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Issue No.05 - May (2010 vol.9)
pp: 686-702
Jinwon Lee , Korea Advanced Institute of Science and Technology, Deajeon
Seungwoo Kang , Korea Advanced Institute of Science and Technology, Deajeon
Youngki Lee , Korea Advanced Institute of Science and Technology, Deajeon
Souneil Park , Korea Advanced Institute of Science and Technology, Deajeon
Junehwa Song , Korea Advanced Institute of Science and Technology, Deajeon
The key feature of many emerging pervasive computing applications is to proactively provide services to mobile individuals. One major challenge in providing users with proactive services lies in continuously monitoring users' context based on numerous sensors in their PAN/BAN environments. The context monitoring in such environments imposes heavy workloads on mobile devices and sensor nodes with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the mobile device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency.
Context monitoring, shared and incremental processing, sensor control, energy efficiency, personal computing, portable devices, ubiquitous computing, wireless sensor network, pervasive computing.
Jinwon Lee, Seungwoo Kang, Youngki Lee, Souneil Park, Junehwa Song, "A Scalable and Energy-Efficient Context Monitoring Framework for Mobile Personal Sensor Networks", IEEE Transactions on Mobile Computing, vol.9, no. 5, pp. 686-702, May 2010, doi:10.1109/TMC.2009.154
[1] K.V. Laerhoven, A. Schmidt, and H. Gellersen, "Multi-Sensor Context Aware Clothing," Proc. Int'l Symp. Wearable Computers, 2002.
[2] O. Amft et al., "Analysis of Chewing Sounds for Dietary Monitoring," Proc. Conf. Ubiquitous Computing (UbiComp), 2005.
[3] C. Park et al., "A Wearable Wireless Sensor Platform for Interactive Dance Performances," Proc. Int'l Conf. Pervasive Computing and Comm. (PerCom), 2006.
[4] M. Sung, C. Marci, and A. Pentland, "Wearable Feedback Systems for Rehabilitation," J. Neuro Eng. and Rehabilitation, vol. 2, no. 1, 2005.
[5] J.E. Bardram, "Applications of Context-Aware Computing in Hospital Work—Examples and Design Principles," Proc. ACM Symp. Applied Computing (SAC), 2004.
[6] T. Sohn et al., "Place-Its: A Study of Location-Based Reminders on Mobile Phones," Proc. Conf. Ubiquitous Computing (UbiComp), 2005.
[7] L. Bao and S.S. Intille, "Activity Recognition from User-Annotated Acceleration Data," Proc. Pervasive, 2004.
[8] J. Lester et al., "A Practical Approach to Recognizing Physical Activities," Proc. Pervasive, 2006.
[9] P. Fahy and S. Clarke, "CASS—A Middleware for Mobile Context-Aware Applications," Proc. MobiSys, 2004.
[10] T. Gu et al., "A Middleware for Building Context-Aware Mobile Services," Proc. IEEE Vehicular Technology Conf. (VTC), 2004.
[11] H. Chen, T. Finin, and A. Joshi, "An Ontology for Context-Aware Pervasive Computing Environments," Proc. Workshop Ontologies in Agent Systems (AAMAS), 2003.
[12] D. Salber, A.K. Dey, and G.D. Abowd, "The Context Toolkit: Aiding the Development of Context-Enabled Applications," Proc. ACM CHI, 1999.
[13] A. Ranganathan and R.H. Campbell, "A Middleware for Context-Aware Agents in Ubiquitous Computing Environments," Proc. Conf. Middleware, 2003.
[14] P. Korpipää et al., "Managing Context Information in Mobile Devices," IEEE Pervasive Computing, vol. 2, no. 3, pp. 42-51, 2003.
[15] T. Hofer et al., "Context-Awareness on Mobile Devices—The Hydrogen Approach," Proc. Hawaii Int'l Conf. System Sciences, 2003.
[16] O. Riva, "Contory: A Middleware for the Provisioning of Context Information on Smart Phones," Proc. Conf. Middleware, 2006.
[17] E. Shih et al., "Wake-on-Wireless: An Event Driven Energy Saving Strategy for Battery Operated Devices," Proc. ACM MobiCom, 2002.
[18] J. Sorber et al., "Turducken: Hierarchical Power Management for Mobile Devices," Proc. MobiSys, 2005.
