2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems A Context Aware Energy-Saving Scheme for Smart Camera Phones based on Activity Sensing Yuanyuan Fan,Lei Xie,Yafeng Yin,Sanglu Lu State Key Laboratory for Novel Software Technology,Nanjing University,P.R.China Email:fyymonica@dislab.nju.edu.cn,Ixie@nju.edu.cn,yyf@dislab.nju.edu.cn,sanglu@nju.edu.cn Abstract-Nowadays more and more users tend to take photos time of smart camera phones.However,the previous work in with their smart phones.However,energy-saving continues to be energy-saving schemes for smart phones have the following a thorny problem for smart camera phones,since smart phone limitations:First,they mainly reduce the energy consumption photographing is a very power hungry function.In this paper, in a fairly isolated approach,without sufficiently considering we propose a context aware energy-saving scheme for smart the user's actual behaviors from the application perspective, camera phones,by accurately sensing the user's activities in the photographing process.Our solution is based on the observation this may greatly impact the user's experience of the smart that during the process of photographing,most of the energy are phones.Second,in regard to the energy-saving scheme for wasted in the preparations before the shooting.By leveraging photographing,they mainly focus on the shooting process the embedded sensors like the accelerometer and gyroscope,our instead of the preparations before shooting. solution is able to extract representative features to perceive the user's current activities including body movement,arm movement In this paper,we propose a context aware energy-saving and wrist movement.Furthermore,by maintaining an activity scheme for smart camera phones,by accurately sensing the state machine,our solution can accurately determine the user's user's activities in the photographing process.Our idea is current activity states and make the corresponding energy saving that,since current smart phones are mostly equipped with strategies.Experiment results show that,our solution is able to tiny sensors such as the accelerometer and gyroscope,we can perceive the user's activities with an average accuracy of 95.5% leverage these tiny sensors to effectively perceive the user's and reduce the overall energy consumption by 46.5%for smart activities,such that the corresponding energy-saving strategies camera phones compared to that without energy-saving scheme. can be applied according to the user's activities. There are several challenges in building an activity sensing- I.INTRODUCTION based scheme for smart phones.The first challenge is to ef- Nowadays smart phones have been widely used in our fectively classify the user's activities during the photographing daily lives.These devices are usually equipped with sensors process,which contains various levels of activities of bodies. such as the camera,accelerometer,and gyroscope.Due to arms and wrists.To address this challenge,we propose a the portability of smart phones,more and more people tend three-tier architecture for activity sensing,including the body to take photos with their smart phones.However,energy- movement,arm movement and wrist movement.Furthermore. saving continues to be a upsetting problem for smart camera by maintaining an activity state machine,we can accurately phones,since smart phone photographing is a very power determine the user's current activity states and make the hungry function.For example,according to KS Mobile's [1] corresponding energy saving strategies.The second challenge report in 2014,the application Camera 360 Ultimate is listed is to make an appropriate trade-off between the accuracy of in the first place of top 10 battery draining applications for activity sensing and energy consumption.In order to accurately Android.Therefore,the huge energy consumption becomes a perceive the user's activities with the embedded sensors,more non-negligible pain point for the users of smart camera phones. types of sensor data and higher sampling rates are required. However,this further causes more energy consumption.To Nevertheless,during the process of photographing,we address this challenge,our solution only leverages those observe that a fairly large proportion of the energy is wasted low power sensors,such as accelerometer and gyroscope,to in the preparations before shooting.For example,the user first classify the activities by extracting representative features to turns on the camera in the smart phone,then the user may distinguish the user's activities respectively.We further choose move and adjust the camera phone time and again,so as to sampling rates according to the user's current activities.In this find a view,finally,the user focuses on the object and presses way,we can sufficiently reduce the energy consumption of the button to shoot.A lot of energy is wasted in the process activity sensing so as to achieve the overall energy efficiency. between two consecutive shootings,since the camera phone uses the same settings like the frame rate during the whole We make the following contributions in three folds:First, process,and these settings requires basically equal power we propose a context aware energy-saving scheme for smart consumption in the camera phone.Besides.it is also not wise camera phones,by leveraging the embedded sensor to conduct to frequently turn on/off the camera function,since it is not activity sensing.Based on the activity sensing results,we can only very annoying but also not energy efficient.Therefore, make the corresponding energy saving strategies.Second,we it is essential to reduce the unnecessary energy consumption build a three-tier architecture for activity sensing,including during the photographing process to greatly extend the life the body movement,arm movement and wrist movement.We use low-power sensors like the accelerometer and gyroscope Corresponding Author:Dr.Lei Xie.Ixie@nju.edu.cn to extract representative features to distinguish the user's IEEE 978-1-4673-9101-6W15$31.0002015IEEE 64 Φcomputer DOI10.1109/MASS.2015.17 