Guidance-based Power Conservation Framework for User-interface Developers on Mobile Devices

Mobile devices and specific applications for sport professionals have become nowadays very useful in specific training, but their potential is not fully exploited. The general goal of this paper is to point out bridges between the two communities of sport professionals and mobile applications developers. In this paper we survey these mobile devices according to these two different viewpoints. We first take into account their utility, the sports involved, and the specific training processes and present the available offer of mobile devices and applications. Main producers and tools are mentioned as well. The sport profesional viewpoint shows the way they are percieving the mobile applications and devices in the training process, and what are their expectations. Then we consider the technological aspects of the mobile devices and their potential for sport professionals. The technological aspects are treated from the developer's point of view. We present what the nowadays mobile devices are able to offer for the benefit of the sport, due to their design and embedded technology, tools and capabilities. Different operating systems, main producers of mobile devices and developers of mobile applications are briefly mentioned. A short classification of the applications for mobile devices is given, and the levels of the corresponding software developping skills are also mentioned. This suggest an ideal composition of a mobile application developing team. Our paper is an invitation for reflexion on the modern approaches of professional sport training through the nowadays mobile technologies. The invitation is addressed both to sport professionals and mobile devices developers. We conclude that the mobile devices and applications have a huge potential for sport professionals and, at the same time, the sport training offers a large domain of applications to the mobile devices developers.

Currently, people spend a lot of time using their mobile devices. With such ubiquity of mobile devices in our daily life, battery capacity and quality are of utmost importance. Running software applications (called apps) are one of the major factors influencing the power consumption in mobile devices. In order to meet user needs, mobile apps are becoming inherently complex and resource greedy. Therefore, fulfilling these requirements at the pace imposed by the market may degrade software construction quality and insert so-called energy code smells: bad patterns in the source code of an app that indicate a deeper problem and adversely affect power consumption. This work proposes a developer-oriented framework for identifying and fixing patterns via analyzing different application code flavors in a user-driven test scenario. A special app was designed in order to validate the Android implementation of the proposed methodology. The study results have shown significant improvement regarding energy efficiency after correcting one or more energy code smells, with a 4 to 30 percent decrease in battery drain. Additionally, the power consumption signature term is defined in the context of mobile applications. This paper presents a developer-oriented framework for assessing power consumption in mobile applications. Our key contributions include identifying significant energy code smells, demonstrating their impact on power consumption, and providing a toolset for developers to improve energy efficiency.

