• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1423-1435
• /
• 2010

Building wireless sensor networks requires a constituting sensor node to consider the following limited hardware resources: a small battery lifetime limiting available power supply for the sensor node, a low-power microprocessor with a low-performance computing capability, and scarce memory resources. Despite such limited hardware resources of the sensor node, the sensor node platform needs to activate real-time sensing, guarantee the real-time processing of sensing data, and exchange data between individual sensor nodes concurrently. Therefore, in this paper, we propose an energy-efficient real-time task scheduling technique for battery-powered wireless sensor nodes. The proposed energy-efficient task scheduling technique controls the microprocessor's operating frequency and reduces the power consumption of a task by exploiting the slack time of the task when the actual execution time of the task can be less than its worst case execution time. The outcomes from experiments showed that the proposed scheduling technique yielded efficient performance in terms of guaranteeing the completion of real-time tasks within their deadlines and aiming to provide low power consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.2821-2839
• /
• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.3
• /
• pp.1-10
• /
• 2010

As processor platforms are continuously moving toward wireless mobile systems, embedded mobile processors are expected to perform more and more powerful, and therefore the development of an efficient power management algorithm for these battery-operated mobile and handheld systems has become a critical challenge. It is well known that a memory system is a main performance limiter in the processor point of view. Although many DVFS studies have been considered for the efficient utilization of limited battery resources, recent works do not explicitly show the interaction between the processor and the memory. In this research, to properly reflect short/long-term memory access patterns of the embedded workloads in wireless mobile processors, we propose a memory buffer utilization as a new index of DVFS level prediction. The simulation results show that our solution provides 5.86% energy saving compared to the existing DVFS policy in case of memory intensive applications, and it provides 3.60% energy saving on average.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.7
• /
• pp.454-461
• /
• 2007

Portable embedded systems have recently become smaller in size and offer a variety of junctions for users. These systems require high performance processors to handle the many functions and also a small battery to fit inside the system. However, due to its size, the battery life has become a major issue. It is important to have both efficient power design and management for each function, while optimizing processor voltage and clock frequency in order to extend the battery life of the system. In this paper, we calculated the efficiency of power in optimizing power rail. This system has two microprocessors. One is used to play music and movie files while the other is for DMB. In order to reduce power consumption, the DMB microprocessor is turned of while music or videos are played. Lastly, DVFS is applied to the processor in the system to reduce power consumption. Experimental results of the implemented system have resulted in reduced power consumption.