Energy Consumption Of Smartphones Research Articles

Sustainable use of a smartphone and regulatory needs

AbstractThe significance of information and communication technologies (ICT) for the Paris Climate Agreement is continuously increasing because of its growing energy consumption. Here we examine the question for the smartphone and extend the investigation to more aspects of sustainability. Critical issues are identified for ten UN Sustainable Development Goals. Measurements of smartphone energy consumption show that a significant savings potential can be unlocked by reducing the data outflow and the large amount of personal data stored in data centers. Main discrepancies are also traced to the oligopolistic market structure of operating systems (OSs), messenger services, and social media apps. Technical means for a sustainable smartphone use are suggested as alternative OSs, social media channels of the Fediverse, as well as free and open‐source software. Finally, societal conditions are emphasized to make the market for OSs and apps more diverse so that a sustainable smartphone use can generally prevail.

Energy consumption of smartphones and IoT devices when using different versions of the HTTP protocol

HTTP is frequently used by smartphones and IoT devices to access information and Web services. Nowadays, HTTP is used in three major versions, each introducing significant changes with respect to the previous one. We evaluated the energy consumption of the major versions of the HTTP protocol when used in the communication between energy-constrained devices and cloud-based or edge-based services. Experimental results show that in a machine-to-machine communication scenario, for the considered client devices – a smartphone and a Single Board Computer – and for a number of cloud/edge services and facilities, HTTP/3 frequently requires more energy than the previous versions of the protocol. The focus of our analysis is on machine-to-machine communication, but to obtain a broader view we also considered a client–server interaction pattern that is more browsing-like. In this case, HTTP/3 can be more energy efficient than the other versions.

Measuring the Energy of Smartphone Communications in the Edge-Cloud Continuum: Approaches, Challenges, and a Case Study

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As computational resources are placed at different points in the edge-cloud continuum, not only the responsiveness on the client side is affected, but also the energy spent during communications. We summarize the main approaches used to estimate the energy consumption of smartphones and the main difficulties that are typically encountered. A case study then shows how such approaches can be put into practice. Results show that the edge is favorable in terms of energy consumption, compared to more remote locations.</i>

Frequency-independent smartphone peripherals energy consumption estimation

The power consumption of mobile devices is a hot topic these days, and it is important to address it when developing applications. One of the most popular ways to measure it is accessing internal sensors using Android Debug Bridge (ADB). We discovered that measurement frequency may skew the power readings. Based on this approach we propose our own algorithm for calculating smartphone energy consumption constants — the power in milliamperes at nominal voltage for different peripherals states. Our algorithm takes measurement frequency bias into account, and its results are compared with the method previously published in literature as well as the baseline data from power profile. We conclude that the developed approach provides better estimation.

Feature extraction and feature selection in smartphone-based activity recognition

Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.

SOD

A major concern for today’s smartphones is their much faster battery drain than traditional feature phones, despite their greater battery capacities. The difference is mainly contributed by those more powerful but also much more power-consuming smartphone components, such as the multi-core application processor and the high-definition (HD) display. While the application processor must be active when any smart apps are being used, it is also unnecessarily waken up, even during idle periods, to perform operations related to basic phone functions (i.e., incoming calls and text messages). In addition, the power-hungry HD display is also used unnecessarily for such basic functions. In this article, we investigate how to increase the battery life of smartphones by minimizing the use of application processor and HD display for operations related to basic functions. We find that the application processor is often waken up by a process running on it, called the Radio Interface Layer Daemon (RILD), which interfaces the user and apps to the GSM/LTE cellular network. In particular, we demonstrate that a great amount of energy could be saved if RILD is stopped, such that the application processor can sleep more often. Based on this key finding, we design a Smart On Demand (SOD) configuration that reduces the smartphone energy consumption by running RILD operations on a secondary low-power microcontroller and by using a secondary low-power display to interface the user with basic functions. As a result, basic phone functions can be handled at much lower energy costs and the power-consuming components, i.e., application processor and HD display, are waken up only when one needs to use any smart apps, in an on-demand manner. We have built a hardware prototype of SOD and evaluated it with real user traces. Our results show that SOD can increase its battery life by up to 2.5 more days.

