Battery Life Of Mobile Devices Research Articles

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.

Adaptive prediction resolution video coding for reduced DRAM bandwidth

We present a novel approach to video coding that can dramatically reduce decoder DRAM bandwidth requirements while incurring a minimal reduction in compression efficiency, and which may yield an increase in compression efficiency under certain circumstances. Our approach is based on the principle that areas of video pictures where there is high motion are typically captured with significant blur along the direction of motion. This blur permits the judicious use of reduced resolution reference pictures for prediction without significantly reducing prediction quality. Our approach makes it feasible to limit worst-case DRAM bandwidth through the use of reasonably sized on-chip caches for pixel data, which can lead to provably compliant real-time behavior. The reductions in DRAM bandwidth can be expected to yield commensurate reductions in DRAM power dissipation, and consequently improvements in battery life for mobile devices. Although we concentrate our analysis on decoders, our approach can yield even greater advantages for encoders, which require additional bandwidth for motion estimation. We show that the compression efficiency of our encoder can approach that of a standard reference encoder on natural video sequences, but that it may fall short by a modest amount on pathological synthetic sequences.

Viewer-Aware Intelligent Efficient Mobile Video Embedded Memory

Embedded memory is a critical component in today’s mobile video processing systems, increasingly dominating power consumption and shortening battery life of mobile devices. Traditional hardware-level power optimization techniques usually come with significant implementation overhead to solve the memory failure problem during low-voltage operations. This paper presents a novel mobile video memory to exploit the power saving opportunities provided by a viewer experience under environmental visual interference. The viewing contexts, in particular the ambient luminance, significantly influence the quality of the viewer experience, and in the context with higher luminance levels, mobile users have higher tolerance to the video degradation. Accordingly, the memory failures can be introduced adaptively to achieve power savings without influencing the viewer experience. To meet the silicon area constraint in mobile devices, a simple but an efficient hardware implementation scheme is developed to minimize area overhead. The experimental results based on a 45-nm CMOS technology show that, as compared with the conventional memory design, the proposed technique can achieve up to 48% power savings with good perceivable quality and negligible implementation overhead.

A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.

Caching to Reduce Mobile App Energy Consumption

Mobile applications consume device energy for their operations, and the fast rate of battery depletion on mobile devices poses a major usability hurdle. After the display, data communication is the second-biggest consumer of mobile device energy. At the same time, software applications that run on mobile devices represent a fast-growing product segment. Typically, these applications serve as front-end display mechanisms, which fetch data from remote servers and display the information to the user in an appropriate format—incurring significant data communication overheads in the process. In this work, we propose methods to reduce energy overheads in mobile devices due to data communication by leveraging data caching technology. A review of existing caching mechanisms revealed that they are primarily designed for optimizing response time performance and cannot be easily ported to mobile devices for energy savings. Further, architectural differences between traditional client-server and mobile communications infrastructures make the use of existing caching technologies unsuitable in mobile devices. In this article, we propose a set of two new caching approaches specifically designed with the constraints of mobile devices in mind: (a) a response caching approach and (b) an object caching approach. Our experiments show that, even for a small cache size of 250MB, object caching can reduce energy consumption on average by 45% compared to the no-cache case, and response caching can reduce energy consumption by 20% compared to the no-cache case. The benefits increase with larger cache sizes. These results demonstrate the efficacy of our proposed method and raise the possibility of significantly extending mobile device battery life.

