Xueying Li
Enhong Chen
Abstract
With the prevalence of smart mobile devices with multiple sensors, the commercial application of intelligent context-aware services becomes more and more attractive. However, limited by the battery capacity, the energy efficiency of context-sensing is the bottleneck for the success of context-aware applications. Though several previous studies for energy-efficient context-sensing have been reported, none of them can be applied to multiple types of high-energy-consuming sensors. Moreover, applying machine learning technologies to energy-efficient context-sensing is underexplored too. In this article, we propose to leverage machine learning technologies for improving the energy efficiency of multiple high-energy-consuming context sensors by trading off the sensing accuracy. To be specific, we try to infer the status of high-energy-consuming sensors according to the outputs of software-based sensors and the physical sensors that are necessary to work all the time for supporting the basic functions of mobile devices. If the inference indicates the high-energy-consuming sensor is in a stable status, we avoid the unnecessary invocation and instead use the latest invoked value as the estimation. The experimental results on real datasets show that the energy efficiency of GPS sensing and audio-level sensing are significantly improved by the proposed approach while the sensing accuracy is over 90%.
- Abdesslem, F. B., Phillips, A., and Henderson, T. 2009. Less is more: Energy-Efficient mobile sensing with senseless. In Proceedings of the 1st ACM Workshop on Networking, Systems, and Applications for Mobile Handhelds (MobiHeld). 61--62. Google Scholar
Digital Library
- Abowd, G. D., Atkeson, C. G., Hong, J., Long, S., Kooper, R., and Pinkerton, M. 1997. Cyberguide: A mobile context-aware tour guide. Wirel. Netw. 3, 5, 421--433. Google ScholarDigital Library
- Benini, L., Bogliolo, A., Paleologo, G. A., and De Micheli, G. 1999. Policy optimization for dynamic power management. IEEE Trans. Comput.-Aid. Des. Integr. Circ. Syst., 182--187. Google ScholarDigital Library
- Breiman, L., Friedman, J. H., Stone, C. J., and Olshen, R. A. 1984. Classification and Regression Trees. CRC Press.Google Scholar
- Campobello, G., Leonardi, A., and Palazzo, S. 2008. Ace: An emergent algorithm for highly uniform cluster formation. In Proceedings of the IEEE International Conference on Communications (ICC). 154--171.Google Scholar
- Campobello, G., Leonardi, A., and Palazzo, S. 2009. A novel reliable and energy-saving forwarding technique for wireless sensor networks. In Proceedings of the 10th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). 269--278. Google ScholarDigital Library
- Chan, H. and Perrig, A. 2008. On the use of chinese remainder theorem for energy saving in wireless sensor networks. In Proceedings of the 1st European Workshop on Sensor Networks (EWSN).Google Scholar
- Chang, C. and Lin, C. 2001. LIBSVM: A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Google ScholarDigital Library
- Chen, Y., Yang, Q., and Yin, J. 2006. Power-Efficient access point selection for indoor location estimation. IEEE Trans. Knowl. Data Engin. 18, 877--888. Google ScholarDigital Library
- Choi, W., Shah, P., and Das, S. 2004. Framework for energy-saving data gathering using two-phase clustering in wireless sensor networks. In Proceedings of the 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous).Google Scholar
- Chong, S. K., Gaber, M. M., and Krishnaswamy, S. 2005. A context-aware approach to conserving energy in wireless sensor networks. In Proceedings of the 1st International Workshop on Sensor Networks and Systems for Pervasive Computing. Google ScholarDigital Library
- Chong, S. K., Gaber, M. M., and Krishnaswamy, S. 2008. Arts: Adaptive rule triggers on sensors for energy conservation in applications using coarse-granularity data. In Proceedings of the International Conference on Embedded Software and Systems (ICESS). 314--321. Google ScholarDigital Library
- Constandache, I., Gaonkar, S., and Sayler, M. 2009. Enloc: Energy-Efficient localization for mobile phones. In Proceedings of the INFOCOM Conference. IEEE, 2716--2720.Google Scholar
- Cortes, C. and Vapnik, V. 1995. Support-vector networks. Mach. Learn. 20, 2, 273--297. Google ScholarDigital Library
- Derksen, S. and Keselman, H. J. 1992. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. Brit. J. Math. Statist. Psychol. 45, 265--282.Google ScholarCross Ref
- Fan, R. E., Chen, P. H., and Lin, C. J. 2005. Working set selection using second order information for training svm. J. Mach. Learn. Res. 6, 1889--1918. Google ScholarDigital Library
- Farrahi, K. and Gatica-Perez, D. 2011. Discovering routines from large-scale human locations using probabilistic topic models. ACM Trans. Intell. Syst. Technol. 2, 3:1--3:27. Google ScholarDigital Library
- Garg, R., Son, S. W., Kandemir, M., Raghavan, P., and Prabhakar, R. 2009. Markov model based disk power management for data intensive workloads. In Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID). 76--83. Google ScholarDigital Library
- Heinzelman, W., Chandrakasan, A., and Balakrishnan, H. 2000. Energy-Efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd International Conference on System Sciences (HICSS). Google ScholarDigital Library
- Jolliffe, T. 2002. Principal Component Analysis 2nd Ed. Springer.Google Scholar
- Kjærgaard, M. B., Langdal, J., and Godsk, T. 2009. Entracked: Energy-Efficient robust position tracking for mobile devices. In Proceedings of the 7th Annual International Conference on Mobile Systems, Applications and Services (MobiSys). 221--234. Google ScholarDigital Library
- Krause, A., Rajagopal, R., Gupta, A., and Guestrin, C. 2009. Simultaneous placement and scheduling of sensors. In Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN). 181--192. Google ScholarDigital Library
- Lemlouma, T. and Layaïda, N. 2004. Context-aware adaptation for mobile devices. In Proceedings of the 5th IEEE International Conference on Mobile Data Management (MDM). 106--111.Google Scholar
- NokiaContextData. 2004. http://www.pervasive.jku.at/Research/Context_Database/.Google Scholar
- Paek, J., Kim, J., and Govindan, R. 2010. Energy-efficient rate-adaptive gps-based positioning for smartphones. In Proceedings of the 8th Annual International Conference on Mobile Systems, Applications and Services (MobiSys). 299--314. Google ScholarDigital Library
- Paleologo, G. A., Benini, L., et al., A. B., and Micheli, G. D. 1998. Policy optimization for dynamic power management. In Proceedings of the 35th Annual Design Automation Conference (DAC). 182--187. Google ScholarDigital Library
- Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann. Google ScholarDigital Library
- Schilit, B., Adams, N., and Want, R. 1994. Context-aware computing applications. In Proceedings of the Workshop on Mobile Computing Systems and Applications. 85--90. Google ScholarDigital Library
- Simunic, T., Benini, L., Glynn, P., and Micheli, G. D. 2000. Dynamic power management for portable systems. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MobiCom). 11--19. Google ScholarDigital Library
- Steinberg, D. and Colla, P. 1997. Cart--classification and regression trees. In Salford Systems.Google Scholar
- Williamus, H. G. 2002. Conometric Analysis 5th Ed. Prentice Hall.Google Scholar
- Younis, O. and Fahmy, S. 2004. Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mobile Comput. 3, 366--379. Google ScholarDigital Library
- Zheng, Y. and Xie, X. 2011. Learning travel recommendations from user-generated gps traces. ACM Trans. Intell. Syst. Technol. 2, 2:1--2:29. Google ScholarDigital Library
- Zhuang, Z., Kim, K., and Singh, J. P. 2010. Improving energy efficiency of location sensing on smartphones. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys). 315--330. Google ScholarDigital Library
Index Terms
- Learning to Infer the Status of Heavy-Duty Sensors for Energy-Efficient Context-Sensing
Recommendations
Context-Aware Broadcast in Duty-Cycled Wireless Sensor Networks
As the energy efficiency remains a key issue in wireless sensor networks, duty-cycled mechanisms acquired much interest due to their ability to reduce energy consumption by allowing sensor nodes to switch to the sleeping state whenever possible. The ...
An energy efficient clustering method for wireless sensor networks
EHAC'07: Proceedings of the 6th WSEAS International Conference on Electronics, Hardware, Wireless and Optical CommunicationsWireless sensor networks have many sensor nodes with a limited energy in a limited area. One of key issues in wireless sensor networks is to prolong the network lifetime. In this paper, we propose a scheme to construct an energy-efficient cluster ...
Energy-efficient area coverage by sensors with two adjustable ranges
WiOPT'09: Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless NetworksIn wireless sensor networks, density control is an important technique for prolonging a network's lifetime. To reduce the overall energy consumption, it is desirable to minimize the overlapping sensing area of sensor nodes. In this paper, we study the ...
Comments