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones.

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers’ vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

An energy model of 4G smartphone oriented towards typical network applications

4G has achieved considerable success because of its high data rate. But the short battery lifetime of mobile devices hinders the development of 4G. To improve energy efficiency, much effort has been made to study the process of energy consumption in smartphones. Some hardware-based methods, using expensive instruments, are correct but unhandy. Some software-based methods are flexible, but incorrect, because they often omit the influence from communication performance. This paper proposes a smartphone energy model where typical applications (HTTP and FTP) and the wireless traffic data flow are considered. Experimental results show that the energy model accurately estimates the smartphone energy consumption in one operation of the application, whose accuracy rate is higher than 90%. This work is the first step toward the development of a new software-based method, which accurately estimates smartphone energy consumption.

User-aware Frame Rate Management in Android Smartphones

Frame rate has a direct impact on the energy consumption of smartphones: the higher the frame rate, the higher the power consumption. Hence, reducing display refreshes will reduce the power consumption. However, it is risky to manipulate frame rate drastically as it can deteriorate user satisfaction with the device. In this work, we introduce a screen management system that controls the frame rate on smartphone displays based on a model that detects user dissatisfaction due to display refreshes. This approach is based on understanding when higher frame rates are necessary, and providing lower frame rates —thus, saving power— if the lower rate is predicted not to cause user dissatisfaction. According to the results of our first user survey with 20 participants, individuals show highly varying requirements: while some users require high frame rates for the highest satisfaction, others are equally satisfied with lower frame rates. Based on this observation, we develop a system that predicts user dissatisfaction on the runtime and either increases or decreases the maximum frame rate setting. For user dissatisfaction predictions, we have compared two different approaches: (1) static model, which uses dissatisfaction characteristics of a fixed group of people, and (2) user-specific model, which is learning only from the specific user. Our second set of experiments with 20 participants shows that users report 32% less dissatisfaction and 4% more dissatisfaction than the default Android system with user-specific and static systems, respectively. These experiments also show that, compared to the default scheme, our mechanisms reduce the power consumption of the phone by 7.2% and 1.8% on average with the user-specific and static models, respectively.

Online knapsack of unknown capacity: How to optimize energy consumption in smartphones

We propose a new variant of the standard online knapsack problem where the only information missing to the provided instances is the capacity B of the knapsack. We refer to this problem as the online Knapsack of Unknown Capacity (KUC) problem. Any algorithm solving the KUC problem must provide a strategy for filling online the knapsack until its capacity is revealed. When the knapsack capacity is revealed, no other item can be inserted and also the last inserted item is discarded if it does not completely fit in the knapsack.Apart for the interest in a new version of the fundamental knapsack problem, the motivations that lead to define this new variant come from energy consumption constraints in smartphones communications. We provide lower and upper bounds to the problem for various cases. In general, we design an optimal algorithm admitting a 12-competitive ratio. When all items admit uniform ratio of profit over size, our algorithm provides a 4986=.569… competitive ratio that leaves some gap with the provided bound of 1φ=.618…, the inverse of the golden number. We then conduct experimental analysis for the competitive ratio guaranteed algorithms compared to the optimum and to various heuristics.

Profiling Energy Efficiency and Data Communications for Mobile Internet of Things

This paper proposes a novel power management solution for resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and asynchronous power consumption incurred by heterogeneous components including their on-board sensors. Their interaction with the cloud allows them to offload computation tasks and access remote data storage. In this work, we aim at monitoring the power consumption behaviours of the smartphones, profiling both individual applications and the system as a whole, to make better decisions in power management. We design a cloud orchestration architecture as an epic predictor of behaviours of smart devices by extracting their application characteristics and resource utilization. We design and implement this architecture to perform energy profiling and data analysis on massive data logs. This cloud orchestration architecture coordinates a number of cloud-based services and supports dynamic workflows between service components, which can reduce energy consumption in the energy profiling process itself. Experimental results showed that small portion of applications dominate the energy consumption of smartphones. Heuristic profiling can effectively reduce energy consumption in data logging and communications without scarifying the accuracy of power monitoring.

