The capability of energy harvesting application powered by indoor photovoltaic energy is severely affected by dynamic light environments. Accordingly, accurate understanding of the target environment and deploying energy harvesting sensors is practically very hard. In this article, we propose HarvAR, which manages photovoltaic energy harvesting sensors with mobile augmented reality (AR)-empowered techniques. HarvAR utilizes the error-prone RGBD data of mobile device to construct a digital twin (DT), performing depth error compensation and estimating the optical properties of the target space. Using the DT, the proposed system predicts the harvesting capability with low overhead, and recommends adequate locations for installing or relocating harvesting sensors. We implemented the HarvAR system and evaluated its accuracy and efficiency in three indoor environments. Our experiments show that DT configuration and harvesting prediction can be performed in minutes, compared to over 10 h using existing techniques, and harvesting prediction is provided with less than 20% error.

Duty cycle management is critical for the energy-neutral operation of batteryless devices. Many efforts have been made to develop an effective duty cycling method, including machine-learning-based approaches, but existing methods can barely handle the dynamic harvesting environments of batteryless devices. Specifically, most machine-learning-based methods require the harvesting patterns to be collected in advance, as well as manual configuration of the duty-cycle boundaries. In this article, we propose a configuration-free duty cycling scheme for batteryless devices, called CTRL, with which energy harvesting nodes tune the duty cycle themselves adapting to the surrounding environment without user intervention. This approach combines reinforcement learning (RL) with a control system to allow the learning algorithm to explore all possible search space automatically. The learning algorithm sets the target State of Charge (SoC) of the energy storage, instead of explicitly setting the target task frequency at a given time. The control system then satisfies the target SoC by controlling the duty cycle. An evaluation based on the real implementation of the system using publicly available trace data shows that CTRL outperforms state-of-the-art approaches, resulting in 40% less frequent power failures in energy-scarce environments while achieving more than ten times the task frequency in energy-rich environments.

Transiently-powered devices rely solely on energy harvesters. Such devices typically use capacitors to store the harvested energy, but recent systems employ supercapacitors to store energy for extended operations. In a supercapacitor-based transiently-powered system, the energy-efficient estimation of the state-of-charge (SoC) of a supercapacitor is critical for practical use of the system, due to its tight energy budget. Conventional voltage-based schemes for capacitors do not provide accuracy in SoC estimation for supercapacitors. Also, the supercapacitor-specific SoC estimation which exploits its charge redistribution characteristics provides an accuracy, but the scheme demands significant overhead, and thus, is not applicable to transiently-powered systems, such as wireless sensor nodes. In this article, we specify three requirements for estimating supercapacitor SoC that should be met to function in transiently-powered systems. We then propose a scheme that meets those requirements. The proposed scheme does not require additional hardware, has a low computation cost, and operates when the system is intermittently powered. Thus, the scheme fits the energy-efficient operation of transiently-powered systems. We implemented the proposed scheme in real hardware and evaluated its functionality and accuracy. The proposed scheme estimated the SoC of supercapacitors with high accuracy, in various configurations and use scenarios, while guaranteeing operations in a typical energy harvesting environment.

Games are energy-intensive applications on mobile devices. Optimizing the energy efficiency of games is hence critical for battery-limited mobile devices. Although the advent of energy-aware scheduling (EAS) integrated in recent devices has provided opportunities for improved energy management, the framework is not specifically tuned for game applications. In this paper, we aim to improve the energy efficiency of game applications running on EAS-enabled mobile devices. To this end, we first analyze the functional characteristics of games, and investigate the source of the energy inefficiency. We then propose a scheme, called System-level Energy-optimization for Game Applications (SEGA), to improve the energy efficiency of games. SEGA governs CPU and GPU power consumption in a tightly coupled manner by employing three key techniques: (1) Lsync-aware GPU DVFS governor, (2) adaptive capacity clamping, and (3) on-demand touch boosting. We implemented SEGA on the latest Android-based smartphones. The evaluation results for 23 popular games showed that SEGA reduced the energy consumption of the Google Pixel 2 XL and Samsung Galaxy S9 Plus smartphones, at the device level, by 6.1–22.3 and 4.0–11.7 percent, respectively, with a quality of service (QoS) degradation of 1.1 and 0.5 percent, on average.

Predicting a click sequence in mobile applications improves the user experience in various ways. By predicting which button will be clicked next, one can predict how the application will work and how the device will operate. However, predicting the click sequence is difficult because of the problems involved in collecting click sequences in real application usage. More importantly, accurate predictions are extremely challenging. In this paper, we address these issues. We propose PathFinder, a scheme for collecting click events and based on them predicting the next click in the application. The clicks are collected with the Android Accessibility Service and the next click is predicted via long shortterm memory (LSTM). For the prediction, the base click sequence model is first generated from all users' data; then, a personalized model is trained with an individual click sequence. As training data considerably influences the performance of LSTM, several techniques are developed to enhance the quality of the training data. The experimental results for 100 popular applications showed that the coverage and accuracy of click sequence tracing were 95% and 96%, respectively. Furthermore, PathFinder predicted the top three buttons that would be clicked next with a 0.76 F-measure for 1775043 real click data.

Managing the power consumption of display-related components in mobile devices is difficult because of performance degradation. Therefore, eliminating hidden workloads, such as redundant frames, is preferable, as it directly reduces power without affecting the user experience. Our preliminary study shows that the default launcher of the Android Open Source Project (AOSP) and popular applications, such as Instagram and Pinterest, generate redundant frames. In this paper, we propose a scheme to optimize the power consumption of the smartphone's display-related components by preventing redundant frames generation. By analyzing the frame-generation process, we observe that redundant frame generation is possible in the current Android framework. We then propose a scheme that recognizes and prevents redundant frame generation before actual frame generation (i.e., frame rendering in the GPU). The proposed scheme utilizes a display list, which was introduced in recent Android smartphones for efficient frame generation. We implemented the proposed scheme on Nexus smartphones. On the Nexus 5, the proposed solution reduced the energy of the AOSP default launcher, Instagram, and Pinterest by 40, 35.4, and 39.6 percent, respectively. Furthermore, the experimental results with a general usage scenario showed that our scheme prevented about 35 percent of redundant frame generation with a false-positive rate of 1.8 percent.

With the increasing popularity of smartphones, battery life is among the most crucial issues for mobile users. This paper presents a crowdsensing-based use guide to extend the lifetime of smartphones. The system answers a question raised by phone usage: Why is my phone battery draining quickly compared to others phones despite running the same applications? The proposed system pinpoints the major causes of battery drain in terms of both hardware and software aspects. In relation to the hardware aspect, the system quantifies degree of battery aging as a ratio metric; an estimate of 50% indicates that the battery is at half of full capacity, meaning that battery usage time is approximately half that of a new battery. The system automatically profiles battery age based on charging duration data collected by crowdsensing. In its software aspect, the system guides phone configuration to extend application usage times. The system mines large-scale usage data to infer the major energy holes in a user's phone usage. The scheme works autonomously without user intervention and does not require any external equipment. Extensive evaluation with 3,000 users demonstrated that the proposed scheme successfully extends battery life for typical mobile users.

Many smart home applications, such as monitoring for the elderly and home automation, require location information for individual occupants. Several techniques have been proposed for tracking occupants in a home environment. However, the current techniques do not provide a seamless in-home locating system owing to the occupants' device-free movement and the lack of cost-effective infrastructure for home location tracking. In this paper, we propose a home occupant tracking system that uses a smartphone and an off-the-shelf smartwatch without additional infrastructure. In our system, activity fingerprints are automatically generated from the microphone and the inertial sensors of the smartwatch, and location information is periodically obtained from the smartphone. We designed a hidden Markov model using the relationship between home activities and the room's location. Extensive experiments showed that our system tracks the location of users with 87% accuracy, even when there is no manual training for activities.

