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Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

Lately, unmanned aerial vehicles (UAVs) communications acquired great attention because of its weighty new applications, particularly in rescue services. In such a case, access and gateway UAVS are spread to cover and fully support communications over disaster areas where the ground network is malfunctioned or wholly damaged. Each access UAV collects essential information from its assigned area, then flies and transfers it to the nearby gateway UAVs that deliver this collected information to the closest operating ground network. Meanwhile, collisions may occur as two or more access UAVs might target the same gateway UAV. This paper leverages and modifies two multi-armed bandit (MAB) based algorithms, namely, Kullback Leibler upper confidence bound (KLUCB) and minimax optimal stochastic strategy (MOSS) to formulate the gateway UAV selection issue. The issue is modeled as a budget-constrained multiagent MAB (MA-MAB) that maximizes data rates while considering access UAVs’ flight battery consumption. Hence, MA battery aware KLUCB (MABA-KLUCB) and battery aware MOSS (MA-BA-MOSS) algorithms are proposed for efficient gateway UAV selection. The proposed MAB algorithms maximize the UAV network’s total sum rate over the conventional selection techniques with assuring good convergence performance.

Service Discovery (RD) and finding Resource Directory (RD) for services in Internet of Things (IoT) is one of the fundamental tasks in providing services for constrained devices in IoT networks. Providing these services faces serious problems in the power consumption and the increasing number of devices in this kind of networks. Many approaches and efforts have n\been made to address the problem of power consumption and when tested on a large number of nodes these approaches faced serious problems. To handle this problem, this paper proposes an approach with implementation to increasing the number of devices in an Internet of Things (IoT) providing scalable approach and at the same time keeps the network lifetime with minimal affect. The experiment shows that the proposed scalable approach improves the network lifetime with increasing the number of nodes.

A typical wireless sensor network (WSN) is used to monitor or sense an area for events. Sensing-area coverage of a WSN is the collective sensing area of all the active nodes in the WSN. However, there might be a possibility of multiple active nodes monitoring the same area. This would result in energy wastage. Further, the sudden failure of nodes may result in coverage gaps within the sensing area. We propose Optimized Discharge-Curve-based Coverage Protocol (ODCP) to handle these problems. ODCP determines optimal sleep schedules for redundant nodes using their neighboring active nodes’ battery discharge rate, failure probability, and coverage overlap information. ODCP is simulated extensively using various scenarios of WSNs. The simulation results show that ODCP provides energy-efficient coverage as compared to other existing coverage protocols.

Given the relentless growing number of mobile devices, researchers have pointed out that distributed computing environments, such as clusters or even computational Grids, could increase the available resources by scavenging devices’ capabilities. However, this negatively impacts on their batteries. We study centralized job schedulers that aim at exploiting clusters of mobile resources with rather stable devices in terms of connection time. These schedulers outperform a previous centralized scheduler, finishing up to 30% more jobs with the same amount of energy. We also show that the schedulers perform competitively compared to schedulers based on job stealing, which are more difficult to implement and consume more network resources and hence energy. Fil: Hirsch Jofré, Matías Eberardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina

The market of small drones has been recently increasing due to their use in many fields of application. The most popular drones are multirotors, in particular quadcopters. They are usually supplied with batteries of limited capacity, and for this reason their total flight time is also limited.As a consequence of the non linear characteristics of batteries, estimation of the real flight time may become an issue, since most battery models do not include all the non idealities. Consequently, applications such as delivery service, search and rescue, surveillance might not be accomplished correctly because of inaccurate energy estimations.This paper describes a battery-aware model for an accurate analysis of the drone energy consumption; this model is then applied to a scenario of drone delivery. Results show an accuracy greater of about 16% with respect to the traditional estimation model.

