Battery Life Of Mobile Devices Research Articles

Framework for Agent-Based Multistage Application Partitioning Algorithm in Mobile Cloud Computing

The idea of computational offloading is quickly catching on in the world of mobile cloud computing (MCC). Today’s applications have heavy demands on power and computing resources, creating issues with energy consumption, storage capacity, and mobile device performance. Mobile devices may efficiently offload their calculations to cloud servers using the offloading paradigm and then get the processed results back onto the device. The investigation relates to identifying the specific application components that should be offloaded and run remotely and which parts are supposed to be treated locally. The applications need to be partitioned to differentiate between remote and local codes. In this paper, an agent-based multistage graph partitioning (ABMP) scheme is proposed. The framework of the scheme is based on three-tier architecture that includes mobile, cloudlet, and cloud for the execution of application tasks. The main goal is to provide an efficient partitioning and offloading scheme in the mobile cloud computing area. The results show that incorporating both agent-based multistage graph partitioning and offloading algorithms yields superior performance as compared to previous methods in terms of reducing execution costs and conserving battery life for mobile devices.

Multi-objective optimization for energy efficient cooperative communication in energy-constrained overlay Cognitive Radio Networks

With the rapid proliferation of wireless devices, energy efficiency and addressing spectrum scarcity have become critical global concerns. To address these challenges, this paper explores radio-frequency energy harvesting in Cognitive Radio Networks (CRNs) as a solution which can significantly extend the battery life of mobile devices. In this setup, energy-constrained secondary users (SUs) use Simultaneous Wireless Information and Power Transfer (SWIPT) technology to harvest energy from primary signals and aid primary transmission. We propose a joint time-switching and power-splitting technique to maximize energy harvesting, achieving a 97.7% accurate sub-optimal solution compared to the optimal (benchmark) result. Furthermore, we investigate optimal power allocation during cooperative communication and secondary transmission, resulting in a near-optimal solution with 98.5% accuracy. We also introduce a cooperative communication framework using the Hedonic game concept, enabling SUs to establish preferred partnerships with suitable primary users (PUs). Numerical results indicate that this cooperative framework enhances the secondary network’s overall utility, average SU satisfaction, and SU participation when compared to the non-cooperative approach.

A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR (Accuracy and Energy Aware-HAR)</i> model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR</i> introduces a “light-weight” <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine learning</i> on-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.

Decentralized Machine Learning Through Experience-Driven Method in Edge Networks

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing. To fully utilize the widely distributed data, we concentrate on a wireless edge computing system that conducts model training using decentralized peer-to-peer (P2P) methods. However, there are two major challenges on the way towards efficient P2P model training: limited resources (<i>e.g.</i>, network bandwidth and battery life of mobile devices) and time-varying network connectivity due to device mobility or wireless channel dynamics, which receives less attention in recent years. To address these two challenges, this paper studies the impact of topology construction on the P2P training performance. Specifically, we dynamically construct an efficient P2P topology, where model aggregation occurs at the edge. In a nutshell, we first formulate the topology construction for P2P learning (TCPL) problem with resource constraints as an integer programming problem. Then a learning-driven method is proposed to adaptively construct a topology at each training epoch. We evaluate the performance of our proposed algorithm through extensive simulations and physical platform. Evaluation results show that our method can improve the model training efficiency by about 11&#x0025; with resource constraints, reduce the communication cost by 30&#x0025; and the network traffic consumption by about 60&#x0025; under the same accuracy requirement compared to the benchmarks.

