The Community for Technology Leaders
RSS Icon
Issue No.08 - Aug. (2013 vol.12)
pp: 1472-1486
Suman Nath , Microsoft Research, Redmond
We propose an acquisitional context engine (ACE), a middleware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. The ACE provides user's current context to applications running on it. In addition, it dynamically learns relationships among various context attributes (e.g., whenever the user is Driving, he is not AtHome). The ACE exploits these automatically learned relationships for two powerful optimizations. The first is inference caching that allows the ACE to opportunistically infer one context attribute (AtHome) from another already-known attribute (Driving), without acquiring any sensor data. The second optimization is speculative sensing that enables the ACE to occasionally infer the value of an expensive attribute (e.g., AtHome) by sensing cheaper attributes (e.g., Driving). Our experiments with two real context traces of 105 people and a Windows Phone prototype show that the ACE can reduce sensing costs of three context-aware applications by about 4.2 times, compared to a raw sensor data cache shared across applications, with a very small memory and processing overhead.
Context, Sensors, IEEE 802.11 Standards, History, Association rules, Context-aware services, Smart phones, rule-based optimizations, Continuous context-aware applications, sensor-rich mobile environment
Suman Nath, "ACE: Exploiting Correlation for Energy-Efficient and Continuous Context Sensing", IEEE Transactions on Mobile Computing, vol.12, no. 8, pp. 1472-1486, Aug. 2013, doi:10.1109/TMC.2013.12
[1] R. Agrawal, T. Imieliński, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, 1993.
[2] M. Azizyan, I. Constandache, and R. Roy Choudhury, "Surroundsense: Mobile Phone Localization via Ambience Fingerprinting," Proc. ACM MobiCom, 2009.
[3] N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner, "Virtual Compass: Relative Positioning to Sense Mobile Social Interactions," Proc. Int'l Conf. Pervasive Computing, 2010.
[4] D. Choujaa and N. Dulay, "TRAcME: Temporal Activity Recognition Using Mobile Phone Data," Proc. IEEE/IFIP Int'l Conf. Embedded and Ubiquitous Computing, 2008.
[5] E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, "MAUI: Making Smartphones Last Longer with Code Offload," Proc. Int'l Conf. Mobile Systems, Applications, and Services, 2010.
[6] A. Deshpande, C. Guestrin, W. Hong, and S. Madden, "Exploiting Correlated Attributes in Acquisitional Query Processing," Proc. Int'l Conf. Data Eng., 2005.
[7] N. Eagle, A. Pentland, and D. Lazer, "Inferring Social Network Structure Using Mobile Phone Data," Proc. Nat'l Academy of Sciences, vol. 106, pp. 15274-15278, 2009.
[8] M. Ficek and L. Kencl, "Spatial Extension of the Reality Mining Data Set," Proc. Int'l Conf. Mobile Ad Hoc and Sensor Systems (MASS), 2010.
[9] R. Greiner, R. Hayward, M. Jankowska, and M. Molloy, "Finding Optimal Satisfying Strategies for AND-OR Trees," Artificial Intelligence, vol. 170, pp. 19-58, Jan. 2006.
[10] H. Höpfner and K. Sattler, "Cache-Supported Processing of Queries in Mobile DBS," Proc. Workshop Database Mechanisms for Mobile Applications, pp. 106-121, 2003.
[11] L. Hammond, M. Willey, and K. Olukotun, "Data Speculation Support for a Chip Multiprocessor," Proc. Int'l Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), 1998.
[12] S. Kang, J. Lee, H. Jang, H. Lee, Y. Lee, S. Park, T. Park, and J. Song, "SeeMon: Scalable and Energy-Efficient Context Monitoring Framework for Sensor-Rich Mobile Environments," Proc. ACM Int'l Conf. Mobile Systems, Applications, and Services, 2008.
[13] J.R. Kwapisz, G.M. Weiss, and S.A. Moore, "Activity Recognition Using Cell Phone Accelerometers," ACM SIGKDD Explorations Newsletter, vol. 12, pp. 74-82, Mar. 2011.
[14] N.D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A.T. Campbell, "A Survey of Mobile Phone Sensing," IEEE Comm. Magazine, vol. 48, no. 9, pp. 140-150, Sept. 2010.
[15] K. Lin, A. Kansal, D. Lymberopoulos, and F. Zhao, "Energy-Accuracy Trade-Off for Continuous Mobile Device Location," Proc. ACM Int'l Conf. Mobile Systems, Applications, and Services, 2010.
[16] H. Lu, W. Pan, N.D. Lane, T. Choudhury, and A.T. Campbell, "SoundSense: Scalable Sound Sensing for People-Centric Applications on Mobile Phones," Proc. ACM Int'l Conf. Mobile Systems, Applications, and Services, 2009.
[17] H. Lu, J. Yang, Z. Liu, N.D. Lane, T. Choudhury, and A.T. Campbell, "The Jigsaw Continuous Sensing Engine for Mobile Phone Applications," Proc. ACM Int'l Conf. Embedded Networked Sensor Systems, 2010.
[18] E. Miluzzo, N.D. Lane, K. Fodor, R. Peterson, H. Lu, M. Musolesi, S.B. Eisenman, X. Zheng, and A.T. Campbell, "Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application," Proc. ACM Int'l Conf. Embedded Networked Sensor Systems, 2008.
[19] M. Mun, S. Reddy, K. Shilton, N. Yau, J. Burke, D. Estrin, M. Hansen, E. Howard, R. West, and P. Boda, "PEIR, the Personal Environmental Impact Report, as a Platform for Participatory Sensing Systems Research," Proc. ACM Int'l Conf. Mobile Systems, Applications, and Services, 2009.
[20] K. Munagala, S. Babu, R. Motwani, and J. Widom, "The Pipelined Set Cover Problem," Proc. Int'l Conf. Database Theory, 2005.
[21] E.B. Nightingale, P.M. Chen, and J. Flinn, "Speculative Execution in a Distributed File System," ACM Trans. Computer Systems, vol. 24, pp. 361-392, Nov. 2006.
[22] C. Qin, X. Bao, R.R. Choudhury, and S. Nelakuditi, "TagSense: A Smartphone Based Approach to Automatic Image Tagging," Proc. ACM Int'l Conf. Mobile Systems, Applications, and Services, 2011.
[23] B. Schilit, N. Adams, and R. Want, "Context-Aware Computing Applications," Proc. First Workshop Mobile Computing Systems and Applications, 1994.
[24] M. Schirmer and H. Höpfner, "SENST∗: Approaches for Reducing the Energy Consumption of Smartphone-Based Context Recognition," Proc. Int'l and Interdisciplinary Conf. Modeling and Using Context (CONTEXT), 2011.
[25] S.P. Tarzia, P.A. Dinda, R.P. Dick, and G. Memik, "Demo: Indoor Localization without Infrastructure Using the Acoustic Background Spectrum," Proc. ACM Int'l Conf. Mobile Systems, Applications, and Services, 2011.
[26] J.D. Ullman, "A Survey of Association-Rule Mining," Proc. Third Int'l Conf. Discovery Science, 2000.
[27] M. Wirz, D. Roggen, and G. Troster, "Decentralized Detection of Group Formations from Wearable Acceleration Sensors," Proc. Int'l Conf. Computational Science and Eng., vol. 4, 2009.
[28] M.J. Zaki, "Mining Non-Redundant Association Rules," Data Mining and Knowledge Discovery, vol. 9, no. 3, pp. 223-248, Nov. 2004.
22 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool