최근 모바일 디바이스에서 D2D (Device-to-Device) 통신을 가능하게 하는 기술로 WLAN을 이용한 Wi-Fi Direct가 떠오르고 있다. 하지만 제한적인 전력을 사용하는 모바일 디바이스 특성상 전력을 많이 소비하는 WLAN은 에너지 비효율성 문제를 야기할 수 있다. 이러한 문제를 해결하기 위해 Wi-Fi Direct 표준에서는 전력 관리 기법으로 Opportunistic 기법과 Notice of Absence(NoA) 기법을 정의하고 있다. 하지만 표준에서는 각 전력 관리기법이 어떠한 환경에서 보다 효율적인지에 관한 구체적인 기준을 명시하지 않고 있다. 본 논문에서는 데이터 트래픽 패턴에 따라 두 전력 관리 기법의 성능이나 에너지 효율성이 매우 다른 양상을 보이고 있음을 ns-3 시뮬레이션에 기반한 다양한 실험을 통해 확인하고, 실제 데이터 트래픽 패턴 변화에 따라 전력 관리 기법을 동적으로 변경 하는 기법을 제안한다.

“Necessity is the Mother of Invention”- one of the most Important and Interesting Man Made Inventions is Mobile Phone. there is a Rapid Increase in the Number of Users of Mobile Phone in the Last Decade. Today, it's Hard to Find a Cell Phone that Doesn't Offer some Sort of "smart" Technology because it's in such a High Demand. the Use of Mobile Phone has Become an Integral Part of Human Lives. in this Paper, an Attempt is Made to Study the Optimization of 3-Dimensional Heat Transfer Phenomena in Mobile Phone Using ANSYS for Three Different Models and Different Companies such as HTC Desire C, Nokia Lumia 900 and Sony Xperia Tipo. the Actual Dimensions and Materials are Chosen for above Models. the Three Models are Modelled in Pro/E, then Meshed by 3D-10 Nodded Tetrahedral Thermal Solid Elements (SOLID87) and Analysed in ANSYS Software, by Applying Boundary Conditions. the Analysis is Carried-out to Find the Nodal Temperatures, Directional Thermal Fluxes. it is Found that the Optimum Heat Transfer Occurs at HTC Desire C and Validated with the References.

— Different subpixel layouts for multi-primary displays will be presented and their spatial performance analyzed. The layouts studied include red, green, blue, yellow, and cyan subpixels, arranged in 5/5, 5/4, and 5/3 configurations. In the 5/5 configuration, five primaries are arranged on five subpixels forming a square pixel. In the 5/4 configuration, five primaries are arranged on two square units, each of which have four subpixels so that the cyan and yellow subpixels are missing in alternate pixels. In the 5/3 layout, the multi-primary color matrix is placed on top of a standard RGB TFT backplane with a subpixel aspect ratio of 1:3, resulting in an increased period of the full color sequence. Different data-rendering methods for the modified color sequences were studied and their implication on the spatial performance were analyzed, given in terms of reproduction accuracy, i.e., the average S-CIELAB error between data reproduced on a reference display and that reproduced on the examined layout. The reproduction error as a function of the angular substance of a pixel is reported for different layouts and rendering methods and are compared to that of an RGB display. It will be shown that the modified multi-primary layouts reduce power consumption and provide good image quality for mobile applications.

xxx Each day the number of devices or systems with some level of processing is greater. This point has contributed to areas with little or no affinity to set an emerging class of systems: the Cyber-Physical Systems (CPS). They are systems in which the computational core is integrated into the physical, biological and engineering processes; so new capabilities can be integrated to them. These systems are formed by different components and most of them are powered by batteries, because of this they have different energy profiles. The finite nature of the battery capacity is a crucial aspect to the system performance because it interferes directly on the quantity, quality and durability of its features. Thus, the usage of techniques which provide the means for managing the power consumption becomes an essential requirement in energy-aware designs. Therefore, this thesis proposes a set of integrated actions that are performed via software. The purpose is to realize a management of the power consumption in CPSs that are batterypowered. The approach uses an energy consumption mathematical model which defined by an operation mode. The operation mode encapsulates the energy consumption of external peripherals, that are controlled by an embedded software, into the CPS. Thus, the active states of the system are characterized by computational tasks, used to perform the energy consumption profile. In this way, it is possible to estimate the battery life, which is used as a reference for setting the system demand. A CPS scenario was used to validate the approach which was characterized by a mobile robotic system. Some targeted experiments have been developed in order to measure the currents drawn by the robot peripherals and also by the computer system during its operation. The results have been compared with the estimated values using the proposed approach. This analysis confirms the validity of the hypothesis: there is a close relationship between the computer system and electromechanical devices, that is, the devices activation is related to the type of computational task performed by the system. Based on that, the considerations underlying the proposal are presented and they go toward the validation of the model and the feasibility of the developed approach. Finally, the contributions and limitations of the research, as well as the suggestions for a future work are related.