The goal of energy monitoring and eco-feedback systems is to induce energy consumers to change their behaviors to achieve a more sustainable way of life. Comprehensive research and commercial solutions provide energy consumers with information about overall energy costs or appliance-level energy usage. However, in order to better promote spontaneous energy saving with an eco-feedback system, personalized information is required. The conventional solutions cannot identify the energy usage of an individual user in a shared residential environment. In this paper, we propose the Personalized Energy Auditor, which estimates personal energy usage at home. Our system monitors and analyzes appliance usage, as well as the energy cost of the daily activities of residents. The system then estimates personal energy usage automatically, by linking appliance usage data with the individual user. Our system was installed in residential homes, and the experimental results indicate that it accurately estimates personal energy usage.

Reducing the standby power used by home appliances is critical in a household energy management system. Although significant effort has been made to minimize the standby power use of appliances, manual operation is still required to eliminate standby power usage. Additionally, the current regulation strategy of standby power typically focuses on real-power consumption, and it does not consider the apparent power and power factors. We propose an automatic standby power reduction system that is based on user-context profiling. Our system profiles and analyzes the occupancy pattern, as well as the appliance usage. The system then actively manages standby power utilization by predicting the probabilities of future appliance usage. We built a prototype smart meter to monitor and control the power lines. We also developed software that implements the proposed scheme. Our experiments, conducted for three to five weeks in four households, show that power consumption in standby mode can be reduced.

Available techniques for indoor object locating systems, such as inertial sensor-based system or radio fingerprinting, hardly satisfy both cost-effectiveness and accuracy. In particular, inertial sensor-based locating systems are often supplemented with radio signals to improve localization accuracy. A radio-assisted localization system is still costly due to the infrastructure requirements and management overheads. In this paper, we propose a low-cost and yet accurate indoor pedestrian localization scheme with a small number of radio beacons whose location information is unknown. Our scheme applies the Simultaneous Location and Mapping (SLAM) technique used in robotics to mobile device, which is equipped with both inertial sensors and the IEEE802.15.4a Chirp Spread Spectrum (CSS) radio, to obtain accurate locations of pedestrians in indoor environment. The proposed system is validated with real implementations. The experiment results show approximately 1.5 m mean error observed during 276 m of pedestrian moving in a 380 m2indoor environment with five position-unknown beacons.

Abstract Network reprogramming is a process used to update program codes of sensor nodes that are already deployed. To deal with potentially unstable link conditions of wireless sensor networks, the epidemic approach based on 3‐way advertise‐request‐data handshaking is preferred for network reprogramming. Existing epidemic protocols, however, require a long completion period and high traffic overhead in high‐density networks, mainly due to the hidden terminal problem. In this paper, we address this problem by dynamically adjusting the frequency of advertisement messages in terms of the density of sensor nodes, which is the number of sensor nodes in a certain area. We compare the performance of the proposed scheme, called DANP (Density‐Adaptive Network Reprogramming Protocol), with a well‐known epidemic protocol, Deluge. Simulations indicate that, in the grid topologies, DANP outperforms Deluge by about 30% in terms of the completion time and about 50% in terms of the traffic overhead. Significant performance gain is observed in random topologies as well. The performance of DANP is further confirmed via measurements in an experimental test bed. Copyright © 2009 John Wiley & Sons, Ltd.

Recently the demand on high graphic ability has increased in handheld systems due to multimedia or game application. Conventional DVS (dynamic voltage scaling) methods focus on reducing energy consumption of CPU. They didn't consider the influence of multimedia accelerators to make DVS policies. From the preliminary experiment, we found that the conventional DVS algorithm is not optimal to a system with a multimedia accelerator in context of reducing energy consumption of the system. We propose a power management mechanism that guarantees both QoS and reduction of energy consumption by considering the relation between the frequencies of the CPU and the frequencies of the multimedia accelerator. The proposed mechanism is a DVS technique in context of multimedia accelerator. We experimented the proposed mechanism on a testbed equipped with Intel XScale processor and Intel 2700 G multimedia accelerator. The experimental result shows that the proposed method reduces energy consumption of the system as much as by 33% compared to conventional CPU-only DVS algorithms.