EACH year, the International Conference on Mobile Systems, Applications, and Services, or MobiSys as it is more commonly known, publishes a selection of cuttingedge research papers in mobile computing. MobiSys takes a broad systems perspective of mobile computing. The papers presented at the 2011 conference in June near Washington, DC ranged from smartphones to energy-efficiency, aspects of security, sensor networks and wireless protocols, and mobile applications and services. This special section brings to you revised versions of highly-ranked papers from MobiSys 2011. The conference was very competitive, with 25 papers being selected from 141 submissions in 2011. From the accepted papers, the PC chairs further selected three of the top papers as judged by the PC reviews and invited the authors of these papers to submit a revised paper to the IEEE Transactions on Mobile Computing. The three papers are a diverse set, and they offer you a glimpse into what is exciting in mobile computing today, as well as the different kinds of research that are presented at MobiSys. The first paper in the set is “Leveraging Smartphone Cameras for Collaborative Road Advisories,” by Emmanouil Koukoumidis, Margaret Martonosi, and LiShiuan Peh. In this paper, we learn about a mobile computing application that will advise the driver of a car to slow down or speed up in order to smoothly sail through green traffic lights, rather than wait at red traffic lights. What is new here? The work leverages the rich sensing capabilities of smartphones that make them such a compelling platform. It is not simply a GPS system that receives signal timing information from traffic signal controllers. Rather, placing the mobile on the windshield allows its camera to capture images that are analyzed in real-time to detect the phase of upcoming traffic lights. Information from all sources, including measurements of other nearby participants, is used to estimate traffic signal timings and make recommendations to the driver. As is typical for MobiSys, the system has been prototyped, and the paper reports on how well it works in small scale tests. In “Chameleon: A Color-Adaptive Web Browser for Mobile OLED Displays,” by Mian Dong and Lin Zhong, we shift focus from new mobile applications to the energy consumption of mobile displays. It is well known that the display is a substantial consumer of energy in mobile devices. In this work, Dong and Zhong explore the energy tradeoffs of OLED (Organic Light Emitting Diode) displays, which require no backlight but may be expensive in terms of energy to display bright images. Their answer is a system that targets web browsing to transform the colors of images in real time, shifting them from pixel values that are energyexpensive to alternative choices that are legible yet less expensive in terms of energy. An evaluation with a prototype and field trials shows both the potential to significantly extend battery life and a system that is valued by users. For the last paper of the set, we return to classic problems of mobile communications. In “Avoiding the Rush Hours: WiFi Energy Management via Traffic Isolation,” Justin Manweiler and Romit Roy Choudhury explore the latest in a series of designs to lower the amount of energy that mobile devices need to spend to communicate via 802.11. Standard techniques for power saving (such as 802.11 Power Save Mode) are assumed as a baseline. The problem is that the ever higher density of 802.11 APs (access points) tends to increase power consumption even with traditional power-saving designs. This is because clients will have to share the channel with a larger number of other clients and APs, and hence remain in an active state for longer periods of time to receive a burst of power save mode packets. Manweiler and Choudhury tackle this problem by decorrelating the cycles of overlapping APs, thus allowing the clients of one AP to sleep while the clients of another AP are awake. When prototyped and tested on real mobile applications, this design can greatly reduce the energy needed to power the network interface. We trust you will enjoy these papers. IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 5, MAY 2012 705

With the increasing availability of mobile applications, the usage of mobile devices, especially smart phones and tablets, has become popular nowadays. However, mobile devices are limited in battery, memory, storage, and processing capabilities. These constraints prevent mobile devices from widely running all kinds of rich mobile applications. Computation offloading is believed to be a potential solution to the hardware limitations of mobile devices for higher performance and/or energy savings. Computation offloading is often conducted on a remote server, typically in the cloud as reported in the literature. In this paper, we consider that it is not always necessary to offload all computations to a remote cloud server from a mobile device mainly due to the high communication delay that may be generated. Instead, the offloading target can be a nearby PC via a lower communication cost mechanism, such as Bluetooth, depending on the computation demands. We have examined the impact of various factors, such as computation workload, file size, and wireless communication protocols, and have investigated insights on power consumption through offloading to a nearby PC or a remote cloud server. Finally, we develop an adaptive algorithm based on experimental results to automatically select a computation resource which has capacity to execute computationally intensive applications and thus to save energy consumption of mobile devices.

