Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphone\u27s battery

SPS: an SMS-based Push Service for Energy Saving in Smartphone\u27s Idle State

Despite of all the advances in smartphone technology in recent years, smartphones still remain limited by their battery life. Unlike other power hungry components in the smartphone, the cellular data and Wi-Fi interfaces often continue to be used even while the phone is in the idle state to accommodate unnecessary data traffic produced by some applications. In addition, bad reception has been proven to greatly increase energy consumed by the radio, which happens quite often when smartphone users are inside buildings. In this paper, we present a Short message service Push based Service (SPS) to save unnecessary power consumption when smartphones are in idle state, especially in bad reception areas. First, SPS disables a smartphone\u27s data interfaces whenever the phone is in idle state. Second, to preserve the real-time notification functionality required by some apps, such as new email arrivals and social media updates, when a notification is needed, a wakeup text message will be received by the phone, and then SPS enables the phone\u27s data interfaces to connect to the corresponding server to retrieve notification data via the normal data network. Once the notification data has been retrieved, SPS will disable the data interfaces again if the phone is still in idle state. We have developed a complete prototype for Android smartphones. Our experiments show that SPS consumes less energy than the current approach. In areas with bad reception, the SPS prototype can double the battery life of a smartphone

Performance Analysis of Smartphone-based Mobile Wi-Fi Hotspots Operating in a Congested Environment

In this work, we address the ubiquity of internet connections in smart cities by analyzing mobile Wi-Fi hotspots in terms of speed and energy efficiency in a congested Wi-Fi environment. We consider state-of-theart consumer smartphones in our work since they are the major devices in establishing mobile Wi-Fi hotspots nowadays. There are two main wireless connections in mobile Wi-Fi hotspots, the cellular connection and the Wi-Fi connection. It has been known that the speed of WiFi connections enormously supersedes the speed of cellular connections with the use of present technologies of each. In this work, we show that this well-known fact becomes controversial when establishing mobile Wi-Fi hotspots using smartphones in a nowadays typical congested Wi-Fi environment

Power Consumption Analysis, Measurement, Management, and Issues:A State-of-the-Art Review of Smartphone Battery and Energy Usage

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed

Tortoise or Hare? : Quantifying the Effects of Performance on Mobile App Retention

We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user’s decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.Peer reviewe

Scalable and Energy Efficient Software Architecture for Human Behavioral Measurements

Understanding human behavior is central to many professions including engineering, health and the social sciences, and has typically been measured through surveys, direct observation and interviews. However, these methods are known to have drawbacks, including bias, problems with recall accuracy, and low temporal fidelity. Modern mobile phones have a variety of sensors that can be used to find activity patterns and infer the underlying human behaviors, placing a heavy load on the phone's battery. Social science researchers hoping to leverage this new technology must carefully balance the fidelity of the data with the cost in phone performance. Crucially, many of the data collected are of limited utility because they are redundant or unnecessary for a particular study question. Previous researchers have attempted to address this problem by modifying the measurement schedule based on sensed context, but a complete solution remains elusive. In the approach described here, measurement is made contingent on sensed context and measurement objectives through extensions to a configuration language, allowing significant improvement to flexibility and reliability. Empirical studies indicate a significant improvement in energy efficiency with acceptable losses in data fidelity

