2012 2nd Baltic Congress on Future Internet Communications
Understanding Mobile Phone Battery - Human Interaction for Developing World A Perspective of Feature Phone Users in Africa Amandeep Dhir1, Puneet Kaur2,Nobert Jere3, Ibrahim A Albidewi4
1
Department of Computer Science and Engineering, School of Science, Aalto University, Finland Department of Information and Service Economy, School of Economics, Aalto University, Finland 3 Telkom Center of Excellence in ICTD, Computer Science Department, University of Fort Hare, South Africa 2
4
Information Systems Department, King Abdulaziz University, Saudi Arabia {
[email protected]}1,2
scientific research related to mobile phone battery have been targeted towards smartphone users while feature phone users have been neglected in the research space even through Global mobile statistics [3] states that feature phone sales still outnumber smartphones by 4:1 in 2011.
Abstract— Mobile phone battery is well recognized as an open usability concern for both device manufacturers and end users. Due to the ubiquitous nature of mobile phones, battery related problems have received increasing attention in the scientific conferences. In our paper, we have presented the need for understanding and investigating Human Battery Interaction for Development (HBI4D). In our study we are mainly interested in the non-smartphone or feature phone users who belong from low income; low literacy and resource limited areas. This would enable us in understanding the existing design opportunities available for developing and designing energy efficient mobile battery solutions for emerging markets. Our work is first of its kind as almost all the existing work on battery efficiency is targeted towards smartphone users from western world. However, our work mainly comprises of users from emerging markets such as India, China and South Africa.
The emergence of mobile computing had lead to the advancement of the feature phones, which are also referred as “plain phones”. Due to this advancement, it is not difficult to distinguish between smartphones and feature phones. In order to solve this confusion over the basic definition for any “feature phone” we have decided our own definition for “feature phone” that will be used throughout in this paper. Feature phone can less powerful phone that has only one main feature like web, camera, GPS, 3G, GPRS connectivity apart from voice calling and text messaging. Feature phone comes with a black and white screen or colored display. They are cheap in price and mostly owned by people with low to middle class socioeconomic conditions.
Keywords - Human-Battery interaction, ethnography, energy efficiency, smartphone
I.
INTRODUCTION
Mobile phones are increasingly becoming entrance to one’s life. According to wireless intelligence estimates, world’s total mobile ownership will cross 6 billion in number [1]. Mobile phones now come equipped with several useful sensors such as GPS, camera, microphone, accelerometer and magnetometer. However, due to limited battery capabilities, possessing capacity and sensors often get congested. Mobile phones have opened plethora of opportunities for common people by expanding their businesses and opening new markets for their products and services especially in the emerging markets such as India, China and South Africa. However, mobile phones bear an essential need and burden called battery, which if gets depleted than can render all essential operations such as voice calling and text messaging on mobile phones. In general, mobile phone users are overly dependent on phone’s charging cord and battery and this has significantly affected the usability of mobile devices [2]. This also affects when, how and where mobile devices are used. Therefore, understanding battery consumption characteristics in mobile phone is pressing need at present. Identifying the patterns of energy consumption in mobile phones and behavior patterns related to battery charging can potentially help in the design and implementation of the energy efficient solutions for energy intensive mobile devices. So far majority of the
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Ubiquitous Computing (aka UbiComp) community has been increasingly focusing on developing those technological solutions that can serve common users. However, these technological solutions suffer from a tradeoff between sustainability and usability. It means that users show their keen interest in endorsing sustainable technological solutions however from user point of view it should not be achieved at the cost of usability or convenience [4]. Mobile phone battery is one such example of mobile computing research that is also affected by UbiComp research. Larger focus is given on increasing the battery lifetime without tackling issues related to energy efficiency, which should take human behavior and choices into consideration. Social scientists believe that if human behavior is studied, analyzed and technical solutions are modeled based on their findings then friendly technology can be devised. For example, we are of the opinion that if user behavior related to mobile phone battery use and consumption is studied then energy wastage due to overcharging and unnecessary use of battery under nonurgent situations can be saved.
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According to a recent estimate, about £40 million British pounds worth of energy is wasted every year in UK due to overcharging of mobile phones. This overcharging account for the generation of 85,000 tones of CO2, produced due to nightlong overcharging sessions. Furthermore, on an average, most people in UK leave their mobile phones for well over eight hours and mainly during overnight [5]. Similarly one other estimate shows that energy equivalent to approximately 99 million barrels of oil are wasted every year in US due to standby overcharging wastages [6]. This clearly shows that mobile phone users do not fully understand and appreciate their actual need from mobile battery charging i.e. for how long they should charge their mobile phones in order to carry out their planned use of the phones.
Human Computer Interaction for Development (HCI4D) literature [8]. In our study, we have hypothesized that by performing a user study involving a combination of different research methods on HBI4D in emerging markets, we can estimate to some extent, the real design opportunities for reducing energy consumption thus increasing battery life time, better prediction of battery life time and when energy intensive computational operations could be scheduled. The rest of the paper is organized as follows. In Section II, the existing work pertaining to HBI is presented. Section III, examines research methods to be used in HBI4D. Section IV presents our research study and Section V discusses some of the preliminary study results. II.
The above statistics clearly reflects the alarming situation caused due to overcharging for mobile phones. We did not find any documented study, which has made cost related estimates to energy loss due to overcharging in any of the developing countries. So we assume that this problem is also present in rural regions of emerging markets as this kind of user group is less enlightened compared to mobile users in developed or industrial economies. This will be an open research question for our research too however we do not aim to estimating the exact numbers rather we will emphasize on assessing the gravity of energy losses due to overcharging in rural regions. There could be a counter argument that users are same everywhere, i.e., developed and developing world so what is the difference between the wastage of energy caused by either of them? Our opinion on this matter is that energy wastage due to poor charging habits and overcharging is much worse in developing countries due to large number of mobile phone users. Therefore, a new research agenda should be framed around this issue, which will specifically focus on the mobile phone battery in development world.
EXISTING WORK ON HUMAN BATTERY INTERACTION
Mobile phone battery is recognized as a major usability concern for both device manufactures and mobile phone users [2,7]. Mobile phone battery and its relation with users have been less discussed as main focus has been given on understanding user-charging behavior in order to perform opportunistic scheduling of the mobile phone updates. Due to the emergence of the mobile application distribution channels such as Apple app store, Android app market and Nokia ovi Store, application developers are able to exploit the diversified mobile phone’s inbuilt sensors such as GPS, Bluetooth, camera and accelerometer. However, application developers possess limited experience in developing energy efficiency mobile applications. Due to this lack of this knowledge, already energy-constrained mobile phones are forced to host energy hungry applications that drastically affect the battery management and device performance [4]. The ultimate suffers of this benightedness are the mobile phone users who often struggle to determine as which applications are energy efficient. Furthermore, it is often seen that when mobile battery gets depleted due to such energy incompetent applications then users often accuse either device manufactures i.e. device brands or inbuilt software design [9].
For better understanding the relationship between mobile phone users and battery, the term called Human-Battery Interaction (HBI) first came into picture in 2007 when Rahmati et al. [7] stated that HBI is equivalent to a process when a mobile phone user deals with limited battery lifetime in order to manage the available battery. However, in this paper our focus is targeted towards a special class of users’ i.e. feature phone rural users in low literacy, low income and resource limited areas of the emerging markets. We do not agree with the term “HBI” as it is misleading because only word “battery” has several different meanings. Battery refers to “testing” in psychology and “act of offense” in the dictionary of law. However, mobile phone battery refers to the cell-battery used in the mobile phones. We aim to study the HBI perspective as coined by Rahmati et al. [7] but from the development world’s perspective. Thus we coin a term called Mobile Phone Battery - Human Interaction for Development (MBHI4D) as our focus is influenced from
Banerjee et al. [2] has claimed to perform the first user study on battery use and charging behavior involving both laptop and mobile users. They performed trace collection through automatic logging tools, user interviews for understanding battery management and in-situ survey for understanding issues related to phone charging in real context. They found that majority of recharges happen when the battery has considerable amount of energy left or battery levels are high at that moment, good number of charging events are driven by context i.e. location and time. Finally, there exists great variation among users and systems i.e. mobile phones and laptop computers. Their findings suggest that good design opportunities exist for enhancing existing battery management policies, which are currently based on increasing battery lifetime. Due to
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increasing focus on increasing battery lifetime, majority of users overcharge their mobile phones that lead to wastage of energy.
mobile users, are limited in their findings. Our argument is that mobile usage and charging related behavior changes from one user group to another. For example, working users might have more battery needs compared to students who are known for their economical approach towards service usage such as phone calling, text messaging and accessing multimedia.