With the rise in mobile device adoption, and growth in mobile application market expected to reach $30 billion by the end of 2013, mobile user expectations for pervasive computation and data access are unbounded. Yet, various applications, such as face recognition, speech and object recognition, and natural language processing, exceed the limits of standalone mobile devices. Such applications resort to exploiting larger resources in the cloud, which sparked researching problems arising from data and computational offloading to the cloud. Research in this area has mainly focused on profiling and offloading tasks to remote cloud resources, automatically transforming mobile applications by provisioning and partitioning its execution into offloadable tasks, and more recently, bringing computational resources (e.g. Cloudlets) closer to task initiators in order to save mobile device energy. In this work, we argue for environments in which computational offloading is performed among mobile devices forming what we call a Mobile Device Cloud (MDC). Our contributions are: (1) Implementing an emulation testbed for quantifying the potential gain, in execution time or energy consumed, of offloading tasks to an MDC. This testbed includes a client offloading application, an offloadee server receiving tasks, and a traffic shaper situated between the client and server emulating different communication technologies (Bluetooth 3.0, Bluetooth 4.0, WiFi Direct, WiFi, and 3G). Our evaluation for offloading tasks with different data and computation characteristics to an MDC registers up to 80% and 90% savings in time or energy respectively, as opposed to offloading to the cloud. (2) Providing an MDC experimental platform to enable future evaluation and assessment of MDC-based solutions. We create a testbed, shown in Figure 1, to measure the energy consumed by a mobile device when running or offloading tasks using different communication technologies. We build an offloading Android-based mobile application and measure the time taken to offload tasks, execute them, and receive the results from other devices within an MDC. Our experimental results show gains in time and energy savings, up to 50% and 26% respectively, by offloading within MDCs, as opposed to locally executing tasks. (3) Providing solutions that address two major MDC challenges. First, due to mobility, offloadee devices leaving an MDC would seriously compromise performance. Therefore, we propose several social-based offloadee selection algorithms that exploit contact history between devices, as well as friendship relationships or common interests between device owners or users. Second, we provide solutions for balancing power consumption by distributing computational load across MDC members to elongate and MDC's life time. This need occurs when users need to maximize the lifetime of an ensemble of devices that belong to the same user or household. We evaluate the algorithms we propose for addressing these two challenges using the real datasets that contain contact mobility traces and social information for conference attendees over the span of three days. Our results show the impact of choosing the suitable offloadee subset, the gain from leveraging social information, and how MDCs can live longer by balancing power consumption across their members.

The display subsystem of a mobile device usually consumes 38%–68% [1] of the total battery power in video streaming. Therefore, a few schemes have been designed to reduce the display power consumption. The basic idea is to dim the backlight level while properly compensating the pixel luminance to maintain image fidelity. The luminance compensation and proper backlight level calculation are computation intensive and demand per-frame luminance information. For these reasons, existing schemes only work for video-on-demand where each frame (and thus the luminance information) is available in advance. In addition, they demand additional computing resource support. Otherwise, if the computation is conducted on the mobile device, the power consumption due to such computation can easily offset the power savings from dimming the backlight. In this work, we set to investigate power saving for real-time video calls on mobile devices. Different from video-on-demand, real-time video calls are highly delay sensitive and the frame luminance information is not known in advance. Moreover, video calls often involve multiple streaming sources from multiple (≥2) participants, making it more difficult. Because there are few background changes and the frame rate is usually small in video calls, we design a Greedy Display Power saving scheme, called LCD-GDP, which utilizes the commonly available GPU on mobile devices without demanding additional support. Our design is implemented on WebRTC, a popular real-time web browser based video call standard. Experiments show that our scheme can save up to 33% power consumption in video calls without affecting the video call quality.

Chapter 10 - Mobile Applications and Devices

모바일 앱 사용에 영향을 미치는 요인에 관한 연구: 앱 카테고리 간 상관관계를 중심으로

2 - User characteristics

Digital video playback on mobile devices is fast becoming widespread and popular. Since mobile devices are typically resource constrained in terms of network bandwidth, battery power and available screen resolution, it is often necessary to formulate special encoding techniques in order to optimize power consumption during video streaming and playback. The existing H.264 standard is popular for video encoding on mobile devices, since it results in a low-bitrate video with visual clarity that is adequate for video playback on mobile devices. However, due to the complexity of the H.264 representation, the video decoding procedure is typically computationally intensive. In this paper, we propose a novel lossy video representation termed as Ligne-Claire (LC) video. LC videos are obtained via graphics overlay of outlines or silhouettes of objects in the video over an approximated texture video. Since the playback of LC video is typically meant for mobile devices, the visual quality of video is adequate for most mobile applications wherein the semantic content of the video can be characterized by object shapes and approximate texture information. Experimental results presented in the paper demonstrate that the proposed lossy LC video encoding scheme results in power savings of 50% or more during video playback compared to standard H.264-encoded videos, of similar video file size. In order to evaluate the visual quality of the LC video, we compare the performance of LC videos with H.264-encoded videos in the context of some typical computer vision tasks. Our results indicate that the performance of the computer vision algorithms on these videos is similar. This fact, coupled with subjective evaluation, and the resulting significant power savings, indicates that the proposed LC representation can be used effectively to encode video for power-constrained mobile devices.