Optimized task scheduling for cost-latency trade-off in mobile fog computing using fuzzy analytical hierarchy process

Mobile Fog Computing (MFC) paradigm can be integrated as a unit called as Multi-Access Edge Computing (MFC) in a fifth-generation (5G) network. There are extensive researches coercing to the MFC. Task scheduling is an important issue in the area of MFC to solve computing capacities such as limited CPU power, storage capacity, memory constraints, and limited battery life in Mobile Devices (MDs). The multi-criteria decision-making problem in fog nodes has not been widely studied. According to the variety and difficulty of criteria, the scheduling in the fog node has become a challenge. The previous works in the tasks scheduling context considered a few criteria of dynamic scheduling without covering other enough criteria. Besides, in MFC, the tasks come with different priorities. We present a scheduling algorithm based on the Priority Queue, Fuzzy and Analytical Hierarchy Process namely PQFAHP in our paper. We use PQFAHP to combine several priorities and prioritize multi-criteria. In our paper, dynamic scheduling includes the completion time, energy consumption, RAM, and deadline criteria. Our experimental results show that the proposed algorithm can consider multi-criteria for scheduling Our proposed work is one of the multi-criteria algorithms that performs optimal results than several benchmark algorithms in terms of waiting time, delay, service level, mean response time, and the number of scheduled tasks on the MFC side. This paper has considerable contributions related to the scheduling of fog computing. For instance, it could decrease 14.2%, 49%, and 26% in average waiting time, delay, and energy consumption respectively, and increase 10.8% in service level.

Content-Adaptable ROI-Aware Video Storage for Power-Quality Scalable Mobile Streaming

The demand for mobile video streams is constantly increasing. With this demand comes a need for mobile devices to receive more videos at ever increasing quality. However, due to the large size of video data and intensive computational requirements, video streaming requires frequent memory access that consume a substantial amount of mobile device power; as a result, the battery life of mobile devices is limited. In this paper, we present a video content-adaptable Region-of-Interest (ROI)-aware video storage technique that promotes power savings. During the video encoding process on the transmitting server, based on the macroblock variance and ROI characterization, the “macroblocks of interest” are identified and embedded in the encoded bitstream. In the decoding process, a new frame buffer with dynamic power-quality trade-off is presented to adapt to the macroblock characteristics during run-time. Results from the system-level and circuit-level simulations show that the proposed technique enables substantially more truncated bits and significant power savings while delivering similar or better video quality as compared to other state-of-the-art solutions.

Meta-analytical comparison of energy consumed by two sorting algorithms

Mobile devices performance and uptime heavily depend on energy consumed at the hardware and software level. Hence implementation of efficient algorithms has become a crucial aspect for increasing the performance of such systems and battery life for mobile devices. Sorting algorithms are implicitly the building block of many program implementation. Over the past years, researchers have spent more time optimizing hardware components to reduce their energy consumption. However, it has not been so clear which sorting algorithm is more energy efficient. In this study, we conduct a meta-analytical comparison of the energy consumed by the two most common sorting algorithms namely quick sort and merge sort. Our study mainly focused on energy consumption for mobile devices and embedded systems. For our meta-analysis and literature review, we took into consideration studies published not more than 20 years ago. The meta-analytical results show that there is no significant difference between both algorithms in terms of energy efficiency.

Strategy for Reducing Delays and Energy Consumption in Cloudlet-Based Mobile Cloud Computing

In the paper, the problem of using cloudlet-based mobile cloud computing to solve the issues (resource deficiency and limited energy) that require large computing and memory resources on mobile devices has been studied. Energy-saving of mobile devices, resource limitations in mobile devices, and elimination of network delays are analyzed. It is shown that the solution of mobile users' tasks in the cloud leads to the increased battery life of mobile devices and reduces network delays, which is shown by mathematical calculations. The article considers the balanced distribution of the tasks in the cloudlet network. The paper also deals with the selection of cloudlets according to user requirements. The cases for which a cloud application can be loaded by the user were evaluated and a model was proposed using the possible values that determine the importance of cloudlets (user proximity, high reliability, etc.).