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of the Dissertation GreenDM: A Versatile Tiering Hybrid Drive for the Trade-Off Evaluation of Performance, Energy, and Endurance by Zhichao Li for the Degree of Doctor of Philosophy in Computer Science Stony Brook University 2014 There are trade-offs among performance, energy, and device endurance for storage systems. These trade-offs become more complex in storage system comprising different storage technologies. Designs optimized for one dimension or workload often suffer in another. Therefore, it is important to study the trade-offs so as to be able to adapt the system to workloads. As different types of drives have different traits, tiering hybrid drives are studied more closely. However, previous tiering hybrids are often designed for high throughput, efficient energy consumption, or improving endurance—leaving empirical study on the trade-offs being unexplored. Past endurance studies also lack a concrete model and metric to help study the trade-offs. Lastly, previous designs are often based on inflexible policies that cannot adapt easily to changing conditions. We designed and developed GreenDM, a versatile tiering hybrid drive that combines Flashbased SSDs with traditional HDDs; we present our endurance model to study the aforementioned trade-offs. GreenDM presents a block interface and requires no modifications to existing application software. GreenDM migrates hot data to the faster SSD and cold data to the slower HDD. GreenDM offers tunable parameters useful in adapting the system to many workloads. We have designed, developed, and carefully evaluated GreenDM with a variety of workloads using commodity SSD and HDD drives. We demonstrated the importance of versatility to be able to adapt to various workloads. Our thesis is that one must study the trade-offs among performance, energy, and endurance, especially in the ever more popular tiered storage systems, to enable adaptation to workloads. Our system is versatile so that it can adapt to different workloads to achieve certain trade-offs by adjusting the important system parameters. We also provide several interesting observations along the cost dimension. We developed a cost model for GreenDM and evaluated it under realistic cost metrics. Future storage system designs have to consider multiple optimizations dimensions: performance, energy, endurance, and dollar cost. ii We close with several interesting long-term future research. First, it will be interesting to provide automated control knobs for users to trade-off performance, energy efficiency, and endurance. Second, one could extend the two-tier system to three tiers and explore more tiering policies. Third, it would be useful to add security as an additional dimension to further explore these trade-offs. Forth, one could experiment with different storage devices and policies in the future, and help build more efficient storage systems to achieve high performance at minimum cost. Fifth and last, it would be interesting to provide control support at the CPU level as well to further justify the trade-offs among performance, energy, and endurance.

of Licentiate Thesis Author Timo Lilja Title of Thesis Fine-grained Energy Profiling in Mobile Devices Abstract Mobile phones have several use cases such as making a phone call, sending an SMS, browsing the Internet, or playing a game. Mobile phones are also equipped with a wide variety of hardware allowing these activities. The overall energy consumption is easily measurable but it does not explain how much power is dissipated on particular activities and which devices are mostly responsible for the consumption. Thus, this thesis defines the concept of fine-grained energy profiling where the total energy consumed is broken down into subsystems. This thesis examines mobile phone power dissipation and energy consumption analysis. It describes how to measure overall total energy consumed by the device and develops methods that allow breaking down the energy consumption to components or subsystems. Specifically, the tools developed in this thesis allow studying the energy consumed by the CPU, GPU, display, WiFi, Cellular 3G and SSD disk. The device studied here is the Nokia N900 Maemo/Linux phone. This thesis describes a full-system power measurement setup including a fake battery and DAQ acting as an electric power meter. The thesis also develops logging tools that monitor the subsystem load and provide a method for formulating a linear regression model between these two through a set of microbenchmarks. This model allows estimating the subsystem energy consumption and total energy consumption based on observed loads of the subsystems. The model achieved 80% accuracy when compared with measured total energy consumption to the total power dissipation predicted by model. Research Field

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and innovative applications for mobile consumer- electronic devices, especially for Smartphones are developing rapidly, advances in battery technology have not kept pace with rapidly growing energy demands. Since, the batteries are limited in size and therefore capacity, power consumption is a critical concern for battery driven Smartphone and this implies that managing energy well is paramount in such device. The core need for good energy management requires a better understanding of where and how the energy is used: how much of the system's energy is consumed by which parts of the system and under what circumstances. This paper focuses on understanding the power consumption of major hardware components and their usage on different scenarios. We present a detail analysis of the power consumption of iPhone and develop a method PPAS-programmable power analysis method to analyze power consumption in different scenarios. This work identifies the most promising areas to focus on for further improvements of power management as well as implement some effective techniques to optimize power consumption. Keywordspower consumption, iPhone, iOS important to their usability and needs to optimize the usage of power. Effective and efficient energy management requires good understanding of where and how the energy is used, how much energy is consumed by which parts of the system and under what circumstances. This paper attempts to answer these questions and provide a good analysis of the uses of power by different functionalities. We collect power consumption data using software which is a better solution for latest devices. Because it is very difficult to take physical power measurements at the component level on a piece of real hardware due to the integration of hardware components into a single chips or board. Some effective and optimized techniques are also discussed with comparison data which will help all the Smartphone application developers to