The IEEE 802.11ad standard, with its 14 GHz of unlicensed spectrum around 60GHz, is touted as one of the key technologies for building the next generation of WLANs that will enable high throughput demanding mobile applications. However, there have been serious concerns regarding the susceptibility of mmWave links to mobility and blockage as well as smartphone energy consumption at Gigabit scale data rates. In this dissertation, first, through extensive measurement campaignswith commercial off-the-shelf (COTS) devices as well as a highly configurable software-defined radio (SDR) testbed, we characterize the performance and energy efficiency of mobile devices operating in 60 GHz WLANs and identify problems that prevent wide adoption of the mmWave technology in such devices. Then, using the insights from these measurement campaigns, we design solutions to tackle these problems and prototype them for real-world evaluation. This dissertation makes the following contributions: (i) We extensively study the performance and power consumption of802.11ad on commercial smartphones. We focus on the specific aspects affected by unique smartphone features, e.g., antenna placement or user mobility patterns, and compare the performance against that achieved by 802.11ad laptops in previous studies. We also compare 802.11ad against its main competitors 802.11ac and 802.11ax. Overall, our results show that 802.11ad is better suited to address the needs of emerging bandwidth-intensive applications in smartphones than its 5 GHz counterparts. At the same time, we identify several key research directions towards realizing its full potential. (ii) We extensively study the two main link adaptation mechanisms in 802.11ad,rate adaptation (RA) and beamforming. We undertake a large measurement campaign using an SDR-based testbed giving us complete access to the PHY and MAC layers. We look at the two link adaptation mechanisms separately and study the effectiveness of a few RA and beamforming heuristics. Further, look at the interaction between the two link adaptation mechanisms, specifically, which mechanism should be triggered when and in what order. We design a practical, standard-compliant link adaptation framework that leverages ML and PHY layer information to determine when to trigger link adaptation and which adaptation mechanism to use. (iii) To address the issues with mmWave link reliability, we explore the use ofmultiple frequency bands to couple the performance of 802.11ad with the reliability of legacy WiFi. To accomplish this, we develop a Multipath TCP (MPTCP) scheduler to efficiently use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. Further, we port MPTCP to a dual-band (5 GHz/60 GHz) smartphone, evaluate its power consumption, and provide recommendations towards the design of an energy-aware MPTCP scheduler. (iv) To enable high user QoE, and maintain that in the face of ever-changingnetwork conditions, applications such as virtual reality (VR) and video streaming perform quality adaptation. A key component of quality adaptation is throughput prediction. Thus, we extensively study the predictability of the network throughput of an 802.11ad WLAN in downloading data to an 802.11ad-enabled mobile device under varying mobility patterns and orientations of the mobile device. (v) With a dramatic increase in throughput requirements of applications andAP-user density in the near future, multi-user multi-stream communication in the 60 GHz band is required. To this end, the IEEE 802.11ay standard, successor to the current 802.11ad standard, includes support for simultaneous transmission over multiple data streams. Using an SDR-based testbed, we extensively study the performance of SU- and MU-MIMO in 60 GHz WLANs in multiple environments, analyze the performance in each environment, identify the factors that affect it, and compare it against the performance of SISO. Finally, we propose two heuristics that perform both beam and user selection with low overhead while outperforming previously proposed approaches.--Author's abstract