Truong et al. [10] emphasized that although it is obvious that mobile phone battery imposes restrictions on its users but still existing battery interfaces are limited in the required battery status information and they only present high-level feedback on the remaining battery life. However, user requires accurate and detailed information for making decision related to mobile phone usage. Due to the lack of this required information, mobile users often develop incorrect mental models about percentage of battery left, when will battery discharge and mapping between a particular application usage and percentage battery shown on the battery interface. They conducted questionnaire based survey comprising of 104 mobile users while smartphone users account for 95% of the total respondents. They studied how mobile users interact with different battery interfaces and it is important that user must be presented with an accurate and easily understandable interface. Based on their findings, task-centered battery interface was developed on a mobile device that shows more accurate information on how long individual and combination of tasks with several applications can be hosted.
Ferreira et al. [4] conducted 4 weeks of user study through trace collection for assessing mobile phone related charging behavior and habits. They argue that understanding the charging habits is crucial for tackling the issues of energy wastage and opportunistic processing on mobile phones. Their study concludes with important insights on how users charge their phones, how to enhance the user experience with battery lifetime and how their findings can improve the battery management in future smartphones. Oliver [13] presents preliminary results from first of its kind, a large-scale smartphone user study involving 17,300 blackberry users. Their study examines how mobile users interact with their devices that in a way consume energy. They have put forward open research questions for other researcher and practitioners interested in user behavior modeling through smartphone based logging and trace collection. Oliver [14] termed three classes of mobile phone users based on their charging patterns determined in their previous study [13] i.e. opportunistic chargers, light consumers and night time chargers. Opportunistic chargers account for 63% of the total population and they are characterized through frequent and short duration opportunistic charging events between 8am to 5 pm. They are also known as aggressive energy consumers as they account for nearly 4.8% of device’s energy per hour. Light consumers charge and discharge their mobile phones for longer durations. They account for 20% of the total mobile phone users and possess lowest discharge rate of 20%. Last category is of nighttime chargers that account for 17% of total mobile users. Their mobile phones host nightlong charging sessions that maintain their battery level at 72.5%. Oliver et al. [15] in another study implemented a toolkit named Energy Emulation Toolkit (EET) based on their earlier large-scale user study [13]. EET enables the application developers to evaluate the energy consumption requirements of their applications against the collected user handset traces. Their study claims that through active adaption to energy constraints and classifying users based on their charging behavior, energy level can be predicted with 72% accuracy a full day in advance.
Rahmati et al. [7] also claim to be the first one to perform systematic study of human-battery interaction involving mobile phone users through a combination of qualitative and quantitative data collection methods. They performed user studies involving similar research methods to Banerjee et al. [2]. However, their questionnaire survey is known to be the first and only extensive international survey on HBI involving 350 student mobile users from India, China and United States till date [11]. They emphasized that studying HBI can provide crucial insights into the user habits dealing with limited battery life, effectiveness of the battery interfaces and user behavior related to managing tradeoff between battery life and device performance. Their study suggests that 80% of mobile users take measures to increase the battery life, existing battery interfaces are under developed so they impose both cognitive and technology challenging mental loads. This results in underutilized battery saving settings that sum into a bad user experience. Heikkinen et al. [12] examined consumer attitude towards battery consumption through a questionnaire survey based study involving 150 student participants. The study investigates the needs and expectations of the mobile users from battery, affect of battery depletion on phone usage, application selection and user behavior. Mostly smartphone users also overly dominate this study while basic mobile phones are underrepresented. Furthermore like other questionnaire survey based user studies [2,7], this study also includes “students” as their user group. However, we argue that the existing studies, which include only “student”
Predicting mobile phone's battery lifetime is essential for minimizing the excess battery consumption at the application level [16]. The existing work on predicting battery life involves battery lifetime prediction through model-based approach [17], estimations at operating system (OS) level [18] and measuring battery consumption by a CPU [19]. All these above approaches deals with finding
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static battery consumption rate i.e. determined by battery manufacturers for battery lifetime prediction, thus these approaches do not take user behavior into account. Kang et al. [16] proposed a method different from above approaches for predicting the battery life on mobile phone based on the usage pattern so it is more useful for users.