societyA Context Aware Energy-Saving Scheme for Smart Camera Phones based on Activity Sensing Yuanyuan Fan, Lei Xie, Yafeng Yin, Sanglu Lu State Key Laboratory for Novel Software Technology, Nanjing University, P.R. China Email: fyymonica@dislab.nju.edu.cn, lxie@nju.edu.cn, yyf@dislab.nju.edu.cn, sanglu@nju.edu.cn Abstract—Nowadays more and more users tend to take photos with their smart phones. However, energy-saving continues to be a thorny problem for smart camera phones, since smart phone photographing is a very power hungry function. In this paper, we propose a context aware energy-saving scheme for smart camera phones, by accurately sensing the user’s activities in the photographing process. Our solution is based on the observation that during the process of photographing, most of the energy are wasted in the preparations before the shooting. By leveraging the embedded sensors like the accelerometer and gyroscope, our solution is able to extract representative features to perceive the user’s current activities including body movement, arm movement and wrist movement. Furthermore, by maintaining an activity state machine, our solution can accurately determine the user’s current activity states and make the corresponding energy saving strategies. Experiment results show that, our solution is able to 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 compared to that without energy-saving scheme. I. INTRODUCTION Nowadays smart phones have been widely used in our daily lives. These devices are usually equipped with sensors such as the camera, accelerometer, and gyroscope. Due to the portability of smart phones, more and more people tend to take photos with their smart phones. However, energy￾saving continues to be a upsetting problem for smart camera phones, since smart phone photographing is a very power hungry function. For example, according to KS Mobile’s [1] report in 2014, the application Camera 360 Ultimate is listed in the first place of top 10 battery draining applications for Android. Therefore, the huge energy consumption becomes a non-negligible pain point for the users of smart camera phones. Nevertheless, during the process of photographing, we observe that a fairly large proportion of the energy is wasted in the preparations before shooting. For example, the user first turns on the camera in the smart phone, then the user may move and adjust the camera phone time and again, so as to find a view, finally, the user focuses on the object and presses the button to shoot. A lot of energy is wasted in the process between two consecutive shootings, since the camera phone uses the same settings like the frame rate during the whole process, and these settings requires basically equal power consumption in the camera phone. Besides, it is also not wise to frequently turn on/off the camera function, since it is not only very annoying but also not energy efficient. Therefore, it is essential to reduce the unnecessary energy consumption during the photographing process to greatly extend the life Corresponding Author: Dr. Lei Xie, lxie@nju.edu.cn time of smart camera phones. However, the previous work in energy-saving schemes for smart phones have the following limitations: First, they mainly reduce the energy consumption in a fairly isolated approach, without sufficiently considering the user’s actual behaviors from the application perspective, this may greatly impact the user’s experience of the smart phones. Second, in regard to the energy-saving scheme for photographing, they mainly focus on the shooting process instead of the preparations before shooting. In this paper, we propose a context aware energy-saving scheme for smart camera phones, by accurately sensing the user’s activities in the photographing process. Our idea is that, since current smart phones are mostly equipped with tiny sensors such as the accelerometer and gyroscope, we can leverage these tiny sensors to effectively perceive the user’s activities, such that the corresponding energy-saving strategies can be applied according to the user’s activities. There are several challenges in building an activity sensing￾based scheme for smart phones. The first challenge is to ef￾fectively classify the user’s activities during the photographing process, which contains various levels of activities of bodies, arms and wrists. To address this challenge, we propose a three-tier architecture for activity sensing, including the body movement, arm movement and wrist movement. Furthermore, by maintaining an activity state machine, we can accurately determine the user’s current activity states and make the corresponding energy saving strategies. The second challenge is to make an appropriate trade-off between the accuracy of activity sensing and energy consumption. In order to accurately perceive the user’s activities with the embedded sensors, more types of sensor data and higher sampling rates are required. However, this further causes more energy consumption. To address this challenge, our solution only leverages those low power sensors, such as accelerometer and gyroscope, to classify the activities by extracting representative features to distinguish the user’s activities respectively. We further choose sampling rates according to the user’s current activities. In this way, we can sufficiently reduce the energy consumption of activity sensing so as to achieve the overall energy efficiency. We make the following contributions in three folds: First, we propose a context aware energy-saving scheme for smart camera phones, by leveraging the embedded sensor to conduct activity sensing. Based on the activity sensing results, we can make the corresponding energy saving strategies. Second, we build a three-tier architecture for activity sensing, including the body movement, arm movement and wrist movement. We use low-power sensors like the accelerometer and gyroscope to extract representative features to distinguish the user’s 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems 978-1-4673-9101-6/15 $31.00 © 2015 IEEE DOI 10.1109/MASS.2015.17 64