With user experience becoming richer and richer on Android smartphones, limited battery capacity has become a major concern for users. It is a good practice for Android to tell users where the battery power has gone. Android achieves this by a power profile provided by OEMs that specifies the power usage rate for each system component staying at each specific state. According to a recent study, screen is one of the most dominant power-hungry system components on Android smartphones. However, the accuracy of Android's screen power usage model is to be improved, taking not only the brightness, but also the displayed content into account. We modified Android source code to improve its screen power usage model and verified this improvement with experiments. The results show that our new screen power usage model is more accurate to a significant extent and introduces little overhead.

With the widespread use of mobile devices relying on limited battery power, the burden of optimizing applications for energy has shifted towards the application developers. In their quest to develop energy efficient applications, developers face the hurdle of measuring the effect of software change on energy consumption. A naive solution to this problem would be to have an exhaustive suite of test cases that are executed upon every change to measure their effect on energy consumption. This method is inefficient and also suffers from environment dependent inconsistencies. A more generalized method would be to relate software structural metrics with its energy consumption behavior. Previous attempts to relate change in objectoriented metrics to their effects on energy consumption have been inconclusive. We observe that structural information is global and executed tests are rarely comprehensive in their coverage, this approach is prone to errors. In this paper, we present a methodology to relate software energy consumption with software structural metrics considering the test case execution traces. Furthermore, we demonstrate that software structural metrics can be reliably related to energy consumption behavior of programs using several versions of three open-source iteratively developed android applications. We discover that by using our approach we are able to identify strong correlations between several software metrics and energy consumption behavior.

With the ubiquity and increased usage of mobile devices with higher processing capability, power consumption in such devices is an important issue. In this paper we propose a software based approach to reduce the power consumption of an Android application which is having higher graphics or rendering requirements. Graphics intensive applications such as games, internet browser and video player contribute most in draining the battery. Therefore, we concentrate most on such applications for reducing the power consumption. Our approach involves removing the surface view and directly utilizing the application's main window surface. We conduct an experiment to measure the power consumption when running an application with and without using surface view, and show that we get an improvement in the consumption. We also discuss about advantages and problems of the proposed method along with some possible solutions.

With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in real-time, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces reported/shared by other users/devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudo-identical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to “good” and “bad” areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others' signal traces, mobile devices can save energy up to 35 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.

With the superior color gamut and contrast, the OLED display technology has been widely used in mobile devices. Although, with self-emitting diodes, the OLED display can be potentially more power-efficient than the competing LCD display technology, it can often suffer from high energy consumption for displaying bright images. OLED has different energy consumptions for red, green, and blue color components, with diverse energy behaviors for different colors. To reduce the power consumption, this paper proposes Blind, a color management scheme, which exploits the human color perception inaccuracy. Blind changes a color space to another imperceptibly different color space with higher energy efficiency, achieving up-to 5.9% power reduction for smart phone applications.

With the rise of the usage of computers and mobile devices, and the higher price of electricity, energy management of software has become a necessity for sustainable software, devices and IT services. Energy consumption in IT is rising through the rise of web and distributed services, cloud computing, or mobile devices. Therefore, energy management approaches have been developed, ranging from optimizing software code, to adaptation strategies based on hardware resources utilization. However, these approaches do not use proper energy information for their adaptations rendering themselves limited and not energy-aware. They do not provide an energy feedback of software, and limited information is available on how and where energy is spend in software code. To address these shortcomings, we present, in this thesis, energy models, approaches and tools in order to accurately estimate the energy consumption of software at the application level, at the code level, and for inferring energy evolution models based on the method's own input parameters. We also propose Jalen and Jalen Unit, energy frameworks for estimating how much energy each portion of code consumes, and for inferring energy evolution models based on empirical benchmarking of software methods. By using software estimations and energy models, we are able to provide accurate energy information without the need of power meters or hardware energy investment. The energy information we provide also gives energy management approaches direct and accurate energy measurements for their adaptations and optimizations. Provided energy information also draws a model of energy consumption evolution of software based on the values of their input parameters. This gives developers knowledge on energy efficiency in software leading to choose some code over others based on their energy performance. The experimentations using the implementations of our energy models offer important information on how and where energy is spend in software. In particular, we provide empirical comparison of programming languages (PL), algorithms implementations, the cost of using a virtual machine in PL, compilers' options, and I/O primitives. They also allow the detection of energy hotspots in software, therefore focusing on the main spots where further lookups are needed for energy optimizations. Finally, we demonstrate how our benchmarking framework can detect energy evolution patterns based on input parameters strategies. With our contributions, we aim to advance knowledge in energy consumption in software by proposing models, approaches and tools to accurately measure energy at finer grains. In a nutshell, we build a software-centric energy microscope and conduct experiments aimed to understand how energy is being consumed in software, and directions to be taken for energy optimized software.