Power and Delay Efficient Multilevel Offloading Strategies for Mobile Cloud Computing

Mobile cloud computing has introduced offloading to save the battery life of mobile devices. In mobile cloud computing optimization of power and delay for offloading has become a vital research scope. However, migration of the storage and computation from the mobile device to the remote cloud server enhances the delay and power consumption. To overcome this difficulty, cloudlet comes which is located nearby the mobile device. Since the cloudlet may not be able to fulfill all the offloading requests, sometimes remote public cloud server is used for the same. As a result the power and delay consumptions are increased. For solving this difficulty, private cloud server is used in our scheme along with the cloudlet and public cloud server. In this paper multilevel full and partial offloading strategies are proposed based on cloudlet, private and public cloud servers. The power and delay consumption in the proposed methods are determined and compared with the existing offloading methods. The theoretical and experimental analyses demonstrate that the proposed multilevel offloading methods are power and delay efficient. The simulation results show that the proposed multilevel full and partial offloading strategies reduce the power consumption by approximately 8–9% and 20% respectively than the existing methods.

Signal Strength-Aware Adaptive Offloading with Local Image Preprocessing for Energy Efficient Mobile Devices

To prolong battery life of mobile devices, image processing applications often exploit offloading techniques which run some or all of the computations on remote servers. Unfortunately, the existing offloading techniques do not consider the fact that data transmission time and energy consumption of wireless network interfaces exponentially increase when signal strength decreases. In this paper, we propose an adaptive offloading for image processing applications, which considers wireless signal strength. To improve performance and energy efficiency of offloading, we also propose to adaptively exploit local preprocessing (executing image preprocessing on local mobile devices), considering wireless signal strength; the local preprocessing usually reduces the size of transmission image in offloading. Our proposed technique estimates performance and energy consumption of the following three methods, depending on the wireless signal strength: 1) local execution (executing all the computations on the local mobile devices), 2) offloading without local preprocessing, and 3) offloading with local preprocessing. Based on the estimated performance and energy consumption, our technique employs one among the three methods, which is expected to result in the best performance or energy efficiency. In our evaluation on an off-the-shelf smartphone, when a user prefers performance to energy, our proposed technique improves performance by 27.1 percent, compared to the conventional offloading technique that does not consider the signal strength. On the other hand, when a user prefers energy to performance, our proposed technique saves system-wide (not just CPU nor wireless network interface) energy consumption by 26.3 percent, on average, compared to the conventional offloading technique.

Peace: Privacy-Preserving and Cost-Efficient Task Offloading for Mobile-Edge Computing

The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations.

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.

Toward a modular precision ecosystem for high-performance computing

With the memory bandwidth of current computer architectures being significantly slower than the (floating point) arithmetic performance, many scientific computations only leverage a fraction of the computational power in today’s high-performance architectures. At the same time, memory operations are the primary energy consumer of modern architectures, heavily impacting the resource cost of large-scale applications and the battery life of mobile devices. This article tackles this mismatch between floating point arithmetic throughput and memory bandwidth by advocating a disruptive paradigm change with respect to how data are stored and processed in scientific applications. Concretely, the goal is to radically decouple the data storage format from the processing format and, ultimately, design a “modular precision ecosystem” that allows for more flexibility in terms of customized data access. For memory-bounded scientific applications, dynamically adapting the memory precision to the numerical requirements allows for attractive resource savings. In this article, we demonstrate the potential of employing a modular precision ecosystem for the block-Jacobi preconditioner and the PageRank algorithm—two applications that are popular in the communities and at the same characteristic representatives for the field of numerical linear algebra and data analytics, respectively.

Energy-Optimal Scheduling of Mobile Cloud Computing Based on a Modified Lyapunov Optimization Method

Application offloading is an effective method for extending the battery life of mobile devices by executing some tasks of a mobile application in the cloud. This paper provides an energy-optimal transmission rate scheduling scheme executed at mobile devices in application offloading process. Our objective is to conserve energy for the mobile devices by optimally adjusting the transmission rate of mobile applications. Instead of adopting the widely used dynamic programming (DP) algorithm, we propose a modified Lyapunov optimization method. The method is obtained by modifying the Lyapunov optimization method to adapt it for mobile cloud computing scenario. Compared to DP algorithm which is in need of the statistical information of the stochastic wireless channels, the most important advantage of our proposed method is that it can obtain a closed-form expression for transmission rate only according to the actual measurement of the channel gain in each time slot and thereby can avoid the deviation from prediction of the channel gain in future time slots. The numerical results demonstrate that our proposed method can save about 30% of the energy compared to DP algorithm.