cannot read or write any language. Therefore, questionnaires based studies are not feasible in the developing communities comprising of illiterate users. C. Trace Collection through Contextial Logging Contextual logging refers to software that automatically captures users’ interaction with mobile phone for later analysis. These interaction traces collected in form a log files provide researchers with the opportunity to understand rich access patterns continuously regardless of the user activity and its location due to its un-intrusive nature [4]. However, logging software might have prominent impact on the overall energy consumption of energy hungry embedded devices i.e. mobile phones. As mobile users develop cognitive models on devices’ normal battery life so if there is any noticeable decrease in battery life then it is likely that user can account logging application for this change [13]. We argue that although user logs can generate data both in scale and breadth, and provide deep information on the service usage [22] however, they cannot answer several crucial questions related to behavioral changes such as: what incidents have triggered charging events, why users prefer to put night long charging sessions, what was the context of mobile-user interaction and so on. For example, if due to low battery feedback, user gets irritated and frustrated and due to which user switch off her mobile phone. In this case, usage logs cannot determine the reason behind change in behavior. We believe that additional methodologies are required for determining the reasons behind such human behavioral changes. Our suggestion for mobile manufacturing companies and researchers who are interested in better understanding HBI issues from emerging markets’ mobile phone users perspective should consider research methods other than logging and trace collection due to following reasons: First, majority of users in emerging markets do not possess smartphones and context logging is not possible due to vast diversity of mobile phones. Second, contextual logging might become unpredictable at places having fluctuating network connectivity.
All the user studies involving logging and trace collection are targeted towards specific smartphone platforms. Falaki et al. [20, 21] studied Windows and Android users; Oliver [13] performed trace collection involving Blackberry users and Ferreira et al. [4] emphasized on Android platform. Oliver [13] argued that although majority of the existing trace collection user studies are focused on a particular smartphone platforms but still the challenges and also even findings (to some extent) are isomorphic in nature. The possible reasons behind targeting only smartphone users are: implementation of logging software is easier for specific platforms and due to diversity in basic phones; it becomes difficult to develop logging tools for different mobile phone types. Almost all the human-battery user studies are smartphone based and involve mostly mobile users from developed regions. To best of our knowledge, no prior wok has examined the HBI issues involving feature phone owning rural users from emerging markets. III.
RESEARCH METHODS
For understanding the state-of-the-art Mobile Phone Battery - Human Interaction for Development (MBHI4D) on mobile phones, we have discussed below some of the research methods that can help in better understanding the issues involved in MBHI4D. We have analyzed each of them by explaining their advantages and potential risk involved in practicing them in user studies from emerging market point of view. This kind of discussion will make HCI researchers and practitioners aware of methodological considerations involved in the domain of HBI in general. A. User Interviews In the past structured interviews were practiced as research tools for understanding user interaction with the battery lifetime and mobile phone’s battery interface [2,7,10]. User interviews can take place either individually or in groups. We argue that both individual and focus group interviewing are appropriate for performing ethnographic research on HBI in emerging markets despite the fact that comprised user population possess low literacy. Focus groups help in gathering user data easily as users are able to provide feedback by building on each other’s ideas.
D. Contextual Inquiry and Observations The contextual inquiry involves observing the users in their natural environment while they are performing the required tasks. This can be difficult in the case of observing HBI related issues since it would require observing the users twenty four hours for few days. This might be difficult in the case of developing communities experiencing high crime rates creating the risks of physical threats to the researchers conducting the studies [23,24]. Therefore, practicing contextual inquiry in rural communities is difficult in case of research involving long observation sessions. However, if the researchers are native of the place of study then such concerns can be avoided for example, in our case, MBHI4D study is organized by one of the author of this paper who stays in Alice rural community.