From D2D to Ds2D: Prolonging the Battery Life of Mobile Devices via Ds2D Communications

Emerging device-centric systems (DCSs) such as D2D communications are considered as a standard part of future mobile networks, where operators/ consumers involve the devices in direct communication to improve the cellular system throughput, latency, fairness, and energy efficiency. However, battery life of mobile devices involved in such communications is crucial for 5G smartphone users to explore the emerging applications in DCSs. It is anticipated that the owners of 5G-enabled smartphones will use their devices more extensively to talk, text, email, and surf the web more often than do customers with 4G smartphones or traditional handsets, which puts a significantly higher demand on the battery life. Smartphones are currently equipped with multiple radio interfaces that enable them to access different types of wireless networks including LTE-Direct and Wi-Fi-Direct, besides cellular networks. Such a capability is not well explored within the context of DCS. This article proposes a new scheme to support the emerging features in DCS where a D2D-enabled mobile device (a sink device or a file/ content requester) aggregates the radio resources of multiple mobile devices (source devices or file/content providers) via its multiple radio interfaces such that the scheme is referred to as devices-to-device (Ds2D) communications. The Ds2D communication scheme ensures an optimal packet split among the source mobile devices to improve the file/content transfer latency (FTL), energy efficiency, and battery life. Simulation results demonstrate that the proposed optimal packet split scheme among multiple source devices participating in Ds2D communication guarantees an improvement in mobile battery life over a wide range of data rate levels in comparison with the random packet split strategy and traditional D2D communication paradigm between the sink and source mobile devices.

Bluetooth 5 Energy Management through a Fuzzy-PSO Solution for Mobile Devices of Internet of Things

Energy efficiency is a fundamental requirement for a wireless protocol to be suitable for use within the Internet of Things. New technologies are emerging aiming at an energy-efficient communication. Among them, Bluetooth Low Energy is an appealing solution. Recently, the specifications of Bluetooth 5 have been presented with the purpose to offer significant enhancements compared to the earlier versions of the protocol. Bluetooth 5 comes with new communication modes that differ in range, speed, and energy consumption. This paper proposes a fuzzy-based solution to cope with the selection of the communication mode, among those introduced with Bluetooth 5, that allows the best energy efficiency. This communication mode, used by mobile devices, is dynamically regulated by varying the transmission power, returned as the output of a Fuzzy Logic Controller (FLC). A Particle Swarm Optimization (PSO) algorithm is presented to achieve the optimal parameters of the proposed FLC, i.e., optimizing the triangular membership functions, by varying their range, to reach the best results concerning the battery life of mobile devices. The proposed FLC is based on triangular membership functions because they represent a good trade-off between computation cost and efficiency. The paper presents a detailed description of the FLC design, a logical analysis of the PSO algorithm for the derivation of best performance conditions values, and experimental assessments, obtained through testbed scenarios.

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

AbstractMobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.

Offloading to Improve the Battery Life of Mobile Devices

Moving the responsibility for certain computation, communication, and storage operations away from a mobile device's main processor can help save power while simultaneously increasing performance. Here, the authors present an overview of three such offloading techniques. This department is part of a special issue on energy harvesting and power management.

Network Science Approach for Device Discovery in Mobile Device-to-Device Communications

With the emerging demands for local area services, mobile device-to-device (D2D) communications is conceived as a vital component for next-generation wireless networks to improve spectral reuse, to bring hop gains, and to enhance system capacity. In mobile D2D networks, energy-efficient probing schemes are vital to prolonging the limited battery life of mobile devices. Current solutions mainly focus on optimizing the probing interval during contacts by assumption of exponential distributed contact. However, studies in the area of network science have shown that the contact interval follows a power-law distribution, which indicates that the current solutions are not optimal while, on the other hand, dramatically increasing the difficulty of analyzing this problem. In this paper, we propose a network science approach for adaptive wakeup schedule based on power-law distributed contacts. Our approach requires nodes to stay asleep when a contact is unlikely to happen and to wake up only when the possibility that it successfully contacts another node is relatively high. By predicting node contacts in the future based on network science, our approach significantly reduces energy consumption without degrading the performance of opportunistic networks. Extensive simulations with real-life human and vehicular traces demonstrate the outstanding performance of our approach. The results show that our scheme saves 30% in energy while keeping the same performance in most scenarios, and it enhances the performance in terms of average delivery ratio and delivery delay by over 15%, compared with the existing best wakeup techniques, without considering network science.