Tracking the Evolution and Diversity in Network Usage of Smartphones

ABSTRACT We analyze the evolution of smartphone usage from a dataset obtained from three, 15-day-long, user-side, measurements with over 1500 recruited smartphone users in the Greater Tokyo area from 2013 to 2015. This dataset shows users across a diverse range of networks; cellular access (3G to LTE), WiFi access (2.4 to 5GHz), deployment of more public WiFi access points (APs), as they use diverse applications such as video, file synchronization, and major software updates. Our analysis shows that smartphone users select appropriate network interfaces taking into account the deployment of emerging technologies, their bandwidth demand, and their economic constraints. Thus, users show diversity in both how much traffic they send, as well as on what networks they send it. We show that users are gradually but steadily adopting WiFi at home, in offices, and public spaces over these three years. The majority of light users have been shifting their traffic to WiFi. Heavy hitters acquire more bandwidth via WiFi, especially at home. The percentage of users explicitly turning off their WiFi interface during the day decreases from 50% to 40%. Our results highlight that the offloading environment has been improved during the three years, with more than 40% of WiFi users connecting to multiple WiFi APs in one day. WiFi offload at offices is still limited in our dataset due to a few accessible APs, but WiFi APs in public spaces have been an alternative to cellular access for users who request not only simple connectivity but also bandwidth-consuming applications such as video streaming and software updates. Categories and Subject Descriptors General Terms Measurement Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Energy Awareness for Multiple Network Interface-Activated Smartphone

