Suman Nath
ABSTRACT
We propose ACE (Acquisitional Context Engine), a middleware that supports continuous context-aware applications while mitigating sensing costs for inferring contexts. 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). ACE exploits these automatically learned relationships for two powerful optimizations. The first is inference caching that allows 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 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 ACE can reduce sensing costs of three context-aware applications by about 4.2x, compared to a raw sensor data cache shared across applications, with a very small memory and processing overhead.
- Agrawal, R., Imieliński, T., and Swami, A. Mining association rules between sets of items in large databases. In ACM SIGMOD (1993). Google Scholar
Digital Library
- Azizyan, M., Constandache, I., and Roy Choudhury, R. Surroundsense: mobile phone localization via ambience fingerprinting. In ACM MobiCom (2009). Google ScholarDigital Library
- Banerjee, N., Agarwal, S., Bahl, P., Chandra, R., Wolman, A., and Corner, M. Virtual compass: Relative positioning to sense mobile social interactions. In Pervasive (2010). Google ScholarDigital Library
- Choujaa, D., and Dulay, N. TRAcME: Temporal activity recognition using mobile phone data. In IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (2008). Google ScholarDigital Library
- Cuervo, E., Balasubramanian, A., Cho, D., Wolman, A., Saroiu, S., Chandra, R., and Bahl, P. Maui: making smartphones last longer with code offload. In MobiSys (2010). Google ScholarDigital Library
- Deshpande, A., Guestrin, C., Hong, W., and Madden, S. Exploiting correlated attributes in acquisitional query processing. In ICDE (2005). Google ScholarDigital Library
- Eagle, N., Pentland, A., and Lazer, D. Inferring social network structure using mobile phone data. In Proceedings of the National Academy of Sciences (PNAS) (2009), vol. 106, pp. 15274--15278.Google Scholar
- Ficek, M., and Kencl, L. Spatial extension of the reality mining dataset. In International Conference on Mobile Adhoc and Sensor Systems (MASS) (2010).Google ScholarCross Ref
- Greiner, R., Hayward, R., Jankowska, M., and Molloy, M. Finding optimal satisficing strategies for and-or trees. Artificial Intelligence 170 (January 2006), 19--58. Google ScholarDigital Library
- H. Höpfner, K. S. Cache-supported processing of queries in mobile dbs. Database Mechanisms for Mobile Applications (2003), 106--121.Google Scholar
- Hammond, L., Willey, M., and Olukotun, K. Data speculation support for a chip multiprocessor. In ASPLOS (1998). Google ScholarDigital Library
- Kang, S., Lee, J., Jang, H., Lee, H., Lee, Y., Park, S., Park, T., and Song, J. SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments. In ACM MobiSys (2008). Google ScholarDigital Library
- Kwapisz, J. R., Weiss, G. M., and Moore, S. A. Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12 (March 2011), 74--82. Google ScholarDigital Library
- Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., and Campbell, A. T. A survey of mobile phone sensing. Comm. Mag. 48 (September 2010), 140--150. Google ScholarDigital Library
- Lin, K., Kansal, A., Lymberopoulos, D., and Zhao, F. Energy-accuracy trade-off for continuous mobile device location. In ACM MobiSys (2010). Google ScholarDigital Library
- Lu, H., Pan, W., Lane, N. D., Choudhury, T., and Campbell, A. T. SoundSense: scalable sound sensing for people-centric applications on mobile phones. In MobiSys (2009). Google ScholarDigital Library
- Lu, H., Yang, J., Liu, Z., Lane, N. D., Choudhury, T., and Campbell, A. T. The jigsaw continuous sensing engine for mobile phone applications. In ACM SenSys (2010). Google ScholarDigital Library
- Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S. B., Zheng, X., and Campbell, A. T. Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In ACM SenSys (2008). Google ScholarDigital Library
- Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., and Boda, P. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In ACM MobiSys (2009). Google ScholarDigital Library
- Munagala, K., Babu, S., Motwani, R., and Widom, J. The pipelined set cover problem. In ICDT (2005). Google ScholarDigital Library
- Nightingale, E. B., Chen, P. M., and Flinn, J. Speculative execution in a distributed file system. ACM Trans. Comput. Syst. 24 (November 2006), 361--392. Google ScholarDigital Library
- Qin, C., Bao, X., Choudhury, R. R., and Nelakuditi, S. TagSense: A smartphone based approach to automatic image tagging. In ACM MobiSys (2011). Google ScholarDigital Library
- Schilit, B., Adams, N., and Want, R. Context-aware computing applications. In Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications (1994). Google ScholarDigital Library
- Schirmer, M., and Höpfner, H. SENST*: approaches for reducing the energy consumption of smartphone-based context recognition. In CONTEXT (2011). Google ScholarDigital Library
- Tarzia, S. P., Dinda, P. A., Dick, R. P., and Memik, G. Indoor localization without infrastructure using the acoustic background spectrum. In ACM MobiSys (2011). Google ScholarDigital Library
- Ullman, J. D. A survey of association-rule mining. In Third International Conference on Discovery Science (2000). Google ScholarDigital Library
- Wirz, M., Roggen, D., and Troster, G. Decentralized detection of group formations from wearable acceleration sensors. In Intl. Conf. on Computational Science and Engineering - Volume 04 (2009). Google ScholarDigital Library
- Zaki, M. J. Mining non-redundant association rules. Data Mining and Knowledge Discovery 9, 3 (Nov 2004), 223--248. Google ScholarDigital Library
Index Terms
- ACE: exploiting correlation for energy-efficient and continuous context sensing
Recommendations
ACE: Exploiting Correlation for Energy-Efficient and Continuous Context Sensing
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, ...
SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments
MobiSys '08: Proceedings of the 6th international conference on Mobile systems, applications, and servicesProactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context ...
Design, implementation, and verification of active cache emulator (ACE)
FPGA '06: Proceedings of the 2006 ACM/SIGDA 14th international symposium on Field programmable gate arraysThis paper presents the design, implementation, and verification of the Active Cache Emulator (ACE), a novel FPGA-based emulator that models an L3 cache actively and in real-time. ACE leverages interactions with its host system to model the target ...
Comments