A Comparative Study of Code Offloading Techniques and Application Partitioning Methods in Mobile Cloud Computing

Mobile cloud computing allows the execution of computation-intensive applications of mobile devices in computational clouds, and this process of executing in cloud by sending the application VM/Components is called application/code/component offloading. Offloading is an effective method to save the execution time and energy consumption of mobile devices. Thus it extends the battery life of mobile devices. Applications are first partitioned into offloadable and non-offloadable components, which are then transferred to remote server for execution. The objective of this paper is to explore the different techniques of offloading and application partitioning methods. These techniques are thoroughly reviewed in this paper. This paper also highlights the comparison of different techniques on the basis of their contribution, merits, demerits and also on the basis of improvement in execution time, energy consumption, communication time.

On-demand multimedia data broadcast in MIMO wireless networks

Given a set of multiple requests from clients equipped with M antennae and a wireless network of m channels, on-demand data broadcast requires to find an optimal schedule of broadcasting (placing) all requested data items of R on a set of channels C as evenly as possible under the constraint that each client may use at most M channels. Such a schedule is important for overcoming the shortcomings of wireless networks such as asymmetric bandwidth between uplink and downlink, and limited battery life of mobile devices. Existing schemes for data broadcast assume single-antenna clients and will result in significant bandwidth wastage and client’s data download time increase. To overcome these problems, we propose a novel approach for disseminating multimedia data in a MIMO wireless network by converting it to the multiprocessor scheduling problem where requests and antennae are regarded as tasks and processors respectively. We present three schemes (LFOS, BFOS and BBOS) under this approach: LFOS scheduling data items of largest sizes, BFOS adopting the best matching between data items and channels, and BBOS partitioning data items properly to balance the broadcast cycles of all channels. In comparison with the existing schemes based on single-antenna broadcast, our schemes improve access latency and channel bandwidth usage significantly. This has been verified through extensive experimental results.

An energy-efficient task scheduling for mobile devices based on cloud assistant

Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.

Guest Editorial: Special Section on Circuit and System Design Methodologies for Emerging Technologies

The demand for ever smaller, portable, energy-efficient and high-performance electronic systems has been the primary driver for CMOS technology scaling. As CMOS scaling approaches physical limits, it has been fraught with challenges that required introduction of newer materials, manufacturing processes and device structures. High- $\kappa$ oxide and metal-gate stack was introduced to mitigate oxide leakage which became a significant concern for sub-65nm CMOS technologies. Thin body, undoped channels, and multiple-gate structures were introduced to mitigate subthreshold leakage as battery life for mobile devices rose to prominence. 3D transistors such as double-gate FinFET and trigate transistors have been introduced to improve ON current and reduce subthreshold leakage without compromising layout efficiency that is crucial for device density which leads to lower costs. These innovations have allowed sustained scaling of CMOS technology which will continue to dominate semiconductor manufacturing for reasons that are both technological and financial. In CMOS technology, charge conveys logic information. Movement of charge is central to computation and storage, which entails a minimum energy dissipation of the order kB $\ast$ T per switching event. It has been argued that energy dissipation defines the fundamental limit of CMOS scaling.

Transform recipes for efficient cloud photo enhancement

Cloud image processing is often proposed as a solution to the limited computing power and battery life of mobile devices: it allows complex algorithms to run on powerful servers with virtually unlimited energy supply. Unfortunately, this overlooks the time and energy cost of uploading the input and downloading the output images. When transfer overhead is accounted for, processing images on a remote server becomes less attractive and many applications do not benefit from cloud offloading. We aim to change this in the case of image enhancements that preserve the overall content of an image. Our key insight is that, in this case, the server can compute and transmit a description of thetransformationfrom input to output, which we call atransform recipe.At equivalent quality, our recipes are much more compact than JPEG images: this reduces the client's download. Furthermore, recipes can be computed from highly compressed inputs which significantly reduces the data uploaded to the server. The client reconstructs a high-fidelity approximation of the output by applying the recipe to its local high-quality input. We demonstrate our results on 168 images and 10 image processing applications, showing that our recipes form a compact representation for a diverse set of image filters. With an equivalent transmission budget, they provide higher-quality results than JPEG-compressed input/output images, with a gain of the order of 10 dB in many cases. We demonstrate the utility of recipes on a mobile phone by profiling the energy consumption and latency for both local and cloud computation: a transform recipe-based pipeline runs 2--4x faster and uses 2--7x less energy than local or naive cloud computation.

