreading Patterns of Mobile Phone Viruses," Science, vol. 324, no. 5930, pp. 1071-1076, 2009.
[6] F. Alizadeh-Shabdiz and E.J. Morgan, "System and Method for Estimating Positioning Error within a WLAN-Based Positioning System," US Patent 2008/0 108 371 A1, 2008.
[7] A. LaMarca et al., "Place Lab: Device Positioning Using Radio Beacons in the Wild," Proc. Third Int'l Conf. Pervasive Computing, pp. 116-133, 2005.
[8] M. Azizyan, I. Constandache, and R.R. Choudhury, "Surroundsense: Mobile Phone Localization via Ambience Fingerprinting," Proc. 15th Ann. Int'l Conf. Mobile Computing and Networking, pp. 261-272, 2009.
[9] D.H. Kim, Y. Kim, D. Estrin, and M.B. Srivastava, "SensLoc: Sensing Everyday Places and Paths Using Less Energy," Proc. Eighth ACM Conf. Embedded Networked Sensor Systems, pp. 43-56, 2010.
[10] H. Lu et al., "The Jigsaw Continuous Sensing Engine for Mobile Phone Applications," Proc. Eighth ACM Conf. Embedded Networked Sensor Systems, pp. 71-84, 2010.
[11] J. Paek, J. Kim, and R. Govindan, "Energy-Efficient Rate-Adaptive GPS-Based Positioning for Smartphones," Proc. Eight Int'l Conf. Mobile Systems, Applications, and Services, pp. 299-314, 2010.
[12] Z. Zhuang, K.-H. Kim, and J.P. Singh, "Improving Energy Efficiency of Location Sensing on Smartphones," Proc. Eighth Int'l Conf. Mobile Systems, Applications, and Services, pp. 315-330, 2010.
[13] J. Paek, K.-H. Kim, J.P. Singh, and R. Govindan, "Energy-Efficient Positioning for Smartphones Using Cell-ID Sequence Matching," Proc. Ninth Int'l Conf. Mobile Systems, Applications, and Services, pp. 293-306, 2011.
[14] I. Constandache, S. Gaonkar, M. Sayler, R. Choudhury, and L. Cox, "EnLoc: Energy-Efficient Localization for Mobile Phones," Proc. IEEE INFOCOM, pp. 2716-2720, 2009.
[15] K. Lin, A. Kansal, D. Lymberopoulos, and F. Zhao, "Energy-Accuracy Trade-Off for Continuous Mobile Device Location," Proc. Eighth Int'l Conf. Mobile Systems, Applications, and Services, pp. 285-298, 2010.
[16] Y. Ma, R. Hankins, and D. Racz, "iLoc: A Framework for Incremental Location-State Acquisition and Prediction Based on Mobile Sensors," Proc. 18th ACM Conf. Information and Knowledge Management, pp. 1367-1376, 2009.
[17] N. Klepeis et al., "The National Human Activity Pattern Survey (NHAPS): A Resource for Assessing Exposure to Environmental Pollutants," J. Exposure Analysis and Environmental Epidemiology, vol. 11, no. 3, pp. 231-252, 2001.
[18] Y. Chon and H. Cha, "LifeMap: A Smartphone-Based Context Provider for Location-Based Services," IEEE Pervasive Computing, vol. 10, no. 2, pp. 58-67, Apr.-June 2011.
[19] Y. Chon, E. Talipov, and H. Cha, "Autonomous Management of Everyday Places for Personalized Location Provider," IEEE Trans. Systems, Man, and Cybernetics Part C: Applications Rev., vol. 42, no. 4, pp. 518-531, July 2012.
[20] S. Isaacman et al., "Human Mobility Modeling at Metropolitan Scales," Proc. ACM 10th Int'l Conf. Mobile Systems, Applications, and Services, pp. 239-252, 2012.
[21] A. Nicholson and B. Noble, "BreadCrumbs: Forecasting Mobile Connectivity," Proc. 14th Ann. Int'l Conf. Mobile Computing and Networking, pp. 46-57, 2008.
[22] J.-K. Lee and J.C. Hou, "Modeling Steady-State and Transient Behaviors of User Mobility: Formulation, Analysis, and Application," Proc. Seventh ACM Int'l Symp. Mobile Ad Hoc Networking and Computing, pp. 85-96, 2006.
[23] L. Song, U. Deshpande, U.C. Kozat, D. Kotz, and R. Jain, "Predictability of WLAN Mobility and Its Effects on Bandwidth Provisioning," Proc. IEEE INFOCOM, pp. 1-13, 2006.
[24] S. Scellato, M. Musolesi, C. Mascolo, V. Latora, and A.T. Campbell, "NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems," Proc. Ninth Int'l Conf. Pervasive Computing, 2011.
[25] B. Priyantha, D. Lymberopoulos, and J. Liu, "LittleRock: Enabling Energy-Efficient Continuous Sensing on Mobile Phones," IEEE Pervasive Computing, vol. 10, no. 2, pp. 12-15, Apr.-June 2011.
[26] N. Lane et al., "A Survey of Mobile Phone Sensing," IEEE Comm., vol. 48, no. 9, pp. 140-150, Sept. 2010.
[27] H. Falaki et al., "Diversity in Smartphone Usage," Proc. Seventh Int'l Conf. Mobile Systems, Applications, and Services, pp. 179-194, 2010.
[28] D.H. Kim, J. Hightower, R. Govindan, and D. Estrin, "Discovering Semantically Meaningful Places from Pervasive RF-Beacons," Proc. 11th Int'l Conf. Ubiquitous Computing, pp. 21-30, 2009.
[29] P. Jaccard, "The Distribution of the Flora in the Alpine Zone," New Phytologist, vol. 11, no. 2, pp. 37-50, 1912.
[30] D. Kotz and T. Henderson, "CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth," IEEE Pervasive Computing, vol. 4, no. 4, pp. 12-14, Oct.-Dec. 2005.
68 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool