schemes.Chen et al.10 analyze the power consumption ACKNOWLEDGMENT of AMOLED displays in multimedia applications.Camera recoding incurs high power cost.LiKamWa R et al.[11]do re- This work is supported in part by National Natural Science search on the image sensor and reveal two energy-proportional Foundation of China under Grant Nos.61472185,61373129 mechanisms which are supported by current image sensor but 61321491,91218302;Key Project of Jiangsu Research Pro- unused in mobile system.It indeed saves energy.But it only gram under Grant No.BE2013116:EU FP7 IRSES Mobile- focuses on the energy consumption of the moment of shooting, Cloud Project under Grant No.612212.This work is par- while overlooking the consumption of preparation.Han et al. tially supported by Collaborative Innovation Center of Novel [12]study the energy cost made by human-screen interaction Software Technology and Industrialization.Lei Xie is the such as scrolling on screen.They propose a scrolling-speed- corresponding author. adaptive frame rate controlling system to save energy.Dietrich et al.[3]detect the game's current state and lower the proces- REFERENCES sor's voltage and frequency whenever possible to save energy. [1]"KS Mobile,"http://www.cmcm.com/zh-cn/. [2] "Monsoon PowerMonitor,"https://www.msoon.com/LabEquipment/ B.Activity Sensing PowerMonitor/. With the development of phone's built-in sensors,various [31 B.Dietrich and S.Chakraborty,"Power management using game state detection on android smartphones,"in Proc.of ACM MobiSys,2013. approaches of activity recognition have been done.They can be classified into single-sensor and multi-sensors sensing. [4]X.Fan,W.-D.Weber,and L.A.Barroso,"Power provisioning for a warehouse-sized computer,"in Proc.of ACM S/GARCH,2007 Single sensor is used in the following work.Built-in [5] F.Bellosa.A.Weissel.M.Waitz.and S.Kellner."Event-driven energy microphone is used to detect the events that are closely related accounting for dynamic thermal management,"in Proc.of CoLp,2003. to sleep quality,including body movement,couch and snore [6 D.Rajan,R.Zuck,and C.Poellabauer,"Workload-aware dual-speed [13].Using built-in accelerometers,user's daily activities such dynamic voltage scaling,"in Proc.of /EEE RTCSA.2006. as walking,jogging,upstairs,downstairs,sitting and standing [7] N.Balasubramanian,A.Balasubramanian,and A.Venkataramani, are recognized in [14].With the labeled accelerometer data, "Energy consumption in mobile phones:a measurement study and implications for network applications,"in Proc.of ACM S/GCOMM, they apply four machine learning algorithms and make some 2009 analysis.Lee et al.[15]use accelerometers with hierarchical [8]M.Dong and L.Zhong,"Self-constructive high-rate system energy hidden markov models to distinguish the daily actions. modeling for battery-powered mobile systems,"in Proc.of ACM MobiSys,2011. Multi-sensors are used in the following work.Shahzad et [9] F.Xu.Y.Liu,Q.Li.and Y.Zhang."V-edge:Fast self-constructive al.[16]propose a gesture based user authentication scheme power modeling of smartphones based on battery voltage dynamics."in for the secure unlocking of touch screen devices.It makes Proc.of NSDI,2013. use of the coordinates of each touch point on the screen, 10 X.Chen,Y.Chen,Z.Ma,and F.C.Fernandes,"How is energy accelerometer values and time stamps.Chen et al.[17]take consumed in smartphone display applications?in Proc.of ACM advantage of features as light,phone situation,stationary and HotMobile.2013. silence to monitor user's sleep.They need to use several [11] R.LiKamWa,B.Priyantha,M.Philipose,L.Zhong,and P.Bahl different sensors to obtain all the phone's information.Driving "Energy characterization and optimization of image sensing toward style,which is concerned with man's life,is recognized by continuous mobile vision,"in Proc.of ACM MobiSys,2013. [12] H.Han,J.Yu.H.Zhu,Y.Chen,J.Yang.G.Xue,Y.Zhu,and M.Li,"E using the gyroscope,accelerometer,magnetometer,GPS and 3:energy-efficient engine for frame rate adaptation on smartphones,"in video[18].Bo et al.