Lifetime maximization is a key challenge in battery-powered multi-sensor devices. Battery-aware power management strategies combine task scheduling with dynamic voltage scaling (DVS), accounting for the fact that the power drawn by the device is different from that provided by the battery due to its many non-idealities. However, state-of-the-art techniques in this field do not take into account several important aspects, such as the impact of sensing tasks on the overall power demand, the (operating point dependent) losses due to multiple DC-DC conversions, and the dynamic modifications in battery efficiency caused by different distributions of the currents in the temporal and in the frequency domains. In this work, we propose a novel approach to identify optimal power management solutions, that addresses all these limitations. Specifically, using advanced battery and DC-DC converter models, we propose methods to explore the scheduling space both statically (at design time) and dynamically (at runtime), accounting not only for computation tasks, but also for communication and sensing. With this method, we show that the battery lifetime can be increased by as much as 23.36% if an optimal power management strategy is adopted.

One of the unique features of mobile applications is location awareness. Mobile users take their devices with them everywhere which increases the availability of persons' traces. Extracting and analyzing knowledge from these traces represent a strong support for several application domains, ranging from traffic management to advertisement and social studies. We present a novel approach to online recognition of users' outdoor activities without depleting mobile resources. We associate the places visited by individuals during their movements to meaningful human activities using a novel algorithm that incrementally clusters a user's moves into different types of activities. To optimize battery consumption, the algorithm behaves variably according to users' behaviours and the remaining battery level. Studies using real GPS records from a spatio-temporal region demonstrate that the proposal is effective and is capable of inferring human activities without draining the phone resources.

Battery lifetime is a critical design parameter in the operation of mobile computing devices. Maximizing the battery lifetime is a particularly difficult problem due to the nonlinearity of the battery discharge behavior and its dependence on the discharge profile. In this paper, we address the problem how battery aware dynamic voltage scaling policy optimizes the battery capacity consumption. We propose the procedure of slack distribution optimization to reduce the battery capacity consumption by distributing the slack optimally for the schedules generated by the existing battery aware DVS policies, and analysis the properties of the procedure. The experiment results show that the proposed procedure can further safe battery capacity consumption evidently.

There are several significant ways in which the ubiquity of battery-powered devices impacts the field of collaborative computing. First, applications such as collaborative data gathering become pos...

To minimize battery consumption for portable devices, the prescheduling policy of battery-aware scheduling was improved by optimizing slack distribution. A battery-aware compound task scheduling (BACTS) algorithm considering various aspects including task deadline, current and execution time was proposed and evaluated with the previously prevailing earliest deadline first (EDF) algorithm. The results indicate the proposed BACTS algorithm manages to figure out a feasible schedule (if available) in battery-aware task scheduling even for disorganized connected task graphs beyond the solving ability of EDF. Its schedule achieves better performance with lower charge consumption after prescheduling, and also lower or equal optimum charge consumption after voltage scaling.

Dynamic voltage scaling (DVS) is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy (OPS II) balancing slack supply and demand for DVS is proposed. An OPS II-based slack-nibbling overall planning strategy (SNOPS) algorithm is also proposed, which iteratively nibbles slacks for appropriate tasks selected by an overall planning dynamic priority function to perform DVS until the slack is exhausted and an optimum voltage setting is obtained. For a high-load task set, SNOPS manages to recover battery overload while maintaining schedulability. For random variable-load task sets, SNOPS achieves a saving of 29.51% battery capacity on average, the suboptimal gap is 27.84% narrower than that of our previously proposed OPS-based algorithm, and 92.10% narrower than that of the algorithm proposed by Chowdhury et al. Results indicate that OPS II manages to save battery to various extents while maintaining schedulability, and demonstrates good load compatibility and close-to-optimal performance on average.

Recently, Electric Vehicles (EVs) have been considered as new paradigm of transportation in order to solve environmental concerns, e.g. air pollution. However, EVs pose new challenges regarding their Battery LifeTime (BLT), energy consumption, and energy costs related to battery charging. The EV power consumption may be estimated by having the route information and the EV specifications. Also, by having the battery characteristics, the battery capacity consumption and the BLT may be estimated for each route. In this paper, we propose a driving management which uses the above-mentioned information in order to optimize the driving route by being aware of the EV energy consumption, energy cost, and BLT. Our proposed driving management extends the BLT by 16.8% and reduces the energy consumption by 11.9% and energy cost by 12.6% on average, by selecting the optimized route instead of the fastest route.

