• Journal of Software Engineering Society
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• v.26 no.3
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• pp.53-62
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• 2013

Along with increasing supplies of smart devices, a proliferation of context-aware applications is came. However, acquiring contexts through sensors requires considerable energy consumption. It has became big constraints on running many context-aware applications in mobile devices having limited battery capacity. Hence, energy-efficient methods for monitoring contexts are highly required. In this paper, we propose four context monitoring methods, analyse energy consumption in each method, and provide guidelines for applying the methods. It is effective to decrease energy consumption for monitoring contexts with applying the methods. To assess the proposed methods, we implement an application that is aware of a user's motion and show quantitative comparison between each of the methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.6 s.336
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• pp.23-32
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• 2005

This paper proposes a new algorithm for the energy constraint ad-hoc network which efficiently spread the energy usage over the network through the backup route scheme in order to increase the network lifetime. Recently, the various energy-efficient routing algorithms based on On-demanding method are proposed. Among them, PSR(Power-aware Source Routing) increased the network lifetime through the periodical route alternation depended on the use of the battery while DSR(Dynamice Source Routing) uses only the route selected during the route discovery phase. But PSR has a problem that it increases the route overhead because of the frequent flooding for the route alternation. For solving this problem, we propose HPSR(Hierarchical Power-aware Source Routing) which uses the backup route set during the route discovery in order to alternation the route without the flooding. HPSR increases the network lifetime due to the frequent route alternation using backup route while it decreases the routing overhead due to the reduced flooding. In this paper, we also prove the performance of HPSR through the simulation using OPNET.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.75-84
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• 2004

Geocasting, a variant of the conventional multicasting problem, is one of communication type in which the data packets are delivered to a group of all nodes within a specified geographical region (i.e., the geocasting region) and is called location-based multicasting(LBM)(l). An Ad-hoc network is a dynamically reconfigurable and temporary wireless network where all mobile devices using batteries as energy resources cooperatively maintain network connectivity without central administration or the assistance of base stations. Consequently, the technique to efficiently consume the limited amounts of energy resources is an important problem so that the system lifetime is maximized. In this paper, we propose a LBPA(Location-Based Power Aware) geocasting algorithm that selects energy-aware neighbor to route a packet towards the target region In Ad-hoc network environments. The method Is such that the energy consumption is balanced among the nodes in proportion to their energy reserves. Through the simulations, the proposed LBPA algorithm shows better results, that is, as good as 40% on the average over the conventional LBM algorithm in terms of the network lifetime.

• Journal of Communications and Networks
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• v.17 no.1
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• pp.47-57
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• 2015

With the ever growing demand of data applications, the joint macro-relay networks are emerging as a promising heterogeneous deployment to provide coverage extension and throughput enhancement. However, the current cellular networks are usually designed to be performance-oriented without enough considerations on the traffic variation, causing substantial energy waste. In this paper, we consider a joint macro-relay network with densely deployed relay stations (RSs), where the traffic load varies in both time and spatial domains. An energy-efficient scheme is proposed to dynamically adjust the RS working modes (active or sleeping) according to the traffic variations, which is called traffic-aware relay sleep control (TRSC). To evaluate the performance of TRSC,we establish an analytical model using stochastic geometry theory and derive explicit expressions of coverage probability, mean achievable rate and network energy efficiency (NEE). Simulation results demonstrate that the derived analytic results are reasonable and the proposed TRSC can significantly improve the NEE when the network traffic varies dynamically.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7B
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• pp.721-727
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• 2009

Wireless Sensor Networks(WSN) make use of low cost and energy constrained sensor nodes. Thus, reaching the successful execution of its tasks with low energy consumption is one of the most important issues. The limitation of existing hierarchical algorithms is that many times the data are transmitted to the opposite direction to the sink. In this paper, DDACM (Data Direction Aware Clustering Method) is proposed. In this method, the nearest node to the sink is elected as cluster head, and when its energy level reaches a threshold value, the cluster head is reelected. We also make a comparison with LEACH showing how this method can reduce the energy consumption minimizing the reverse direction data transmission.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.9
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• pp.279-292
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• 2014

Energy aware server cluster aims to reduce power consumption at maximum while keeping QoS(quality of service) as much as energy non-aware server cluster. In the existing methods of energy aware server cluster, they calculate the minimum number of active servers needed to handle current user requests and control server power mode in a fixed time interval to make only the needed servers ON. When loads change rapidly, QoS of the existing methods become degraded because they cannot increase the number of active servers so quickly. To solve this QoS problem, we classify load change situations into five types of rapid growth, growth, normal, decline, and rapid decline, and apply five different thresholds respectively in calculating the number of active servers. Also, we use a flexible scheme to adjust the above classification criterion for multi threshold, considering not only load change but also the remaining capacity of servers to handle user requests. We performed experiments with a cluster of 15 servers. A special benchmarking tool called SPECweb was used to generate load patterns with rapid change. Experimental results showed that QoS of the proposed method is improved up to the level of energy non-aware server cluster and power consumption is reduced up to about 50 percent, depending on the load pattern.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.339-345
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• 2013

This paper addresses the system lifetime maximization algorithm in multi-hop sensor network system. A multi-hop sensor network consists of many battery-driven sensor nodes that collaborate with each other to gather, process, and communicate information using wireless communications. As sensor-driven applications become increasingly integrated into our lives, we propose a energy-aware scheme where each sensor node transmits informative data with adaptive data rate to minimize system energy consumption. We show the optimal data rate to maximize the system lifetime in terms of remaining system energy. Furthermore, the proposed algorithm experimentally shows longer system lifetime in comparison with greedy algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.949-966
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• 2013

Recently, Wireless Body Area Network (WBAN) has promise to revolutionize human daily life. The need for multiple sensors and constant monitoring lead these systems to be energy hungry and expensive with short operating lifetimes. In this paper, we offer a review of existing work of WBAN and focus on energy-aware management in it. We emphasize that nodes computation, wireless communication, topology deployment and energy scavenging are main domains for making a long-lived WBAN. We study the popular power management technique Dynamic Voltage and Frequency Scaling (DVFS) and identify the impact of slack time in Dynamic Power Management (DPM), and finally propose an enhanced dynamic power management method to schedule scaled jobs at slack time with the goal of saving energy and keeping system reliability. Theoretical and experimental evaluations exhibit the effectiveness and efficiency of the proposed method.

• Journal of information and communication convergence engineering
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• v.14 no.3
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• pp.137-146
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• 2016

In this paper, we introduce a quality of experience (QoE)-provisioning mobility management scheme. The emphasis is on a mobility-aware QoE solution enabling network components to recognize the mobility pattern of an end-user and to prepare a handover in advance. We further focus on an energy-adaptive QoE solution based on the energy profile providing the preferred pattern of energy consumption and an energy preference check engine determining whether the provision of the service that the end-user requested is suitable to QoE or not. Lastly, we concentrate on a network-based intelligent mobility management scheme adopting the calm service and the balance. Consequently, we conclude that the proposed schemes improve the handover latency, QoE metrics, and energy efficiency simultaneously.