Energy-Efficient In-Memory Paging for Smartphones

Smartphones are becoming increasingly energy-hungry to support feature-rich applications, posing a lot of pressure on battery lifetime and making energy consumption a non-negligible issue. In particular, dynamic random access memory (DRAM)-based main memory subsystem is a major contributor to the energy consumption of mobile devices. In this paper, we propose direct read (DR). Swap, an energy-efficient in-memory paging design to reduce energy consumption in smartphones. In DR. Swap, we adopt emerging energy-efficient nonvolatile memory (NVM) and use it as the swap area. Utilizing NVMs byte-addressability, we propose DR which guarantees zero memory copy for read-only requests when accessing a page in swap area. To better understand the energy consumption of swapping, we build an energy model to analyze the energy consumption of different paging architectures. We evaluate DR. Swap based on the Google Nexus 5 smartphone, experimental results show that our technique can reduce more than 50% energy consumption compared to DRAM backed swapping.

Sparse mobile crowdsensing: challenges and opportunities

Sensing cost and data quality are two primary concerns in mobile crowdsensing. In this article, we propose a new crowdsensing paradigm, sparse mobile crowdsensing, which leverages the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated, thus lowering overall sensing cost (e.g., smartphone energy consumption and incentives) while ensuring data quality. Sparse mobile crowdsensing applications intelligently select only a small portion of the target area for sensing while inferring the data of the remaining unsensed area with high accuracy. We discuss the fundamental research challenges in sparse mobile crowdsensing, and design a general framework with potential solutions to the challenges. To verify the effectiveness of the proposed framework, a sparse mobile crowdsensing prototype for temperature and traffic monitoring is implemented and evaluated. With several future research directions identified in sparse mobile crowdsensing, we expect that more research interests will be stimulated in this novel crowdsensing paradigm.

A Personalized Network Activity-Aware Approach to Reducing Radio Energy Consumption of Smartphones

The radio energy consumption takes a large portion of the total energy consumption in smartphones. However, a significant portion of radio energy is wasted in a special waiting interval, known as the tail time after a transmission is completed while waiting for a subsequent transmission. In order to reduce the wasted energy in the tail time, the fast dormancy feature allows a quick release of a radio connection in the tail time. For supporting the fast dormancy efficiently, it is important to accurately predict whether a subsequent transmission will occur in the tail time. In this paper, we show that there are strong personal characteristics on how user interacts with a radio network within the tail time. Based on these observations, we propose a novel personalized network activity-aware predictive dormancy technique, called Personalized Diapause (pD). By automatically identifying user-specific tail-time transmission characteristics for various network activities, our proposed technique takes advantages of personalized high-level network usage patterns in deciding when to release radio connections. Our experimental results using real network usage logs from 25 users show that pD can reduce the amount of the wasted tail time energy by 51 percent on average, thus saving the total radio energy consumption by 23 percent with less than 10 percent reconnection increase.

An Energy Model of 4G Smartphone Oriented Towards Typical Network Applications

4G has achieved considerable success because of its high data rate. But the short battery lifetime of mobile devices hinders the development of 4G. To improve energy efficiency, much effort has been made to study the process of energy consumption in smartphones. Some hardware-based methods, using expensive instruments, are correct but unhandy. Some software-based methods are flexible, but incorrect, because they often omit the influence from communication performance. This paper proposes a smartphone energy model where typical applications (HTTP and FTP) and the wireless traffic data flow are considered. Experimental results show that the energy model accurately estimates the smartphone energy consumption in one operation of the application, whose accuracy rate is higher than 90%. This work is the first step toward the development of a new software-based method, which accurately estimates smartphone energy consumption.

