25,447 research outputs found
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
Energy-Efficient Mobile Network I/O Optimization at the Application Layer
Mobile data traffic (cellular + WiFi) will exceed PC Internet traffic by
2020. As the number of smartphone users and the amount of data transferred per
smartphone grow exponentially, limited battery power is becoming an
increasingly critical problem for mobile devices which depend on the network
I/O. Despite the growing body of research in power management techniques for
the mobile devices at the hardware layer as well as the lower layers of the
networking stack, there has been little work focusing on saving energy at the
application layer for the mobile systems during network I/O. In this paper, to
the best of our knowledge, we are first to provide an in-depth analysis of the
effects of application-layer data transfer protocol parameters on the energy
consumption of mobile phones. We propose a novel model, called FastHLA, that
can achieve significant energy savings at the application layer during mobile
network I/O without sacrificing the performance. In many cases, our model
achieves performance increase and energy saving simultaneously.Comment: arXiv admin note: text overlap with arXiv:1805.03970 and substantial
text overlap with arXiv:1707.0682
Impact of Memory Frequency Scaling on User-centric Smartphone Workloads
Improving battery life in mobile phones has become a top concern with the increase in memory and computing requirements of applications with tough quality-of-service needs. Many energy-efficient mobile solutions vary the CPU and GPU voltage/frequency to save power consumption. However, energy-aware control over the memory bus connecting the various on-chip subsystems has had much less interest. This measurement-based study first analyse the CPU, GPU and memory cost (i.e. product of utilisation and frequency) of user-centric smartphone workloads. The impact of memory frequency scaling on power consumption and quality-of-service is also measured. We also present a preliminary analysis into the frequency levels selected by the different default governors of the CPU/GPU/memory components.We show that an interdependency exists between the CPU and memory governors and that it may cause unnecessary increase in power consumption, due to interference with the CPU frequency governor. The observations made in this measurement-based study can also reveal some design insights to system designers
Power Consumption Analysis of a Modern Smartphone
This paper presents observations about power consumption of a latest
smartphone. Modern smartphones are powerful devices with different choices of
data connections and other functional modes. This paper provides analysis of
power utilization for these different operation modes. Also, we present power
consumption by vital operating system (OS) components.Comment: 11 pages, 6 figures, 5 table
The Web for Under-Powered Mobile Devices: Lessons learned from Google Glass
This paper examines some of the potential challenges associated with enabling
a seamless web experience on underpowered mobile devices such as Google Glass
from the perspective of web content providers, device, and the network. We
conducted experiments to study the impact of webpage complexity, individual web
components and different application layer protocols while accessing webpages
on the performance of Glass browser, by measuring webpage load time,
temperature variation and power consumption and compare it to a smartphone. Our
findings suggest that (a) performance of Glass compared to a smartphone in
terms of power consumption and webpage load time deteriorates with increasing
webpage complexity (b) execution time for popular JavaScript benchmarks is
about 3-8 times higher on Glass compared to a smartphone, (c) WebP is more
energy efficient image format than JPEG and PNG, and (d) seven out of 50
websites studied are optimized for content delivery to Glass
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
Although the latest high-end smartphone has powerful CPU and GPU, running
deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet
classification on mobile devices is challenging. To deploy deep CNNs on mobile
devices, we present a simple and effective scheme to compress the entire CNN,
which we call one-shot whole network compression. The proposed scheme consists
of three steps: (1) rank selection with variational Bayesian matrix
factorization, (2) Tucker decomposition on kernel tensor, and (3) fine-tuning
to recover accumulated loss of accuracy, and each step can be easily
implemented using publicly available tools. We demonstrate the effectiveness of
the proposed scheme by testing the performance of various compressed CNNs
(AlexNet, VGGS, GoogLeNet, and VGG-16) on the smartphone. Significant
reductions in model size, runtime, and energy consumption are obtained, at the
cost of small loss in accuracy. In addition, we address the important
implementation level issue on 1?1 convolution, which is a key operation of
inception module of GoogLeNet as well as CNNs compressed by our proposed
scheme
Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
While the development of Vehicle-to-Vehicle (V2V) safety applications based
on Dedicated Short-Range Communications (DSRC) has been extensively undergoing
standardization for more than a decade, such applications are extremely missing
for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between
VRUs and vehicles was the main reason for this lack of attention. Recent
developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this
perspective. Leveraging the existing V2V platforms, we propose a new framework
using a DSRC-enabled smartphone to extend safety benefits to VRUs. The
interoperability of applications between vehicles and portable DSRC enabled
devices is achieved through the SAE J2735 Personal Safety Message (PSM).
However, considering the fact that VRU movement dynamics, response times, and
crash scenarios are fundamentally different from vehicles, a specific framework
should be designed for VRU safety applications to study their performance. In
this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P)
framework to provide situational awareness and hazard detection based on the
most common and injury-prone crash scenarios. The details of our VRU safety
module, including target classification and collision detection algorithms, are
explained next. Furthermore, we propose and evaluate a mitigating solution for
congestion and power consumption issues in such systems. Finally, the whole
system is implemented and analyzed for realistic crash scenarios
Energy-Performance Trade-offs in Mobile Data Transfers
By year 2020, the number of smartphone users globally will reach 3 Billion
and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic
the first time. As the number of smartphone users and the amount of data
transferred per smartphone grow exponentially, limited battery power is
becoming an increasingly critical problem for mobile devices which increasingly
depend on network I/O. Despite the growing body of research in power management
techniques for the mobile devices at the hardware layer as well as the lower
layers of the networking stack, there has been little work focusing on saving
energy at the application layer for the mobile systems during network I/O. In
this paper, to the best of our knowledge, we are first to provide an in depth
analysis of the effects of application layer data transfer protocol parameters
on the energy consumption of mobile phones. We show that significant energy
savings can be achieved with application layer solutions at the mobile systems
during data transfer with no or minimal performance penalty. In many cases,
performance increase and energy savings can be achieved simultaneously
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
PowerSpy: Location Tracking using Mobile Device Power Analysis
Modern mobile platforms like Android enable applications to read aggregate
power usage on the phone. This information is considered harmless and reading
it requires no user permission or notification. We show that by simply reading
the phone's aggregate power consumption over a period of a few minutes an
application can learn information about the user's location. Aggregate phone
power consumption data is extremely noisy due to the multitude of components
and applications that simultaneously consume power. Nevertheless, by using
machine learning algorithms we are able to successfully infer the phone's
location. We discuss several ways in which this privacy leak can be remedied.Comment: Usenix Security 201
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