With the recent development of LTE‐A/5G technologies, data sharing among mobile devices offer an attractive opportunity to reduce Internet access. However, it requires smart strategies to share the data with low trade‐offs in time, cost, and energy. Several existing schemes offer a super‐peer‐based two‐tier model using a distributed hash table (DHT) organization for smart devices having device‐to‐device (D2D)/Bluetooth/WiFi capabilities. The primary focus of these schemes has been to reduce Internet usage by increased D2D content sharing. However, the real challenge is not in creating a two‐tier model, but evolving an efficient overlay that offers enhanced opportunities for D2D content sharing over the existing model. In this paper, we formulated a P‐median‐based selection of tier‐1 devices in a distribution network and solved it using the Lagrangian relaxation method. The tier‐2 devices become clients seeking content sharing services from tier‐1 devices. A strong motivation in this work is to raise a user's perception of the grade of service known as quality of experience (QoE). We analyzed the challenge for QoE assessment in resource‐constrained smartphones under the proposed model of enhanced D2D communication. Our focus is to establish a framework to evaluate QoE for applications and services over LTE‐A/5G networks with an improved D2D communication level. The simulation and the experimental results validate the claim that substantial improvements in QoE are possible with the proposed mathematical model for selecting and placing tier‐1 mobile devices and maintaining a DHT for D2D communication.

With the recent advances in battery-based mobile computing technologies, power-saving techniques in real-time embedded devices are becoming increasingly important. This paper presents a novel job scheduling policy for real-time systems, which aims at minimizing the power consumption of processor and memory without missing the deadline constraints of real-time jobs. To do so, we formulate the power saving techniques of processor voltage/frequency scaling and memory job placement as a unified measure, and show that it is a complex search problem that has the exponential time complexity. Thus, an efficient heuristic based on evolutionary computation is performed to cut down the huge searching space and find a reasonable schedule within the feasible time budget. To evaluate the proposed scheduling policy, we conduct experiments under various workload conditions. Our experimental results show that the proposed policy significantly reduces the energy consumption of real-time systems. Specifically, the average reduction in the energy consumption is 41.7% without deadline misses.

With the rapid proliferation of mobile devices, mobile cloud computing is emerging as an increasingly omnipresent paradigm enabling users to use battery-powered mobile devices to access a wide range of compute-intensive applications hosted on the clouds. Often, the assumption is that mobile devices consume less power when they access an application run on the cloud than when the application is run on the device itself. This, however, is increasingly questionable with the significant recent progress in improving power efficiency of mobile devices (e.g., using ultra low power GPUs). This paper aims at analyzing and comparing the benefits of these two alternatives using mobile cloud gaming as an example. Our evaluation shows that, despite the recent advances towards reducing power consumption in mobile devices, mobile cloud computing remains the best of the two alternatives in a wide range of scenarios.

With the rapid development of communication technology, the intelligent mobile terminal brings about great convenience to people’s life with rich applications, while its power consumption has become a great concern to researchers and consumers. Power modeling is the basis to understand and analyze the power consumption characteristics of the terminal. In this paper, we analyze the Bluetooth and hidden power consumption of the android platform and fix the power model of open-source Android platform. Then, a power consumption monitoring tool is implemented based on the model; the tool is divided into three layers, which are original information monitor layer, power consumption calculation layer, and application layer. The original monitor layer gets the power consumption data and running time of the different components under different states, the calculation layer calculates the power consumption of each hardware and each application based on the power model of each component, and the application layer displays the real-time power consumption of the software and hardware. Finally, we test our tool in real environment by using Xiaomi 9 Pro and perform comparison with actual instrument measurement; the error between the monitored value and the measured value is less than 5%.

With the rapid development of Android phones, the users' requirements are increasingly demanding. But the huge power consumption has seriously affected the users' communication demands. In this article, we introduce the power management modules and Wake Lock mechanism of the Android system, and analyze the power consumption of it. And we used DC power sources to measure the power consumption of every hardware modules of Android phones. The analysis of measured power consumption data could produce possible Android power-saving modes or a low power consumption mechanism, or lead us to the design of a new power consumption monitoring and power-saving software for Android. Finally, we summarize Android power management and make a prospect on the uncompleted work of Android power consumption monitoring software models.