Coarse-grained Reconfigurable Architecture (CGRA) is a promising mobile computing platform that provides both high performance and high energy efficiency. Since loop nests are usually mapped onto CGRA for acceleration, optimizing the mapping is an important goal for design of CGRAs. Moreover, how to reduce energy consumption also becomes one of primary concerns in using CGRAs. This paper makes three contributions: a) Proposing an energy consumption model for CGRA; b) Formulating loop nests mapping problem to minimize the battery charge loss; c) Extract an efficient heuristic algorithm called BPMap. Experiment results show that our methods improve the performance of the kernels and lower the energy consumption.

Getting maximum battery life is not just a matter of writing applications for low average power consumption. You can take advantage of power effects to design systems that last longer on one charge. This work has shown that the performance of the text-to-speech engine can be optimized by judicious choice over factors such as word-cluster length. The approach demonstrated using this text-to-speech system can be easily extended to other applications for low-power, mobile embedded systems.

With the proliferation of portable battery-operated devices, smaller, lighter, more powerful, and longer-lasting battery is a much sought-after commodity. Powering laptops, handhelds, cell phones, pagers, watches, medical devices, and many other modern gadgets, batteries play a crucial role in supporting today’s cutting-edge technologies. The widening gap between the increasing power consumption and power density of the popular battery technologies is highlighted to emphasize the relevance of battery-aware systems. Battery-aware synthesis approaches try to make efficient use of the available energy in the battery. So optimization of the battery lifetime, i.e., performing maximum amount of computation per recharge of battery, is one of the primary objectives for portable computing system design. An overview of commonly used battery technologies is provided, and the characteristics of a rechargeable battery are specified. The underlying process of battery discharge is explained. Realizations of battery-driven systems including battery-aware sensor network are presented.

Wireless systems such as satellites and sensor networks are often battery-powered. To operate optimally they must therefore take the performance properties of real batteries into account. Additionally, these systems, and therefore their batteries, are often exposed to loads with uncertain timings. Mixed criticality and soft real-time systems may accept deadline violations and therefore enable trade-offs and evaluation of performance by criteria such as the number of tasks that can be completed with a given battery. We model a task set in combination with the kinetic battery model as a stochastic hybrid system and study its performance under battery-aware scheduling strategies. We believe that this evaluation does not scale with current verification techniques for stochastic hybrid systems. Instead statistical model checking provides a viable alternative with statistical guarantees. Based on our model we also calculate an upper bound on the attainable number of task instances from a battery, and we provide a battery-aware scheduler that wastes no energy on instances that are not guaranteed to make their deadlines.

We describe early results from a program of experiments to characterize battery operation in the WSN regime.

Two IT-system control methods, which realize efficient battery usage for battery-powered IT systems targeting developing countries such as India, are proposed. The proposed methods control the IT equipment and cooling power collaboratively on the basis of a forecast of power outage duration. To quantitatively evaluate these methods, power outages in Bangalore, India, were measured. The proposed methods were evaluated by using this measured power outage data. According to the evaluation results, the proposed methods can improve a measure of battery efficiency, namely, IT-used-energy (Qt )/battery-used-energy (Qu ), by 39% compared to that of conventional IT systems.

This paper proposes a battery-aware wireless video transmission where video coding bitrate is intentionally lowered than transmission channel bitrate. Battery lifetime significantly improves by shutting down transceiver and giving idle time to battery, at the cost of image quality degradation. Measurement shows effective battery lifetime increases to 2∼3 times at the PSNR degradation of 0.25 dB.