Partial mobile application offloading to the cloud for energy-efficiency with security measures

Mobile applications are becoming computationally intensive nowadays due to the increasing convenience, reliance on, and sophistication of smartphones. Nevertheless, battery lifetime remains a major obstacle that prohibits the large-scale adoption of such apps. Mobile cloud computing is a promising solution whereby apps are partially processed in the cloud to minimize the overall energy consumption of smartphones. However, this will not necessarily save energy if there is no systematic mechanism to evaluate the effect of offloading an app onto the cloud. In this paper, we present a mathematical model that represents this energy consumption optimization problem. We propose an algorithm to dynamically solve the problem while taking security measures into account. We also propose the free sequence protocol (FSP) that allows for the dynamic execution of apps according to their call graph. Our experimental setup consists of an Android smartphone and a Java server in the cloud. The results demonstrate that our approach saves battery lifetime and enhances performance. They also show the effects of workload amount, network type, computation cost, security operations, signal strength, and call graph structure on the optimized overall energy consumption.

Monitoring Energy Consumption of Smartphone Apps Using Fuzzy Clustering

Energy management is an important aspect of future mobile Internet. The energy consumption issue of mobile apps has attracted attention of many users and developers. Currently there is an increasing trend of malicious or low-quality apps which has very low energy efficiency. Existing work for evaluating mobile apps can only get limited information about the energy consumption of smartphones, and there are rarely methods proposed for evaluating the performance of individual applications. In this paper, we mainly implemented an application management procedure for Android, including some basic operations for checking and managing applications. Providing some information about individual apps and using our PEA algorithm to calculating resource usage of each app for users to judge which running app needs to stop, even delete it. In here, we use fuzzy C-average clustering algorithm to calculating energy consumption of individual application at terminal server. And experimental results show the expected effect.

Modeling Energy Consumption of Data Transmission Over Wi-Fi

Wireless data transmission consumes a significant part of the overall energy consumption of smartphones, due to the popularity of Internet applications. In this paper, we investigate the energy consumption characteristics of data transmission over Wi-Fi, focusing on the effect of Internet flow characteristics and network environment. We present deterministic models that describe the energy consumption of Wi-Fi data transmission with traffic burstiness, network performance metrics like throughput and retransmission rate, and parameters of the power saving mechanisms in use. Our models are practical because their inputs are easily available on mobile platforms without modifying low-level software or hardware components. We demonstrate the practice of model-based energy profiling on Maemo, Symbian, and Android phones, and evaluate the accuracy with physical power measurement of applications including file transfer, web browsing, video streaming, and instant messaging. Our experimental results show that our models are of adequate accuracy for energy profiling and are easy to apply.

Measuring Variance between Smartphone Energy Consumption and Battery Life

A case study reveals that energy savings do not always translate to longer smartphone battery life and that evaluating any savings plan must be based on battery consumption, not energy used.

Dynamic VoIP codec selection on smartphones

Voice over IP (VoIP) applications can choose a plethora of different speech codecs, which differ in bandwidth, listening speech quality, and resilience to quality degradation under packet loss. However, VoIP Codecs also exhibit differences in facets such as computational complexity or traffic generated that impact on the energy consumption of smartphones due to the use of processor. In this work deals with the study of energy consumption differences among VoIP codecs. We compare the execution time required to encode/decode reference conversations. Our results show that computational complexity has a significant impact on battery consumption (a factor of up to 10 was found between different codecs). Based on our results, we provide a ranking of energy efficiency. We also propose a simple algorithm for codec dynamic selection considering the dimensions of quality, energy and bandwidth. Our algorithm reacts to network conditions choosing the codec that provides less battery consumption constrained to user-defined targets for minimum quality and maximum codec bitrate.