The Evaluation of The Most Used Mobile Devices Applications by Students

Background Mobile health (m-health) covers activities in the field of medicine and public health carried out by using mobile devices. It also includes mobile applications, which are one of the main branches of e-health. The aim of the study was to determine the relationship between the use of mobile health applications by students and everyday behaviour activities that promote health. Methods The study involved 208 students of three Wrocław universities. It was conducted from March till April 2018 and the online diagnostic survey (CAWI) method was used. Results 70.2% of respondents declared they had mobile health applications installed on their smartphone. 49% declared that they used mobile health applications, while 51% did not. Respondents who used the application significantly more often than non-users, took care of their health in almost all of the ways listed in the questionnaire: care about mental health(28%), the quality and duration of sleep(41%), healthy diet(64 %), physical activity(81%), implementation of health-related activities in everyday behaviour(28%), monitoring my health(52%). People, who use health applications significantly more often, undertake most of the physical activities (running 42.2%, working out the gym 39.2%, riding bike 38.2%, attending group classes 33,3%, swimming 21.6%). Respondents use application mostly for 'Registering, supporting physical activity'(80%) Conclusions Almost half of the examined group of students use mobile health applications in their daily lives and this group assesses themselves as health-conscious people in its various manifestations and aspects. These people are more likely to engage in various types of physical activity Key messages The results of the research clearly indicate the positive relationship between the use of mobile applications and health-promoting behaviours and undertaking physical activities. The development and dissemination of mobile applications can significantly affect the extension of possibilities for health promotion activities and supporting a healthy lifestyle.

During Internet streaming, a significant portion of the battery power is always consumed by the display panel on mobile devices. To reduce the display power consumption, backlight scaling, a scheme that intelligently dims the backlight has been proposed. To maintain perceived video appearance in backlight scaling, a computationally intensive luminance compensation process is required. However, this step, if performed by the CPU as existing schemes suggest, could easily offset the power savings gained from backlight scaling. Furthermore, computing the optimal backlight scaling values requires per-frame luminance information, which is typically too energy intensive for mobile devices to compute. Thus, existing schemes require such information to be available in advance. And such an offline approach makes these schemes impractical. To address these challenges, in this paper, we design and implement GoCAD, a GPU-assisted Online Content-Adaptive Display power saving scheme for mobile devices in Internet streaming sessions. In GoCAD, we employ the mobile device's GPU rather than the CPU to reduce power consumption during the luminance compensation phase. Furthermore, we compute the optimal backlight scaling values for small batches of video frames in an online fashion using a dynamic programming algorithm. Lastly, we make novel use of the widely available video storyboard, a pre-computed set of thumbnails associated with a video, to intelligently decide whether or not to apply our backlight scaling scheme for a given video. For example, when the GPU power consumption would offset the savings from dimming the backlight, no backlight scaling is conducted. To evaluate the performance of GoCAD, we implement a prototype within an Android application and use a Monsoon power monitor to measure the real power consumption. Experiments are conducted on more than 460 randomly selected YouTube videos. Results show that GoCAD can effectively produce power savings without affecting rendered video quality.

Mobile computing devices can offer older adults (ages 65+) support in their daily lives, but older adults often find such devices difficult to learn and use. One potential design approach to improve the learnability of mobile devices is a Multi-Layered (ML) interface, where novice users start with a reduced-functionality interface layer that only allows them to perform basic tasks, before progressing to a more complex interface layer when they are comfortable. We studied the effects of a ML interface on older adults’ performance in learning tasks on a mobile device. We conducted a controlled experiment with 16 older (ages 65--81) and 16 younger participants (age 21--36), who performed tasks on either a 2-layer or a nonlayered (control) address book application, implemented on a commercial smart phone. We found that the ML interface’s Reduced-Functionality layer, compared to the control’s Full-Functionality layer, better helped users to master a set of basic tasks and to retain that ability 30 minutes later. When users transitioned from the Reduced-Functionality to the Full-Functionality interface layer, their performance on the previously learned tasks was negatively affected, but no negative impact was found on learning new, advanced tasks. Overall, the ML interface provided greater benefit for older participants than for younger participants in terms of task completion time during initial learning, perceived complexity, and preference. We discuss how the ML interface approach is suitable for improving the learnability of mobile applications, particularly for older adults.