B. Questionnaire Survey Questionnaire based studies aim at gathering quantitative data enabling users to provide opinions without having any influence from the researcher. Questionnaire based studies have been used to investigate HBI issues involving mostly literate users [2,7,12]. However, practicing this methodology is not possible for low literacy users who
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IV.
RESEARCH PROJECT
other and provide rich insight details that would not be possible from one method alone [25]. In the focus discussions, we investigated issues related to problems faced in battery charging, battery life prediction, reaction and concerns of running out of battery, expectations from battery life, and strategies for increasing battery life and charging behavior in general. Figure 1 and 2 presents that pictures captured by the researchers during the field visits.
In our research project, we hypothesize that through understanding the charging related behavior and habits of feature phone users in emerging markets; we can access some of the crucial information details such as charging related activities in rural context, extent to which energy is wasted due to incompetent battery charging habits possessed by people in third world and when, how and where usually charging takes place. We further hypothesize that our study will enable us in identifying design opportunities for promoting energy efficiency in emerging countries. A. Research Questions 1. What are the needs of mobile users from battery lifetime? How to access and control the energy wastage due to incompetent battery charging behavior possessed by mobile users in emerging markets? 2. What are the design guidelines for device manufacturing companies, application developers and researchers who are interested in creating energy efficient products for emerging markets? B. Participant and Sites Our study participant includes all kinds of stakeholders that represent broad category of mobile phone users in low income, low literacy and resource constraint regions of developing markets. We are focusing on youth, working population, old people, farmers and women (mainly staying at home). Table 1 presents the classification of the three user groups that were available for the pilot study that has been reported in this paper. We interviewed 6 old users, 10 women staying at home and 11 working population users in Alice community. At the time of paper submission, we were conducting study in two rural communities of South Africa i.e., Alice and Dwesa (Eastern Cape, South Africa. However, the study results are reported based on the focus group discussions held at Alice community in South Africa.
Figure 1. Working users showing their feature phones
Target Population
Total Mean Standard Number Deviation of Users Old People 6 60.5 4.76 Women at Group 1 5 33 6.51 Home Group 2 5 29.4 3.20 Working Group 1 7 45.4 11.14 Population Group 2 4 37.7 7.80 Table 1: Classification of the study participants in Alice
Figure 2. Young children playing in fields with their feature phones
V.
PRELIMINARY RESULTS
The preliminary results are based on the five focus discussions and observation exercises organized at Alice. Figure 3, 4 and 5 presents discussion sessions organized with our subjects. A total of 27 mobile users (6 male, 21 female) participated in focus group discussions. First two focus discussions consists of 10 women mainly staying at home and not working, five in each group having mean age as 31.2 years and SD = 5.2, all of them possess feature phones and educated below the high school level. Third and fourth focus group consists of 10 working people who were
C. Study Process Our study process involves wide set of research methods that includes but not limited to the following: contextual inquiry and field observations, focus group and personal interviews and in situ field trials. In this study, we aim to follow triangulation research principle that includes different research methods combined together. In triangulation, different research methods complement each
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interviewed in a group of 7 and 4 users (5 male, 6 female). Their mean age was 42.6 years and SD = 10.3, only six were having feature phones while all were having at least a diploma so they did represent low literacy category. During the focus group discussions, we emphasized on having feature phone users from low literacy, low income and resource limited areas however while gathering mobile users for having focus discussions, some enthusiastic smartphone users also participated who all were mainly working people. It would be impolite if we have asked smartphone users not to participate in our study, as it is also consider unsocial in Alice rural community. Last focus group consists of 6 old people (1 male, 5 female) having mean age 60.5 years and SD = 4.7. They all were feature phone users and all were educated below a high school.
the users do not face problem in charging except the having problems in accessing the chargers. In Alice, Nokia phones are widely used so mobile phone users having non-Nokia phones face problems due to unique charger pins. People with Samsung and other mobile phone brands often face problem in charging phones if they did not carried their own phone charger. Women staying at home always carry phone chargers with them when they go out of their home to avoid any unexpected battery shortages. However, women charge their mobile phones only when the battery is too low in order to save electricity cost involved in repeated charging events. Old people are less concerned about the mobile phone battery as they are mainly at their homes so they can charge their phone anytime. Old people have scheduled phone calls by their children working in cities so they have established mental models on phone charging i.e. which day and what time they have to plug charging.