A High-Efficiency Self-Oscillating Class-D Amplifier for Piezoelectric Speakers

The design tradeoffs of the class-D amplifier (CDA) for driving piezoelectric (PZ) speakers are presented, including efficiency, linearity, and electromagnetic interference. An implementation is proposed to achieve high efficiency in the CDA architecture for PZ speakers to extend battery life in mobile devices. A self-oscillating closed-loop architecture is used to obviate the need for a carrier signal generator to achieve low power consumption. The use of stacked-cascode CMOS transistors at the H-bridge output stage provides low-input capacitance to allow high-switching frequency to improve linearity with high efficiency. Moreover, the CDA monolithic implementation achieves 18 $V_{\rm PP}$ output voltage swing in a low-voltage CMOS technology without requiring expensive high-voltage semiconductor devices. The prototype experimental results achieved a minimum THD $+$ N of 0.025%, and a maximum efficiency of 96%. Compared to available CDA for PZ speakers, the proposed CDA achieved higher linearity, lower power consumption, and higher efficiency.

Analyzing the service availability of mobile cloud computing systems by fluid-flow approximation

Mobile cloud computing (MCC) has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods (such as Monte-Carlo simulation) may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are defined to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.

Effective and efficient neighbor detection for proximity-based mobile applications

We consider the problem of maximizing both effectiveness and efficiency of the detection of a device by another device in a mobile ad hoc network, given a maximum amount of time that they remain in the proximity of each other. Effectiveness refers to the degree to which the detection is successful, while efficiency refers to the degree to which the detection is energy saving. Our motivation lies in the emergence of a new trend of mobile applications known as proximity-based mobile applications which enable a user to communicate with other users in some defined range and for a certain amount of time. The highly dynamic nature of these applications makes neighbor detection time-constrained, i.e., even if a device remains in proximity for a limited amount of time, it should be detected with a high probability as a neighbor. In addition, the limited battery life of mobile devices requires the neighbor-detection to be performed by consuming as little energy as possible. To address this problem, we perform a realistic simulation-based study in mobile ad hoc networks and we consider three typical urban environments where proximity-based mobile applications are used, namely indoor with hard partitions, indoor with soft partitions and outdoor urban areas. In our study, a node periodically broadcasts a message in order to be detected as a neighbor. Thus, we study the effect of parameters that we believe could influence effectiveness and efficiency, i.e., the transmission power and the time interval between two consecutive broadcasts. Our results show that regardless of the environment, effectiveness and efficiency are in conflict with each other. Thus, we propose a metric that can be used to make good tradeoffs between effectiveness and efficiency.

Dynamic power management for embedded processors in system‐on‐chip designs

Dynamic power management (DPM), which exploits low-power states of the target device, has been a key research issue to overcome the limited battery life of mobile devices. For efficient power management, today's power management unit in a system-on-chip for mobile devices supports multiple low-power states for embedded processors. Unfortunately, the DPM policies implemented in modern operating systems are not appropriate for processors because they may not understand the idleness of the processor accurately. There may be significant performance degradation if the DPM policy module misunderstands that the processor is idle even when there are many interrupt requests to handle. A novel DPM scheme for embedded processors considering the system response time as well as power reduction is proposed. Experimental results show that the proposed DPM policy achieves performance improvement by up to 25% compared to a conventional DPM policy with a similar amount of power reduction.