Efficient mobile code offloading in heterogeneous wireless networks

SummaryMobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with big.LITTLE technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data‐intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m2. Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3× over the single application and can save an average of 25% power over the single application.

Joint Optimization for Residual Energy Maximization in Wireless Powered Mobile-Edge Computing Systems

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

A Survey on Recent OS-Level Energy Management Techniques for Mobile Processing Units

To improve mobile experience of users, recent mobile devices have adopted powerful processing units (CPUs and GPUs). Unfortunately, the processing units often consume a considerable amount of energy, which in turn shortens battery life of mobile devices. For energy reduction of the processing units, mobile devices adopt energy management techniques based on software, especially OS (Operating Systems), as well as hardware. In this survey paper, we summarize recent OS-level energy management techniques for mobile processing units. We categorize the energy management techniques into three parts, according to main operations of the summarized techniques: 1) techniques adjusting power states of processing units, 2) techniques exploiting other computing resources, and 3) techniques considering interactions between displays and processing units. We believe this comprehensive survey paper will be a useful guideline for understanding recent OS-level energy management techniques and developing more advanced OS-level techniques for energy-efficient mobile processing units.

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

Abstract Optimizing energy consumption in modern mobile handled devices plays a crucial role as lowering the power consumption impacts battery life and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the popularity of power-hungry applications, battery life in mobile devices is an important issue. However, the utilization pattern of large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs and the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented to reduce the energy consumption. This paper presents a framework for ENergy Optimization for MObile platform using User needS (ENOsMOUS). This framework is able to identify user contexts and to understand user habits, preferences and needs to improve the operating system power scheme. Machine Learning (ML) algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user satisfaction requirements. ENOrMOUS is a generic solution that manages the power knobs. When applied to the CPU frequency, the sound level, the screen brightness and the Wi-Fi, ENOrMOUS can lower the power consumption by up to 35% compared the out-of-the-box operating system power manager schemes with a negligible overhead.

Adaptive Content Server for Optimizing Battery Life in Mobile Devices

The latest growth of mobile technology has resulted in more users accessing network services via mobile devices, such as tablets or smartphones. Personalized look and content becomes more important. Mobile devices have different properties than desktop computers. Mobile devices do not have a large screen, convenient input-output devices, and have limited computing capabilities. Content adaptation is a common definition used for adaptation or customization of content transferred from the server via the HTTP protocol to the client. The research method is to adjust the content which will be transferred to the user based on the device resources i.e. the battery on the client side so that higher service quality will be achieved. In this study, researchers will conduct the development of content adaptation methods that consider the condition of battery resources on the devices. Battery resource estimation is done to determine the current condition of existing battery in accessing device. The appropriate content determination mechanism will be based on the value generated from the estimated resource of the battery on the device that has been done.

Eff-HAS: Achieve higher efficiency in data and energy usage on dynamic adaptive streaming

With the limited battery life of mobile devices and the full usage of shared network resources, mobile users are required to devise canny and amicable plans for energy and data consumption. In this paper, we propose an efficient chunk-scheduling strategy for adaptive video streaming which enhances per-power data utility. We first reveal that currently available chunk-scheduling strategies for adaptive video streaming do not consider the general viewing patterns of streaming users, causing a severe inefficiency in terms of not only data usage but also energy consumption. We then propose a energy- and data-efficient chunk scheduling algorithm that increases the per-power data utility for single bitrate encoded videos. We further extend the basic algorithm customizing it to dynamic adaptive streaming over HTTP (DASH) formatted video streaming systems, the most popular video streaming technique. We implemented the proposed scheme and the experiment results show that, while consuming 2% less energy, the scheme reduces the overall data wastage by 55% compared to a prior competing scheme.