[19] A. Rahmati and L. Zhong, "Context-for-Wireless: Context-Sensitive Energy-Efficient Wireless Data Transfer," Proc. MobiSys, 2007.
[20] S. Chakraborty et al., "On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication," IEEE Trans. Mobile Computing, vol. 5, no. 2, pp. 157-169, Feb. 2006.
[21] S. Cui et al., "Energy-Efficiency of MIMO and Cooperative MIMO Techniques in Sensor Networks," IEEE J. Selected Areas Comm., vol. 22, no. 6, pp. 1089-1098, 2004.
[22] W. Ye, J. Heidemann, and D. Estrin, "An Energy-Efficient MAC Protocol for Wireless Sensor Networks," Proc. IEEE INFOCOM, 2002.
[23] K. Seada et al., "Energy-Efficient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor Networks," Proc. Int'l Conf. Embedded Networked Sensor Systems (SenSys), 2004.
[24] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
[25] G. Anastasi et al., "Performance Measurements of Motes Sensor Networks," Proc. Int'l Symp. Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), 2004.
[26] G. Xing et al., "Minimum Power Configuration for Wireless Communication in Sensor Networks," ACM Trans. Sensor Networks (TOSN), vol. 3, no. 2, 2007.
[27] K.L. Wu and P.S. Yu, "Interval Query Indexing for Efficient Stream Processing," Proc. Int'l Conf. Information and Knowledge Management (CIKM), 2004.
[28] E. Hanson and T. Johnson, "Selection Predicate Indexing for Active Databases Using Interval Skip Lists," Information Systems, vol. 21, no. 3, pp. 269-298, 1996.
[29] J. Lee et al., "BMQ-Index: Shared and Incremental Processing of Border Monitoring Queries over Data Streams," Proc. Conf. Mobile Data Management (MDM), 2006.
[30] KAIST UFC Project, http:/, 2008.
[31] HUINS, http:/, 2009.
[32] MIT Affective, , 2009.
[33] FFTW, http:/, 2009.
[34] Weka 3: Data Mining Software in Java, http://www.cs.waikato. , 2009.
[35] Next Generation Computing Show, http://www.nextcomshow. comen, 2007.
[36] V. Shnayder et al., "Simulating the Power Consumption of Large-Scale Sensor Network Applications," Proc. Int'l Conf. Embedded Networked Sensor Systems (SenSys), 2004.
[37] R.S. Sandhu and P. Samarati, "Access Control: Principles and Practice," IEEE Comm. Magazine, 1994.
[38] D. Abadi et al., "Aurora: A New Model and Architecture for Data Stream Management," Very Large Data Bases J., vol. 12, no. 2, 2003.
[39] R. Motwani et al., "Query Processing, Resource Management, and Approximation in a Data Stream Management System," Proc. Conf. Innovative Data Systems Research (CIDR), 2003.
[40] S.R. Madden et al., "Continuously Adaptive Continuous Queries over Streams," Proc. SIGMOD, 2002.
[41] J. Froehlich et al., "MyExperience: A System for In Situ Tracing and Capturing of User Feedback on Mobile Phones," Proc. MobiSys, 2007.
[42] P.J. Lang et al., "International Affective Picture System (IAPS): Instruction Manual and Affective Ratings," Technical Report A-4, Center for Research in Psychophysiology, Univ. of Florida, 1999.
[43] K. Murao et al., "A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption," Proc. Pervasive, 2008.
[44] CC2420 Datasheet, cc2420.html, 2009.
[45] S. Kang et al., "SeeMon: Scalable and Energy-Efficient Context Monitoring Framework for Sensor-Rich Mobile Environments," Proc. MobiSys, 2008.
[46] V. Chvatal, "A Greedy Heuristic for the Set-Covering Problem," Math. Operations Research, vol. 4, no. 3, pp. 233-235, 1979.
[47] P. Slavik, "A Tight Analysis of the Greedy Algorithm for Set Cover," Proc. Symp. Theory of Computing (STOC), 1997.
[48] M. Popescu and E. Florea, "Linking Clinical Events in Elderly to In-Home Monitoring Sensor Data: A Brief Review and a Pilot Study on Predicting Pulse Pressure," J. Computing Science and Engineering, vol. 2, no. 1, pp. 180-199, Mar. 2008.
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