Adaptive battery charge scheduling with bursty workloads (original) (raw)
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Many portable devices rely on batteries for their power supply. The capacity of the batteries is finite, and the duration with which one can use the device is limited by the battery lifetime. Accordingly, to increase the efficiency of these systems, energy consumption and also managing the use of the batteries are too important. Given the characteristics of the nonlinear behaviour of the battery, for maximizing battery life, which is related to the discharge pattern of batteries, is one of np-hard problems. This paper to extending the system lifetime and maximizing the efficiency of the battery, presents a greedy algorithm for dynamic voltage scaling according to battery and power consumption characteristics of the tasks. These tasks have deadline and should be done on the specific time. In order to test the proposed algorithm offered in this paper, we test it with three algorithms to compare the results. Simulation results show that the proposed method (gjtbs) in different conditions (with different workload of the system) maximized systems lifetime
An efficient on-demand charging schedule method in rechargeable sensor networks
Journal of Ambient Intelligence and Humanized Computing, 2020
Nowadays, wireless energy charging (WEC) is emerging as a promising technology for improving the lifetime of sensors in wireless rechargeable sensor networks (WRSNs). Using WEC, a mobile charger (MC) reliably supplies electric energy to the sensors. However, finding an efficient charging schedule for MC to charge the sensors is one of the most challenging issues. The charging schedule depends on remaining energy, geographical and temporal constraints, etc. Therefore, in this article, a novel efficient charging algorithm is proposed, such that the lifetime of the sensors in WRSN are increased. The proposed algorithm uses a multi-node MC that can charge multiple sensors at the same time. In this algorithm, the charging requests of the low-energy sensors are received by the MC. Then, a reduced number of visiting points are determined for the MC to visit them. The visiting points are within the charging range of one or more requesting sensors. Thereafter, an efficient charging schedule is determined using an adaptive fuzzy model. Sugeno-fizzy inference method (S-FIS) is being used as a fuzzy model. It takes remaining energy, node density, and distance to MC, as network inputs for making real-time decisions while scheduling. Through simulation experiments, it is finally shown that the proposed scheme has higher charging performance comparing to base-line charging schemes in terms of survival ratio, energy utilization efficiency, and average charging latency. In addition, ANOVA tests are conducted to verify the reported results.
Maximizing system lifetime by battery scheduling
2009
Abstract The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.
Scheduling battery usage in mobile systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2003
The use of multibattery power supplies is becoming common practice in electronic appliances of the latest generations. Economical and manufacturing constraints are at the basis of this choice. Unfortunately, a partitioned battery subsystem is not able to deliver the same amount of charge as a monolithic battery with the same total capacity. In this paper, we define the concept of battery scheduling, we investigate several policies for solving the problem of optimal charge delivery, and we study the relationship of such policies with different configurations of the battery subsystem. Experimental results, obtained for different kinds of current workloads, demonstrate that the choice of the proper scheduling can make system lifetime as close as 1% of the theoretical upper bound, that is, a monolithic power supply of equal capacity.
Extending lifetime of portable systems by battery scheduling
2001
Multi-battery power supplies are b ecoming popular in electronic appliances of the latest generations, due to economical and manufacturing constraints. Unfortunately, a partitioned b attery subsystem is not able to deliver the same amount of charge as a monolithic battery with the same total capacity. In this paper, we de ne the concept of battery scheduling, we investigate policies for solving the problem of optimal charge delivery, and we study the relationship of such policies with di erent con gurations of the battery subsystem. Results, obtained for di erent workloads, demonstrate that the choice of the proper scheduling can make, in the best case, system lifetime as close as 1% of that guaranteed by a monolithic battery of equal capacity.
Computing optimal schedules of battery usage in embedded systems
2010
Abstract The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.
Energy management for battery-powered embedded systems
ACM Transactions on Embedded Computing Systems, 2003
Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems. In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery. This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.
Computing Optimal Schedules for battery Usage in Embedded Systems
IEEE Transactions on Industrial Informatics, 2010
The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as non-linear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.
2017
Recently, many studies have investigated scheduling mobile devices to recharge and collect data from sensors in wireless rechargeable sensor networks (WRSNs) such that the network lifetime is prolonged. In reality, because mobile devices are more powerful and expensive than sensors, the cost of the mobile devices often consumes a high portion of the budget. Due to a limited budget, the number of mobile devices is often limited. Some sensors in WRSNs may not be operated without time limits due to the limited number of mobile devices. Therefore, sensors in a sensing field must be weighted by their importance; that is, the more important an area covered by a sensor, the higher the weight of the sensor. Therefore, in this paper, the problem of scheduling limited mobile devices for energy replenishment and data collection in WRSNs with multiple sinks such that the total weight of the recharged sensors that can be operated without time limits is maximized, termed the Periodic Energy Reple...
Assessing the Impact of Sensor-based Task Scheduling on Battery Lifetime in IoT Devices
IEEE Transactions on Instrumentation and Measurement, 2021
A well-known system-level strategy to reduce the energy consumption of microprocessors or microcontrollers is to organize the scheduling of the executed tasks so that it is aware of the main battery non-idealities. In the IoT domain, devices rely on simpler microcontrollers; workloads are less rich and, batteries are typically sized to guarantee lifetimes of more extensive orders of magnitude (e.g., days, as opposed to hours). Load current magnitudes in these IoT devices are therefore relatively small compared to other more powerful devices, and they hardly trigger the conditions that emphasize the battery non-idealities. In this work, we carry out a measurement-based assessment about whether task scheduling is really relevant to extend the lifetime of IoT devices. We run experiments both on a physical commercial IoT device hosting four sensors, an MCU, and a wireless radio, as well as on a "synthetic" device emulated with a programmable load generator. We used both secondary lithium-ion and primary alkaline batteries to explore the impact of battery chemistries further. Results show that the impact of different schedules is essentially irrelevant, with a maximum difference of only 3.98% in battery lifetime between the best and worst schedules.