• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.4
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• pp.85-94
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• 2015

Recently, research on the smart grid that combines ICT(Information & Communication technology) to the power system has been actively progressed. If the occupancy of the EV(Electric vehicle) is increased. the V2G(Vehicle to grid) system is available which constitutes the micro-grid through battery of EV. V2G system performs load leveling and efficient energy consumption by battery operation considering load condition. But, if the battery is used only depending on the electricity rates, it doses not consider the life of the battery. The ACC(Achievable cycle) and the total transferable energy of battery varies corresponding to the selected DOD(Depth of discharge). In this paper, the optimal DOD selection method of V2G system considering battery wear cost and average driving distance of EV. Also, the total revenue prediction of various nation is presented considering the actual electricity costs per hour.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.129-138
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• 2017

Faced with the era of the IoT (Internet of Things) and smart homes, this study aims to explore the type of information loaded on smart devices that can lead to consumer's efficient energy use. 105 Americans participated in the survey with eight different versions according to two energy consumption levels (Above or Below condition) by four information frames (Finance, security, environment, or health). It was found that frames can make significant differences in consumers' attitudes; (1) Those in the Below condition worried about environments more than those in the Above condition; (2) Finance-framed information in the Above condition was the least effective to increase consumers' energy saving motivation; (3) In the Below condition, those receiving finance and security framed information revealed more environmental concerns than those receiving other types of informations. This study can contribute to the field by providing with basic research findings that smart device developers can refer to in the future. Also, follow-up studies need to be conducted to examine effective messages for Korean energy consumers.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.4
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• pp.330-335
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• 2017

Increased fossil fuel consumption causes global warming, environmental pollution, and abnormal climate change. Wind-generated power installation is proposed to solve this problem. Recently, the wind power plant construction case encourages the installation of the energy storage system (ESS) to improve the intermittency of wind power. The maximized ESS operation profits connected to wind power are not generated in the simplest operation pattern of charging at night and discharging at day. The battery charging efficiency improvement should be considered to get more profits. Thus, there is a possibility of increasing ESS operation profits by analyzing the battery AC and DC charging/discharging efficiency and the yearly average sealed maintenance free (SMP) in hours. In this paper, the battery impedance characteristic, AC and DC charging/discharging efficiency, and the yearly average SMP are analyzed. The operation scenario to improve the ESS battery charging efficiency connected to wind power is proposed and verified via simulation.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.169-177
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• 2024

Industrial Wireless Sensor Network (IWSN) is a key feature of Industrial IoT that enables industrial automation through process monitoring and control by connecting industrial equipment such as sensors, robots, and machines wirelessly, and must support the strict requirements of modern industrial environments such as real-time, reliability, and energy efficiency. To achieve these goals, IWSN uses reliable communication methods such as multipath routing, fixed redundant resource allocation, and non-contention-based scheduling. However, the issue of wasting redundant resources that are not utilized for communication degrades not only the efficiency of limited radio resources but also the energy efficiency. In this paper, we propose a scheme that utilizes reinforcement learning in communication scheduling to periodically identify unused wireless resources and reallocate them to save energy consumption of the entire industrial network. The experimental performance evaluation shows that the proposed approach achieves about 30% improvement of resource efficiency in scheduling compared to the existing method while supporting high reliability. In addition, the energy efficiency and latency are improbed by more than 21% and 38%, respectively, by reducing unnecessary communication.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.322-345
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• 2018

Climate change has become a major challenge for sustainable development of human society. This study is an attempt to analyze existing literature to identify economic indicators that hamper the process of global warming. This paper includes case studies based on various countries to examine the nexus for environment and its relationship with Foreign Direct Investment, transportation, economic growth and energy consumption. Furthermore, the observations are analyzed from the perspective of China-Pakistan Economic Corridor (CPEC) and probable impact on carbon emission of Pakistan. A major portion of CPEC investment is allocated for transportation. However, it is evident that transportation sector is substantial emitter of carbon dioxide (CO2) gas. Unfortunately, there is no empirical work on the subject of CPEC and carbon emission for vehicular transportation. This paper infers that empirical results from various other countries are ambiguous and inconclusive. Moreover, the evidence for the pollution haven hypothesis and the halo effect hypothesis is limited in general and inapplicable for CPEC in particular. The major contribution of this study is the proposal of an energy efficient transportation model for reducing CO2 emission. In the end, the paper suggests strategies to climate researchers and policymakers for adaptation and mitigation of greenhouse gases (GHG).

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.39-48
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• 2013

A cloud system has to deal with highly variable workloads resulting from dynamic usage patterns in order to keep the QoS within the predefined SLA. Aside from the aspects regarding services, another emerging concern is to keep the energy consumption at a minimum. This requires the cloud providers to consider energy and performance trade-off when allocating virtualized resources in cloud data centers. In this paper, we propose a resource provisioning approach based on dynamic thresholds to detect the workload level of the host machines. The VM selection policy uses utilization data to choose a VM for migration, while the VM allocation policy designates VMs to a host based on its service reputation. We evaluated our work through simulations and results show that our work outperforms non-power aware methods that don't support migration as well as those based on static thresholds and random selection policy.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.6
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• pp.749-762
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• 2019

In recent years, in urban areas, underground of roads are being promoted in order to resolve traffic congestion and to secure green spaces, and due to the low ratio of large vehicles, they are planned or constructed as road tunnels for small cars only. In addition, the tunnels being built in the city is a tendency to be enlarged to play the role of main roads. Accordingly, the capacity of the ventilation system is increasing and various ventilation methods are required, and the importance of maintenance after the completion of the tunnel such as the operating cost of the ventilation system is emphasized. Therefore, the need for optimization of the operation stage for reducing the power consumption of the ventilation system and the study of the ventilation system operation control logic is increasing. In this study, the study on the necessity of the optimization of operation stage and control logic of the ventilation system was carried out to realize the energy-saving operation for the small car only passing through tunnels which is applied of ① jet fan and combination ventilation system (② jet fan + air purifying equipment, ③ jet fan + vertical shaft, ④ jet fan + supply air semi-transverse). As a result of this study, there can be various operating combinations in the case of the combined ventilation system, and even though the amount of ventilation air is the same, the operating power varies greatly according to the operating combinations. It was found that operating the axial fan first rather than the jet fan first operation method has an effect on power saving.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.313-320
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• 2019

Nowadays, as the availability of tiny, low-cost microcomputer increases at a high level, mobile robots are experiencing remarkable enhancements in hardware design, software performance, and connectivity advancements. In order to control Turtlebot 2, several algorithms have been developed using the Robot Operating System(ROS). However, ROS requires to be run on a high-cost computer which increases the hardware cost and the power consumption to the robot. Therefore, design an algorithm based on low-cost hardware is the most innovative way to reduce the unnecessary costs of the hardware, to increase the performance, and to decrease the power consumed by the computer on the robot. In this paper, we present a path-finding algorithm for TurtleBot 2 based on low-cost hardware. We implemented the algorithm using Raspberry pi, Windows 10 IoT core, and RPLIDAR A2. Firstly, we used Raspberry pi as the alternative to the computer employed to handle ROS and to control the robot. Raspberry pi has the advantages of reducing the hardware cost and the energy consumed by the computer on the robot. Secondly, using RPLIDAR A2 and Windows 10 IoT core which is running on Raspberry pi, we implemented the path-finding algorithm which allows TurtleBot 2 to navigate from the starting point to the destination using the map of the area. In addition, we used C# and Universal Windows Platform to implement the proposed algorithm.