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 최성현.최신 스마트폰은 LTE와 Wi-Fi와 같은 네트워크 인터페이스 여러 개를 동시에 사용하여 전송율을 증가시키거나 네트워크 접속성을 향상시킬 수 있다. 이런 경우에, 스마트폰의 에너지 소모는 LTE와 Wi-Fi를 동시에 사용함에 따라 증가할 수 있다. 특히, 제한된 용량을 가지는 배터리로 동작을 하는 스마트폰에서 에너지 소모는 중요한 이슈이기 때문에, 에너지 증가와 성능 향상 사이의 trade-off를 고려하는 것이 요구된다. 에너지 인지 기술과 함께 스마트폰의 LTE와 Wi-Fi 인터페이스를 전략적으로 잘 사용함으로써, 배터리 에너지 소모를 줄임과 동시에 스마트폰 어플리케이션의 성능을 최적화 하는 것이 가능해진다. 본 논문에서는 LTE와 Wi-Fi 링크를 동시에 활용할 수 있는 스마트폰에서 에너지 인지를 가능하게 하는 다음 세 가지 전략을 고려하였다. (i) LTE와 Wi-Fi를 동시에 사용하는 스마트폰의 전력 소모 모델링, (ii) 스마트폰의 배터리 소모율의 실시간 예측 기법, (iii) dynamic adaptive streaming over HTTP (DASH) 기반의 비디오 스트리밍의 성능 최적화. 먼저, 동시에 여러 네트워크를 활성화하여 사용하는 스마트폰의 정밀한 전력 소모 모델링을 제시한다. 패킷 처리에 의해 소모되는 전력과 네트워크 인터페이스에서 소모되는 전력을 분해하여, 다중 네트워크 인터페이스를 활성화시키는 경우의 정밀한 전력 소모 모델을 구성한다. 다양한 시나리오에서 측정된 전력과 모델을 통해 예측된 전력을 비교하며 제안하는 전력 소모 모델의 정확성을 평가하였다. 기존 전력 소모 모델에 비해 단일 네트워크 통신의 경우에도 7%-35% 만큼 추정 오차를 줄였으며, 다중 네트워크 통신의 경우 추정 오차를 25% 줄임을 보였다. 두 번째로, 스마트폰의 실시간 에너지 인지를 가능하게 하기 위해서, 스마트폰에서 사용하고 있는 Li-ion 배터리의 특성을 고려하여 배터리 소모율을 추정하는 기법을 고안한다. Li-ion 배터리는 온도와 노화 상태에 따라서 가용 용량과 내부 저항이 달라지기 때문에, 온도와 노화 상태에 따라서 배터리 소모율이 달라질 수 있다. 배터리 특성을 모델링하는 것은 어려운 일이기 때문에, effective resistance 개념을 도입하여 용량과 내부 저항을 모르고도 배터리 소모율을 예측할 수 있는 BattTracker를 고안한다. BattTracker는 실시간 배터리 소모율을 최대 0.5초 마다 추정할 수 있다. 실제 스마트폰으로 다양한 실험을 통하여 BattTracker가 배터리 소모 예측을 5% 오차율 이내로 예측함을 보였으며, 이를 활용하면 높은 시간 해상도로 스마트폰의 에너지 인지 동작이 가능해진다. 마지막으로, 애너지 인지 기법과 LTE와 Wi-Fi 링크를 동시에 활용하는 것을 dynamic adaptive streaming over HTTP (DASH) 기반 비디오 스트리밍 어플리케이션에 적용한다. 스마트폰에서 LTE와 Wi-Fi 링크를 동시에 활용하는 것은 다양한 측면에서 DASH 기반의 비디오 스트리밍의 성능을 향상시킬 수 있다. 그러나, 배터리 에너지와 LTE 데이터 사용량을 절약하면서 끊김없는 고화질의 비디오를 스트리밍하는 것은 도전적인 일이다. 따라서, DASH 비디오를 시청하는 사용자의 Quality of Experience (QoE)를 향상시키기 위하여 LTE와 Wi-Fi를 동시에 활용하는 DASH video chunk 요청 기법인 REQUEST를 제안한다. REQUEST는 주어진 배터리 에너지와 LTE 사용량 예산 내에서 최적에 가까운 품질의 비디오를 끊김없이 스트리밍해 주는 것을 가능하게 한다. 다양한 환경에서의 시뮬레이션과 실 환경에서의 측정을 통하여, REQUEST가 기존 비디오 스트리밍 기법에 비해 평균 비디오 품질, 재버퍼링, 자원 낭비량 관점에서 상당히 우수함을 보인다. 요약하자면, 우리는 LTE와 Wi-Fi의 동시 사용하는 스마트폰에서의 전력 모델링 방법론, 실시간 배터리 소모율 추정 기법, DASH 기반 비디오 스트리밍 성능 최적화를 제안한다. 이 연구를 통해서, 우리는 프로토타입 구현과 실험 장비들을 통한 실측 기반으로 LTE와 Wi-Fi를 동시에 활용하는 스마트폰을 위한 에너지 인지 기술을 제안한다. 제안하는 기법들의 성능은 상용 스마트폰에 구현하여 실 환경에서의 성능 평가를 통해 검증하였다.State-of-the-art smartphones can utilize multiple network interfaces simultaneously, e.g., LTE and Wi-Fi, to enhance throughput and network connectivity in various use cases. In this situation, energy consumption of smartphones can increase while using both LTE and Wi-Fi interfaces simultaneously. Since energy consumption is an important issue for smartphones powered by batteries with limited capacity, it is required to consider the trade-off between energy increase and performance enhancement. By judiciously utilizing both LTE and Wi-Fi interfaces of smartphones and energy awareness techniques, it is enabled to optimize the performance of smartphones applications while saving battery energy. In this dissertation, we consider the following three strategies to enable the energy awareness for smartphones which utilize both LTE and Wi-Fi links: (i) Power modeling for smartphone which utilizes both LTE and Wi-Fi links simultaneously, (ii) real-time battery drain rate estimation for smartphones, and (iii) optimizing the performance of dynamic adaptive streaming over HTTP (DASH)-based video streaming for smartphones. First, an accurate power modeling is presented for the smartphones, especially, those capable of activating/utilizing multiple networks simultaneously. By decomposing packet processing power and power consumed by network interfaces, we construct the accurate power model for multiple network interface-activated cases. The accuracy of our model is comparatively evaluated by comparing the estimated power with the measured power in various scenarios. We find that our model reduces estimation error by 7%–35% even for single network transmissions, and by 25% for multiple network transmissions compared with existing power models. Second, in order to enable real-time energy awareness for smartphones, we develop a battery drain rate monitoring technique by considering characteristics of Li-ion batteries which are used by smartphones. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. Since it is difficult to model the battery characteristics, we develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with fine-grained time granularity. Finally, we adapt an energy awareness and utilize both LTE and Wi-Fi links for a Dynamic Adaptive Streaming over HTTP (DASH)-based video streaming application. Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of DASH-based video streaming in various aspects. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. Thus, we propose REQUEST, a video chunk request policy for DASH in a smartphone, which can utilize both LTE andWi-Fi to enhance users Quality of Experience (QoE). REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste. In summary, we propose a power modeling methodology, a real-time battery drain rate estimation method, and performance optimization of DASH-based video streaming for a smartphone which utilizes both LTE and Wi-Fi simultaneously. Through this research, we propose several energy-aware techniques for the smartphone, which especially utilizes both LTE and Wi-Fi, based on prototype implementation and the real measurement with experimental equipment. The performance of the proposed methods are validated by implementation on off-the-shelf smartphones and evaluations in real environments.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Power modeling of smartphone 3 1.2.2 Battery drain rate estimation 4 1.2.3 Optimizing the performance of video streaming 5 1.3 Main Contributions 6 1.3.1 PIMM: Power Modeling of Multiple Network Interface-Activated Smartphone 6 1.3.2 BattTracker: Real-Time Battery Drain Rate Estimation 7 1.3.3 REQUEST: Performance Optimization of DASH Video Streaming 8 1.4 Organization of the Dissertation 8 2 PIMM: Packet Interval-Based Power Modeling of Multiple Network Interface-Activated Smartphones 10 2.1 Introduction 10 2.2 Background 12 2.2.1 Power saving operations of Wi-Fi and LTE 12 2.2.2 Related work 13 2.3 Power Consumption Modeling 14 2.3.1 Modeling Methodology 15 2.3.2 Packet interval-based power modeling 21 2.4 Practical issues 30 2.4.1 Impact of packet length 30 2.4.2 Impact of channel quality 33 2.5 Performance Evaluation 34 2.5.1 On-line power estimation 35 2.5.2 Single network data connections 37 2.5.3 Multiple network data connections 42 2.5.4 Model generation complexity 45 2.6 Summary 46 3 BattTracker: Enabling Energy Awareness for Smartphone Using Li-ion Battery Characteristics 47 3.1 Introduction 47 3.2 Background 50 3.2.1 Smartphones battery interface 50 3.2.2 State of Charge (SoC) and Open Circuit Voltage (Voc) 50 3.2.3 Batterys internal resistance 53 3.2.4 Related Work 53 3.3 Li-ion Battery Characteristics 54 3.3.1 Impact of temperature and aging on Li-ion battery 54 3.3.2 Measuring battery characteristics 54 3.3.3 Battery characteristics models 57 3.4 Effective Internal Resistance 58 3.4.1 Effective internal resistance (re) 58 3.4.2 Battery drain rate and lifetime derived from re 60 3.5 BattTracker Design 61 3.5.1 Rd estimator 62 3.5.2 Voc estimator 62 3.5.3 re estimator 64 3.6 Performance Evaluation 65 3.6.1 Comparison schemes and performance metrics 66 3.6.2 Convergence time of re 67 3.6.3 Power consumption overhead of BattTracker 67 3.6.4 Comparison with measurement using equipment 68 3.6.5 BattTracker with aged batteries 70 3.6.6 BattTracker with various video applications 70 3.6.7 BattTracker with varying temperature 73 3.7 Summary 74 4 REQUEST: Seamless Dynamic Adaptive Streaming over HTTP for Multi-Homed Smartphone under Resource Constraints 75 4.1 Introduction 75 4.2 Background and Related Work 77 4.2.1 Background 77 4.2.2 Related Work 79 4.3 Motivation 80 4.3.1 Wi-Fi Throughput Fluctuation 80 4.3.2 Optimizing Resource Utilization 82 4.4 Proposed Chunk Request Policy 83 4.5 Problem Formulation 86 4.6 REQUEST Algorithm 90 4.6.1 Request Interval Adaptation 91 4.6.2 Chunk Request Adaptation 94 4.7 Performance Evaluation 96 4.7.1 Prototype-Based Evaluation 98 4.7.2 Trace-Driven Simulation 102 4.8 Summary 104 5 Concluding Remarks 106 5.1 Research Contributions 106 5.2 Future Work 107 Abstract (In Korean) 117Docto