Abstract
Although mobile health monitoring where mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient’s sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. This paper presents an attempt to decompose the very complex mobile health monitoring system whose layer in the system corresponds to decomposed subproblems, and interfaces between them are quantified as functions of the optimization variables in order to orchestrate the subproblems. We propose a distributed and energy-saving mobile health platform, called mHealthMon where mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the mobile health monitoring application’s quality of service requirements by modeling each subsystem: mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone mobile health monitoring application, in various mobile health monitoring scenarios applying a realistic mobility model.











Similar content being viewed by others
References
Lv, Z., Xia, F., Wu, G., Yao, L., and Chen, Z., iCare: A Mobile Health Monitoring System for the Elderly. In: GreenCom, pp. 699–705, 2010.
U.S. Census Bureau. TIGER. Available at www.census.gov/geo/www/tiger.
Chun, B-G., Ihm, S., Maniatis, P., Naik, M., and Patti, A., CloneCloud: Elastic Execution between Mobile Device and Cloud. EuroSys, pp. 301–314, 2011.
Chowdhury, A. R., and Falchuk, B., MediAlIy: A Provenance-Aware Remote Health Monitoring Middleware. In: PerCom, pp. 125–134, 2010.
Cuervoy, E., Balasubramanianz, A., Cho, D-K., Wolmanx, A., Saroiux, S., Chandrax, R., and Bahl, P., MAUI: Making Smartphones Last Longer with Code Offload. In: MobiSys, pp. 49–62, 2010.
Manku, G. S., Bawa, M., and Raghavan, P., Symphony: Distributed Hashing in a Small World. USITS, Vol 4, pp. 10–10, 2003.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N., The Case for VM-Based Cloudlets in Mobile Computing. IEEE Pervasive Computing, 8(4):14–23, 2009.
Lukowicz, P., AMON: A Wearable Medical Computer for High Risk Patients. In: ISWC, pp. 133–134, 2002.
Lubrin, E., Lawrence, E., and Navarro, K. E., Motecare: an adaptive smart ban health monitoring system. In: BioMed, pp. 60–67, 2006.
Kim, Y. B., Kim, M., and Lee, Y., COSMOS: a middleware platform for sensor networks and a u-healthcare service. In: SAC, pp. 512–513, 2008.
Rodriguez, J., Goni, A., and Illarramendi, A., Real-Time Classification of ECGs on a PDA, IEEE Transactions on Information Technology. In: Biomedicine, Vol. 9, pp. 23–34, 2005.
Wu, W., Cao, J., and Zheng, Y., WAITER: A wearable personal healthcare and emergency aid system. In: PerCom, pp. 680–685, 2008.
Pantelopoulos, A., and Bourbakis, N. G., Prognosis - A Wearable Health Monitoring System for People at Risk: Methodology and Modeling. In T-ITB, 14(3):613–21, 2010.
Gay, V., Leijdekkers, P., and Barin, E., A Mobile Rehabilitation Application for the Remote Monitoring of Cardiac Patients after a Heart Attack or a Coronary Bypass Surgery. In PETRA, No. 21, 2009.
Grossetete, P., ArchRock Energy Optimizer: a case study on IP WSN data for energy and environmental monitoring. In DMSN, No. 2, 2009.
Ahnn, J. H., Lee, U., and Moon, H. J., GeoServ: A Distributed Urban Sensing Platform. CCGRID, pp. 164–173, 2011.
Reddy, S., Chen, G., Fulkerson, B., Kim, S. J., Park, U., Yau, N., Cho, J., Sensor-Internet Share and Search – Enabling Collaboration of Citizen Scientists. In: DSI, pp. 11–16, 2007.
Nath, S., Liu, J., and Zhao, F., SensorMap for Wide-Area Sensor Webs. In IEEE Computer Magazine, 40(7):90–93, 2007.
Buhler, J., and Wunder, G., An optimization framework for heterogeneous access management. WCNC, pp. 2525–2530, 2009.
Harrell, F., Regression modeling strategies. Springer, 2001.
Blau, I., Wunder, G., Karla, I., and Sigle, R., Decentralized Utility Maximization in Heterogeneous Multicell Scenario with Interface Limited Orthogonal Air Interfaces. EURASIP, No. 2, 2009.
Härri, J., Fiore, M., and Fethi, F., VanetMobiSim: Generating Realistic Mobility Patterns. VANET, pp. 96–97, 2006.
Zhang, X., Jeong, S., Kunjithapatham, A., and Simon Gibbs. Towards an Elastic Application Model for Augmenting Computing Capabilities of Mobile Platforms. In Mobileware, 16(3):270–284, 2010.
Giurgiu, I., Riva, O., Juric, D., Krivulev, I., and Alonso, G., Calling the Cloud: Enabling Mobile Phones as Interfaces to Cloud Applications. In Middleware, pp. 83-102, 2009.
Boyd, S., and Vandenberghe, L., Convex Optimization. Cambridge University Press, New York, 2004.
Acknowledgments
This work was in part funded by Samsung Advanced Institute of Technology under grant numbers 290112465.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ahnn, J.H., Potkonjak, M. mHealthMon: Toward Energy-Efficient and Distributed Mobile Health Monitoring Using Parallel Offloading. J Med Syst 37, 9957 (2013). https://doi.org/10.1007/s10916-013-9957-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-013-9957-0