[19]propose a framework to verify whether Proc.of ACM Sensys,2013. the current user is the legitimate owner of the smartphone [13] T.Hao,G.Xing,and G.Zhou,"isleep:unobtrusive sleep quality based on the behavioral biometrics,including touch behaviors monitoring using smartphones,"in Proc.of ACM Sensys,2013. and walking pattens.These features are extracted from smart- [14] J.R.Kwapisz,G.M.Weiss,and S.A.Moore,"Activity recognition phone's built-in accelerometer and gyroscope. using cell phone accelerometers,"SIGKDD,vol.12,no.2,pp.74-82. 2011. VI.CONCLUSION AND FUTURE WORK [15) Y.-S.Lee and S.-B.Cho."Activity recognition using hierarchical hidden markov models on a smartphone with 3d accelerometer."in Proc.of In this paper,we propose a context aware energy-saving Springer HAIS,2011. scheme for smart camera phone based on activity sensing. [16 M.Shahzad,A.X.Liu,and A.Samuel,"Secure unlocking of mobile We take advantage of the features of activities and maintain touch screen devices by simple gestures:You can see it but you can an activity state machine to do the recognition.Then energy not do it,"in Proc.of ACM MobiCom.2013. saving scheme is applied based on the result of recognition [17]Z.Chen,M.Lin,F.Chen,N.D.Lane,G.Cardone,R.Wang,T.Li Our solution can perceive the user's activities with an average Y.Chen,T.Choudhury,and A.T.Campbell,"Unobtrusive sleep monitoring using smartphones,"in Proc.of IEEE PervasiveHealth, accuracy of 95.5%and reduce the overall energy consumption 2013. by 46.5%for smart camera phones. [18]D.A.Johnson and M.M.Trivedi,"Driving style recognition using a Following the current research,there are three possible smartphone as a sensor platform,"in Proc.of IEEE ITSC,2011. directions for future work.First,more data of the process [19]C.Bo,L.Zhang.X.-Y.Li,Q.Huang,and Y.Wang."Silentsense:silent user identification via touch and movement behavioral biometrics."in can be collected with our work to improve the design and Proc.of ACM MobiCom,2013. implementation.Second,a self-constructive user preference learning can be designed to automatically extract the user perference of software settings.Third,to the phone whose configuration is too low,more simple strategy can be designed to avoid the possible delay for changing modes.schemes. Chen et al. [10] analyze the power consumption of AMOLED displays in multimedia applications. Camera recoding incurs high power cost. LiKamWa R et al. [11] do re￾search on the image sensor and reveal two energy-proportional mechanisms which are supported by current image sensor but unused in mobile system. It indeed saves energy. But it only focuses on the energy consumption of the moment of shooting, while overlooking the consumption of preparation. Han et al. [12] study the energy cost made by human-screen interaction such as scrolling on screen. They propose a scrolling-speed￾adaptive frame rate controlling system to save energy. Dietrich et al. [3] detect the game’s current state and lower the proces￾sor’s voltage and frequency whenever possible to save energy. B. Activity Sensing With the development of phone’s built-in sensors, various approaches of activity recognition have been done. They can be classified into single-sensor and multi-sensors sensing. Single sensor is used in the following work. Built-in microphone is used to detect the events that are closely related to sleep quality, including body movement, couch and snore [13]. Using built-in accelerometers, user’s daily activities such as walking, jogging, upstairs, downstairs, sitting and standing are recognized in [14]. With the labeled accelerometer data, they apply four machine learning algorithms and make some analysis. Lee et al. [15] use accelerometers with hierarchical hidden markov models to distinguish the daily actions. Multi-sensors are used in the following work. Shahzad et al. [16] propose a gesture based user authentication scheme for the secure unlocking of touch screen