Abstract To support Quality of service (QoS)‐sensitive applications like real‐time video streaming in IEEE 802.11 networks, a MAC layer extension for QoS, IEEE 802.11e, has been recently ratified as a standard. This MAC layer solution, however, addresses only the issue of prioritized access to the wireless medium and leaves such issues as QoS guarantee and admission control to the traffic control systems at the higher layers. This paper presents an IP‐layer traffic control system for IEEE 802.11 networks based on available bandwidth estimation. We build an analytical model for estimating the available bandwidth by extending an existing throughput computation model, and implement a traffic control system that provides QoS guarantees and admission control by utilizing the estimated available bandwidth information. We have conducted extensive performance evaluation of the proposed scheme via both simulations and measurements in the real test‐bed. The experiment results show that our estimation model and traffic control system work accurately and effectively in various network load conditions without IEEE 802.11e. The presence of IEEE 802.11e will allow even more efficient QoS provision, as the proposed scheme and the MAC layer QoS support will complement each other. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract In recent years, there has been wide‐spread use of large and high‐resolution liquid‐crystal displays (LCDs) on handheld devices. The portion of LCD power consumption in the overall system has gradually increased. While most of the previous research on LCD power management has focused on the hardware level, practical mechanisms at the software level are hardly known. This paper presents a power‐aware LCD management mechanism, based on dynamic refresh‐rate scaling and frame buffer monitoring. The proposed mechanism guarantees the display quality of service, which is inherently specified by content types. The mechanism does not require additional hardware or modifications to applications. The experiment results—on a commercial PDA with a $320\times240$ resolution—show that the proposed mechanisms effectively reduce the power consumption by up to 10%, while satisfying the display quality requirements for the LCD screen. Copyright © 2006 John Wiley & Sons, Ltd.

A popular web site is simultaneously approached by many clients and therefore it is vital to schedule the connections efficiently in or- der to improve the system performance. SRPT (Shortest Remaining Pro- cessing Time first) improves the response time of each connection. The new feature of HTTP/1.1 is to enable the clients to request multiple inlined- documents in a HTML document. When multiple inlined-documents are requested per connection, the response time of each inlined-document may be improved by the connection scheduling based on SRPT. It cannot, how- ever, guarantee that the response time of each connection with multiple inlined-documents is improved. In this paper we propose a window-based connection scheduling technique which provides a better response time in HTTP/1.1. The experimental results show that the performance with the proposed approach is improved.

Cache memory is used in almost all computer systems today to bridge the ever increasing speed gap between the processor and main memory. However, its use in multitasking computer systems introduces additional preemption delay due to the reloading of memory blocks that are replaced during preemption. This cache-related preemption delay poses a serious problem in realtime computing systems where predictability is of utmost importance. We propose an enhanced technique for analyzing and thus bounding the cache-related preemption delay in fixed-priority preemptive scheduling focusing on instruction caching. The proposed technique improves upon previous techniques in two important ways. First, the technique takes into account the relationship between a preempted task and the set of tasks that execute during the preemption when calculating the cache-related preemption delay. Second, the technique considers the phasing of tasks to eliminate many infeasible task interactions. These two features are expressed as constraints of a linear programming problem whose solution gives a guaranteed upper bound on the cache-related preemption delay. This paper also compares the proposed technique with previous techniques using randomly generated task sets. The results show that the improvement on the worst-case response time prediction by the proposed technique over previous techniques ranges between 5 percent and 18 percent depending on the cache refill time when the task set utilization is 0.6. The results also show that as the cache refill time increases, the improvement increases, which indicates that accurate prediction of cache-related preemption delay by the proposed technique becomes increasingly important if the current trend of widening speed gap between the processor and main memory continues.