Figure 3. Researcher interviewing users in a local school
Figure 5. Study Participants gathered in a local school
B. Charging Behavior Majority of the working people charge their phone even if it is full due to several reasons such as electricity is free at their work places so they are not concerned about the electricity bill. They do receive lot of phone calls so they always want to keep their mobile phone battery full. However, women staying at home and old people charge their phone only when it has low battery due to following reasons – both old and women are concerned that overcharging can destroy their battery. As both these user group do not earn and they own old mobile phones given to them either by their children or spouse so take extra care of their phones. Second, women mobile users are miser and do not want to waste electricity cost in repeated charging. Figure 4. A working women interviewed by a researcher
C. Battery Life Prediction Majority of the mobile users mentioned that they cannot make battery life prediction and their phone battery is unpredictable. However, few claimed to make predictions based on their prior experience of using mobile phones. Battery prediction also depends upon the mobile phone as old phones are unpredictable compared to new phones so majority of the low educated and feature phone users make
A. Problems faced in Charging We found that South Africa has uninterrupted power supply and people are informed in advance in case of any power cut so mobile phone users’ situation is different from that of India where unexpected power cuts do happen and mobile users often seen concerned about phone charging due to unpredictable nature of power supply. In Alice, majority of
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estimation based on battery icon. Half full battery icon means battery is in danger.
ACKNOWLEDGMENT First of all, we thank all the researchers from University of Fort Hare who helped us in the data collection and making practical arrangement in Alice community for our study. We are grateful to the anonymous reviewers from the 2nd Baltic conference on future Internet communications who gave us insightful comments on our paper. We are thankful to all the participants from Alice community who have found time for our study and participated with their full spirit. Additionally we also like to thank Sari Kujala from Aalto School of Arts and Design who gave us insightful feedback on questionnaire and entire study tool.
D. Running out of Battery Almost all user groups except old people feel very much boring and frustration when they run out of battery and need to recharge their mobile phone batteries. Furthermore, mobile phones do not inform me well in advance that it is going to die. We found that mobile users having unpredictable battery always put their batteries on charging because their battery dies after some minutes of using mobile phone. Users always have a worry that if their phone is off then they will lose business and loose some important calls.
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E. Strategy for Increasing Battery Majority of the users are not able to make their battery last longer. They can only think of charging their phones when it running out of battery. However, working population that represents smartphone mobile users mentioned that they are not sure if their mobile phones provide any such kind of support. VI.
LIMITATIONS
In this paper, we have only presented some of our preliminary results that are based on the first pilot study conducted in Alice rural communities of South Africa. The results presented in this paper are not generalizable and complete by any means. We have put forward an important research agenda, which in a way supports the mission led by sustainability, and energy efficiency related research. Our present study is totally based on the pure qualitative insights without any trace of quantitative facts. However, despite all these weaknesses, we are certain that our research will draw the attention of the research community towards the deprived sections of our human society that feature phone users in the development world. VII. CONCLUSION AND FUTURE WORK Our paper has introduced the concept of Mobile Phone Battery - Human Interaction for Development (MBHI4D). It correctly highlights the problems related to mobile phone battery faced by middle to low socio-economic class of users living in development world. The specific problem address by this paper concerns the characterization of the interaction between user and his mobile device. Specifically, we tried to dwell on understanding how, where and when a user uses his mobile device and how, where and when charging. The research is intended to provide guidelines for the development of mobile energy efficient applications. In future, we have plans to organize empirical studies on HBI using survey questionnaire, interviews and ethnography studies in different emerging markets such as India, South Africa and Middle East.
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