Smartphone Power Consumption Characterization and Dynamic Optimization Techniques for OLED Display

Smartphones have emerged as the most popular and frequently used platform for the consumption of multimedia. Following the rapid growth of application number and the explosion of cellular network bandwidth, high power consumption, and limited battery capacity remain as the major challenges in smartphone designs. Therefore, lots of research is made to characterize and optimize the smartphone power performance. However, the existing research approaches on smartphone power characterization generally ignore the impact from the components' varying performance in different applications, as well as users' behavior during the practical usage. Hence, the power optimization techniques in the modern smartphone are inflexible to adapt to different application scenarios and user behaviors. In this dissertation, I first proposed a new smartphone power consumption characterization and analysis approach -- ``SEER'', which was associated with both user ethological and smartphone evolutionary perspectives. The real-time power consumption is measured with a set of the most popular applications on different generations of Samsung Galaxy smartphones. And deep analysis is made to find how each smartphone component is utilized in different applications, and how the users' daily usage patterns impact on final energy consumption. The experiments show that some traditional power-hungry components, such as Wi-Fi and CPU, actually consume much less energy in practical daily usage. Meanwhile, OLED display panel is still the biggest power consumer in the whole smartphone system; even it's considered the most promising low power display technology. To further optimize the display power consumption with OLED. I further proposed a set of dynamic power optimization techniques for OLED display, balancing the real-time power performance and the user visual perception experience. In this dissertation, the optimization is full-filled at three different levels: 1) Hardware based Optimization: Based on the traditional AMOLED display pixel driver, a novel DVS-friendly OLED driver design is proposed, which can minimize the display color distortion under aggressive supply voltage scaling. Correlated fine-grained DVS schemes (DiViCi) are also proposed to utilize the DVS-friendly driver into video streaming applications. 2) Software based Optimization: Despite the hardware modification, a dynamic OLED power model is built to evaluate the OLED panel power consumption and human visual perception quality assessment. A novel video category based dynamic tone mapping (DaTuM) technique is proposed for video streaming; 3) User Interaction based Optimization: The user interaction and visual perception during the display content capture phase are also taken into consideration, a novel OLED power friendly video recording application (MORPh) was also proposed. Dedicated real-time management and reliability enhancement schemes are explored to promote the applicability of the proposed approaches . Experiments show that, with these power optimization techniques, the OLED display panel power performance on smartphone device is significantly improved with reasonable visual quality controllability

Profiling Power Consumption on Mobile Devices

The proliferation of mobile devices, and the migration of the information access paradigm to mobile platforms, motivate studies of power consumption behaviors with the purpose of increasing the device battery life. The aim of this work is to profile the power consumption of a Samsung Galaxy I7500 and a Samsung Nexus S, in order to understand how such feature has evolved over the years. We performed two experiments: the first one measures consumption for a set of usage scenarios, which represent common daily user activities, while the second one analyzes a context-aware application with a known source code. The first experiment shows that the most recent device in terms of OS and hardware components shows significantly lower consumption than the least recent one. The second experiment shows that the impact of different configurations of the same application causes a different power consumption behavior on both smartphones. Our results show that hardware improvements and energy-aware software applications greatly impact the energy efficiency of mobile device

An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context

Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy

A Home Security System Based on Smartphone Sensors

Several new smartphones are released every year. Many people upgrade to new phones, and their old phones are not put to any further use. In this paper, we explore the feasibility of using such retired smartphones and their on-board sensors to build a home security system. We observe that door-related events such as opening and closing have unique vibration signatures when compared to many types of environmental vibrational noise. These events can be captured by the accelerometer of a smartphone when the phone is mounted on a wall near a door. The rotation of a door can also be captured by the magnetometer of a smartphone when the phone is mounted on a door. We design machine learning and threshold-based methods to detect door opening events based on accelerometer and magnetometer data and build a prototype home security system that can detect door openings and notify the homeowner via email, SMS and phone calls upon break-in detection. To further augment our security system, we explore using the smartphone’s built-in microphone to detect door and window openings across multiple doors and windows simultaneously. Experiments in a residential home show that the accelerometer- based detection can detect door open events with an accuracy higher than 98%, and magnetometer-based detection has 100% accuracy. By using the magnetometer method to automate the training phase of a neural network, we find that sound-based detection of door openings has an accuracy of 90% across multiple doors