devices. It makes use of the coordinates of each touch point on the screen, accelerometer values and time stamps. Chen et al. [17] take advantage of features as light, phone situation, stationary and silence to monitor user’s sleep. They need to use several different sensors to obtain all the phone’s information. Driving style, which is concerned with man’s life, is recognized by using the gyroscope, accelerometer, magnetometer, GPS and video[18]. Bo et al. [19] propose a framework to verify whether the current user is the legitimate owner of the smartphone based on the behavioral biometrics, including touch behaviors and walking pattens. These features are extracted from smart￾phone’s built-in accelerometer and gyroscope. VI. CONCLUSION AND FUTURE WORK In this paper, we propose a context aware energy-saving scheme for smart camera phone based on activity sensing. We take advantage of the features of activities and maintain an activity state machine to do the recognition. Then energy saving scheme is applied based on the result of recognition. Our solution can perceive the user’s activities with an average accuracy of 95.5% and reduce the overall energy consumption by 46.5% for smart camera phones. Following the current research, there are three possible directions for future work. First, more data of the process can be collected with our work to improve the design and implementation. Second, a self-constructive user preference learning can be designed to automatically extract the user perference of software settings. Third,to the phone whose configuration is too low, more simple strategy can be designed to avoid the possible delay for changing modes. ACKNOWLEDGMENT This work is supported in part by National Natural Science Foundation of China under Grant Nos. 61472185,61373129, 61321491, 91218302; Key Project of Jiangsu Research Pro￾gram under Grant No. BE2013116; EU FP7 IRSES Mobile￾Cloud Project under Grant No. 612212. This work is par￾tially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. Lei Xie is the corresponding author. REFERENCES [1] “KS Mobile,” http://www.cmcm.com/zh-cn/. [2] “Monsoon PowerMonitor,” https://www.msoon.com/LabEquipment/ PowerMonitor/. [3] B. Dietrich and S. 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Zhang, “V-edge: Fast self-constructive power modeling of smartphones based on battery voltage dynamics.” in Proc. of NSDI, 2013. [10] X. Chen, Y. Chen, Z. Ma, and F. C. Fernandes, “How is energy consumed in smartphone display applications?” in Proc. of ACM HotMobile, 2013. [11] R. LiKamWa, B. Priyantha, M. Philipose, L. Zhong, and P. Bahl, “Energy characterization and optimization of image sensing toward continuous mobile vision,” in Proc. of ACM MobiSys, 2013. [12] H. Han, J. Yu, H. Zhu, Y. Chen, J. Yang, G. Xue, Y. Zhu, and M. Li, “E 3: energy-efficient engine for frame rate adaptation on smartphones,” in Proc. of ACM Sensys, 2013. [13] T. Hao, G. Xing, and G. Zhou, “isleep: unobtrusive sleep quality monitoring using smartphones,” in Proc. of ACM Sensys, 2013. [14] J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” SIGKDD, vol. 12, no. 2, pp. 74–82, 2011. [15] Y.-S. Lee and S.-B. Cho, “Activity recognition using hierarchical hidden markov models on a smartphone with 3d accelerometer,” in Proc. of Springer HAIS, 2011. [16] M. Shahzad, A. X. Liu, and A. Samuel, “Secure unlocking of mobile touch screen devices by simple gestures: You can see it but you can not do it,” in Proc.of ACM MobiCom, 2013. [17] Z. Chen, M. Lin, F. Chen, N. D. Lane, G. Cardone, R. Wang, T. Li, Y. Chen, T. Choudhury, and A. T. Campbell, “Unobtrusive sleep monitoring using smartphones,” in Proc. of IEEE PervasiveHealth, 2013. [18] D. A. Johnson and M. M. Trivedi, “Driving style recognition using a smartphone as a sensor platform,” in Proc. of IEEE ITSC, 2011. [19] C. Bo, L. Zhang, X.-Y. Li, Q. Huang, and Y. Wang, “Silentsense: silent user identification via touch and movement behavioral biometrics,” in Proc. of ACM MobiCom, 2013