To predict the worst case execution time (WCET) of real-time tasks, we should consider various factors (e.g., caching, pipelining, and infeasible paths) that affect the accuracy of the prediction. However some of them are inherently difficult to analyze statically, and thus may give rise to significant overestimation in WCET prediction. Therefore, for more accurate WCET prediction, we need to identify, such overestimation sources and analyze how much each of them can make overestimation. Then, such analysis results can be used to refine existing timing analysis techniques. In this paper we do not propose any new timing analysis techniques, but present quantitative analysis results on the impacts of overestimation sources on the accuracy of the worst case timing analysis. For this purpose, we use variance analysis based on a simulation-based methodology to make our analysis independent of any existing techniques. The results show that the dominant factor is pipelining analysis when the cache miss penalty is small and instruction caching analysis when the cache miss penalty is larger than 10 cycles. The results also show that although the impact of data caching analysis is small compared with that of pipelining or instruction caching analysis, if we ignore its effect in the WCET estimation, the WCET can be overestimated up to 275% even when the effects of the other factors are completely analyzed. Finally, the results show that the effects of infeasible paths are largely orthogonal to other analysis features and depend on program characteristics. Also, as for data caching, if infeasible paths are ignored in the WCET estimation, the accuracy of the WCET estimation is degraded significantly (up to 564%).

We propose a technique for analyzing cache-related preemption delays of tasks that cause unpredictable variation in task execution time in the context of fixed-priority preemptive scheduling. The proposed technique consists of two steps. The first step performs a per-task analysis to estimate cache-related preemption cost for each execution point in a given task. The second step computes the worst case response time of each task that includes the cache-related preemption delay using a response time equation and a linear programming technique. This step takes as its input the preemption cost information of tasks obtained in the first step. This paper also compares the proposed approach with previous approaches. The results show that the proposed approach gives a prediction of the worst case cache-related preemption delay that is up to 60 percent tighter than those obtained from the previous approaches.

We propose an enhanced technique for analyzing, and thus bounding cache related preemption delay in fixed priority preemptive scheduling focusing on instruction caching. The proposed technique improves upon previous techniques in two important ways. First, the technique takes into account the relationship between a preempted task and the set of tasks that execute during the preemption when calculating the cache related preemption delay. Second, the technique considers phasing of tasks to eliminate many infeasible task interactions. These two features are expressed as constraints of a linear programming problem whose solution gives a guaranteed upper bound on the cache related preemption delay. The paper also compares the proposed technique with previous techniques. The results show that the proposed technique gives up to 60% tighter prediction of the worst case response time than the previous techniques.

We propose a technique for analyzing cache-related preemption delays of tasks that cause unpredictable variation in task execution time in the context of fixed-priority preemptive scheduling. The proposed technique consists of two steps. The first step performs a per-task analysis to estimate cache-related preemption cost for each execution point in a given task from the number of useful cache blocks at the execution point. The second step computes the worst case response time of each task using a response time equation and a linear programming technique which takes as its input the preemption cost information of tasks obtained in the first step. Our experimental results show that the proposed technique gives a prediction of the worst case cache-related preemption delay that is up to 60% tighter than that obtained from previous approaches.

In multiprocessor and distributed real-time systems, scheduling jobs dynamically on processors is likely to achieve better performance. However, analytical and efficient validation methods to determine whether all the timing constraints are met do not exist for systems using modern dynamic scheduling strategies, and exhaustive simulation and testing are unreliable and expensive. This paper describes several worst-case bounds and efficient algorithms for validating systems in which jobs have arbitrary timing constraints and variable execution times and are scheduled on processors dynamically in a priority-driven manner. The special cases of the validation problem considered here are concerned with independent jobs that are (1) preemptable and migratable, or (2) preemptable and nonmigratable, or (3) nonpreemptable.>

PERTS is a prototyping environment for real-time systems. It contains schedulers and resource access protocols for time-critical applications, together with a comprehensive set of tools for the analysis, validation, and evaluation of real-time systems built on the scheduling paradigms supported by these building blocks. This paper describes the underlying models of real-time systems supported by PERTS, as well as its capabilities and intended use. A key component is the schedulability analyzer. The basic version of this system of tools supports the validation and evaluation of real-time systems built on the framework of the periodic-task model. This system of tools is now available.>