Extending the lifetime of battery is a very important goal in battery-operated systems. The prior works of lifetime management mainly focused on single battery, multi-battery scheduling as another way of prolonging the battery lifetime is considered here. Battery Aware Tasks Allocating Algorithm (BATA) for multi-battery operated system is proposed in this paper. It extends the battery lifetime through making fully use of the idle time of battery scheduling which can reduce the charge loss since the recovery effect of battery. Experimental results indicate that BATA can achieve 15.67%∼ 25.43% reductions on the charge loss and 30.68%∼55.63% gains of battery lifetime while maintains a low level computation complexity.

Clustering allows for data aggregation which reduces congestion and energy consumption. Recent study in battery technology reveals that batteries tend to discharge more power than needed and reimburse the over-discharged power if they are recovered. In this paper, we first provide an online mathematical battery model suitable for implementation in sensor networks. Using our battery model, we propose a new Battery Aware Reliable Clustering algorithm for WSNs (BARC). BARC incorporates many features which are missing in many other clustering algorithms. It rotates cluster heads according to a battery recovery scheme and it also incorporates a trust factor for selecting cluster heads thus increasing reliability. Most importantly, our proposed algorithm relaxes many of the rigid assumptions that the other algorithms impose such as the ability of the cluster head to communicate directly with the base station and having a fixed communication radius for intra-cluster communication. BARC uses Z-MAC which has several advantages over other MAC protocols. Simulation results show that using BARC prolongs the network lifetime greatly in comparison to other clustering techniques.

Since the lifetime of a battery directly impacts the lifetime of sensor networks, one of the key considerations in the design of sensor networks is the ability to maximize battery lifetime. In this paper, we present (1) a task modeling methodology and (2) a battery-aware real-time task scheduling technique for sensor networks. The task modeling is achieved based on task classification in terms of the usage of resources on a micro-sensor system. For exploiting the recovery effect of battery, the battery-aware task scheduling algorithm composed of three phases is designed to maximize the lifetime of a battery while meeting the timing constraint of each task.

A real-time, static scheduling algorithm for battery life optimization has been presented in this paper. The work aims to extend existing battery aware scheduling techniques. It differs from previous work as it takes into consideration many aspects of battery recovery that were not considered previously. This allows for further enhancement of the system battery life by up to 29.41%. The scheduling algorithm achieves a total battery life extension of up to 37.7%.

The advent of multi-core embedded processors has brought along new challenges for embedded system design. This paper presents an efficient, battery aware, code partitioning technique for a text to speech system, which is executed on a multi-core embedded processor. The system achieves significant performance improvements both in terms of execution time as well as battery lifetimes. The mentioned technique provides a new paradigm for battery aware embedded system design which can be easily extended to other applications.

Optimization of battery energy consumption by variable voltage task scheduling technique is studied in the paper. An efficient slack time allocation policy is firstly derived by comprehensive analysis on non-linear characteristics of battery. In further, two heuristic BATS (battery aware task scheduling) algorithms are proposed. The former takes the task discharge current as the priority function for slack time allocation, hence can reduce the peak power efficiently, while the later takes the task discharge current weighted by execution time as the priority function, hence can optimize the battery energy consumption more efficiently. Experiment results show that the proposed algorithms can greatly reduce the battery energy consumption by more than 30% and the peak power of the system simultaneously

In this paper we present a simple yet efficient methodology for battery-aware task execution on FPGAs in portable Reconfigurable Computing (RC) platforms. We divide the reconfigurable area on an FPGA into several fixed reconfigurable slots called Configurable Tiles. We then schedule real-time tasks onto these tiles. Various schedules using different number of tiles are calculated off-line. These schedules along with their execution times are then sent to a run-time scheduler which dynamically decides, which schedule is the most battery efficient. By varying the number of tiles used for scheduling tasks, we can vary the battery usage and lifetime. We tested the methodology by running it on several different task graph structures and sizes, and report an average of 14% and as high as 21%, less battery capacity used, as compared to non-optimal execution. Finally, we present a case study where we implement a real-time face recognition algorithm on the iPACE-V1 [6] platform using the proposed methodology and observed 1.3 to 3.3 times improvement in battery life-time.