• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.69-75
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• 2022

We propose energy-efficient scheduling considering real-time constraints and energy efficiency in smart mobile with heterogeneous multi-core structure. Recently, high-performance applications such as VR, AR, and 3D game require real-time and high-level processings. The big.LITTLE architecture is applied to smart mobiles devices for high performance and high energy efficiency. However, there is a problem that the energy saving effect is reduced because LITTLE cores are not properly utilized. This paper proposes a heterogeneous multi-core assignment technique that improves real-time performance and high energy efficiency with big.LITTLE architecture. Our proposed method optimizes the energy consumption and the execution time by predicting the actual task execution time using SVM (Support Vector Machine). Experiments on an off-the-shelf smartphone show that the proposed method reduces energy consumption while ensuring the similar execution time to legacy schemes.

• ETRI Journal
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• v.35 no.4
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• pp.677-686
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• 2013

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.1
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• pp.29-35
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• 2004

Locomotive scheduling in railway systems experiences many difficulties because of the complex interrelations among resources, knowledge and various constraints. Artificial intelligence technology has been applied to solve these scheduling problems. These technologies have proved to be efficient in representing knowledge and rules for complex scheduling problems. In this paper, we have applied the CSP (Constraints Satisfaction Problems) programming technique, one of the AI techniques, to solve the problems associated with locomotive scheduling. This method is more effective at solving complex scheduling problems than available mathematical programming techniques. The advanced locomotive scheduling system using the CSP programming technique is realized based on the actual timetable of the Saemaul type train on the Kyong-bu line. In this paper, an overview of the CSP programming technique is described, the modeling of domain and constraints is represented and the experimental results are compared with the real-world existing schedule. It is verified that the scheduling results by CSP programming are superior to existing scheduling performed by human experts. The executing time for locomotive scheduling is remarkably reduced to within several decade seconds, something requiring several days in the case of locomotive scheduling by human experts.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.1-10
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• 2014

The proposed scheme schedules given real-time tasks so that energy consumption of multicore processors would be minimized while meeting tasks' deadline and tolerating a permanent fault based on the primary-backup task model. Whereas the previous methods minimize the overlapped time of a primary task and its backup task, the proposed scheme maximizes the overlapped time so as to decrease the core speed as much as possible. It is analytically verified that the proposed scheme minimizes the energy consumption. Also, the proposed scheme saves up to 77% energy consumption of the previous method through experimental performance evaluation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.831-840
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• 1992

This paper presents an efficient short-term generation scheduling method using a rule-based expert/consulting system approach to assist electric energy system operators and planners. The expert system approach is applied to improve the Dynamic Programming(DP) based generation scheduling algorithm. In the selection procedure of the feasible combinations of generating units at each stage, automatic consulting on the manipulation of several constraints such as the minimum up time, the minimum down time and the maximum running time constraints of generating units will be performed by the expert/consulting system. In order to maximize the solution feasibility, the aforementioned constraints are controlled by a rule-based expert system, that is, instead of imposing penalty cost to those constraint violated combinations, which sometimes may become the very reason of no existing solution, several constraints will be manipulated within their flexibilities using the rules and facts that are established by domain experts. In this paper, for the purpose of implementing the consulting of several constraints during the dynamic process of generation scheduling, an expert system named STGSCS is developed. As a building tool of the expert system, C Language Integrated Production System(CLIPS) is used. The effectiveness of the proposed algorithm has been demonstrated by applying it to a model electric energy system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.11-20
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• 2011

In this paper, we propose an optimal scheduling scheme that minimizes the energy consumption of a real-time video task on the multi-core platform supporting dynamic voltage and frequency scaling. Exploiting parallel execution on multiple cores for less energy consumption, the propose scheme allocates an appropriate number of cores to the task execution, turns off the power of unused cores, and assigns the lowest clock frequency meeting the deadline. Our experiments show that the proposed scheme saves a significant amount of energy, up to 67% and 89% of energy consumed by two previous methods that execute the task on a single core and on all cores respectively.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.1-9
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• 2015

Recently, cognitive radio technology has been spotlighted for the efficient use of limited frequency technology. The most important part of CR is to protect the incumbent user's communications. Therefore, CR user should be sensing the currently using channel and another hopping channel for sudden appearance of the Incumbent User. In this paper, we propose the cooperative channel sensing to protect the IU considering each CR user's remained power and sensing-zone, sense the spectrum bands in a fairly distributed manner and share the results among the users within respective sensing zone. Sensing scheme including inband sensing and outband sensing is utilized. The inband sensing is in charge of current using channel scanning while the outband sensing mainly cares about other channels. The performance results by computer simulations show that our sensing scheduling scheme reduces the number of sensing nodes and saves energy need to channel sensing.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.501-510
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• 2010

Wireless sensor networks consist of sensor nodes which are expected to be battery-powered and are hard to replace or recharge. Thus, reducing the energy consumption of sensor nodes is an important design consideration in wireless sensor networks. For the implementation of an energy-efficient MAC protocol, a Sensor-MAC based on the IEEE 802.11 protocol, which has energy efficient scheduling, has been proposed. In this paper, we propose a Dynamic S-MAC that adapts dynamically to the network-traffic state. The dynamic S-MAC protocol improves the energy consumption of the S-MAC by changing the frame length according to the network-traffic state. Using an NS-2 Simulator, we compare the performance of the Dynamic S-MAC with that of the S-MAC protocol.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.962-970
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• 2009

This paper presents a data gathering and scheduling scheme for wireless sensor networks. We use a data gathering tree for sending the data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, a time tree is built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possible different activation rate. Through the simulation, we found that the proposed scheme that uses time trees shows better characteristics in energy and data arrival rate when compared with other schemes such as SMAC and DMAC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1849-1876
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• 2022

Wireless Sensor Network (WSN) is considered as an integral part of the Internet of Things (IoT) for collecting real-time data from the site having many applications in industry 4.0 and smart cities. The task of nodes is to sense the environment and send the relevant information over the internet. Though this task seems very straightforward but it is vulnerable to certain issues like energy consumption, delay, throughput, etc. To efficiently address these issues, this work develops a cross-layer model for the optimization between MAC and the Network layer of the OSI model for WSN. A high value of duty cycle for nodes is selected to control the delay and further enhances data transmission reliability. A node measurement prediction system based on the Kalman filter has been introduced, which uses the constraint based on covariance value to decide the scheduling scheme of the nodes. The concept of duty cycle for node scheduling is employed with a greedy data forwarding scheme. The proposed Duty Cycle-based Greedy Routing (DCGR) scheme aims to minimize the hop count, thereby mitigating the energy consumption rate. The proposed algorithm is tested using a real-world wastewater treatment dataset. The proposed method marks an 87.5% increase in the energy efficiency and reduction in the network latency by 61% when validated with other similar pre-existing schemes.