• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.326-333
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• 2008

In wireless sensor networks, a sensor node with in-network aggregation adjusts the timeout which is a waiting time to receive messages from child nodes. This paper proposes a novel timeout scheduling scheme for data aggregation in wireless sensor networks, which adaptively configures its timeout according to changing data patterns in order to improve energy efficiency and data accuracy during data aggregation. The proposed scheme decreases the timeout when the temporal difference of collected data(data variation) from children is lower than a pre-defined threshold because there is no occurrence of critical events. Conversely, it increases the timeout when the data variation is higher than the pre-defined threshold in order to fulfill more accurate data aggregation. Extensive simulation reveals that the proposed scheme outperforms the cascading-based scheme in terms of energy consumption and data accuracy.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.933-937
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• 2009

This paper proposes a dynamic timeout scheduling for energy efficient and accurate aggregation by analyzing the single hop delay in wireless sensor networks based on IEEE 802.15.4. The proposed scheme dynamically configures the timeout value depending on both the number of nodes sharing a channel and the type of wireless media, with considering the results of delay analysis of the single hop delay. The timeout of proposed scheme is much smaller than the maximum single hop delay which is used as the timeout of traditional data aggregation schemes. Therefore the proposed scheme considerably reduces the energy consumption of idle monitoring for waiting messages. Also, the proposed scheme maintains the data accuracy by guaranteeing the reception ratio required by the sensor network applications. Extensive simulation has revealed that proposed scheme enhances energy consumption by 30% with maintaining data accuracy, as compared with the TAG data aggregation.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.765-768
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• 2006

센서 네트워크에서 데이터 병합 시 타임아웃 스케줄링은 각 센서노드가 자식노드로부터 메시지를 받기 위해 기다려야하는 시간을 조절하는 것이다. 본 논문에서는 데이터 패턴의 변화에 따라 타임아웃을 적응적으로 조절하는 새로운 타임아웃 스케줄링 기법인 ATS-DA(Adaptive Timeout Scheduling for Data Aggregation)를 제안한다. ATS-DA는 자식노드로부터 수신한 데이터가 특별한 이벤트 없이 기준 데이터 변동률 범위 내에 있게 되면 타임아웃 값을 줄인다. 타임아웃을 작게 하면 파워 소모가 감소하고 전송 지연도 짧아진다. 센싱 데이터가 기준 데이터 변동률을 벗어나면 보다 정확한 데이터 병합 값을 얻기 위해 타임아웃 값을 늘린다. 시뮬레이션 결과에서 제안하는 ATS-DA 기법이 기존 캐스케이딩 타임아웃 스케줄링 기법에 비해 데이터 정확도와 파워 소모 측면에서 우수한 성능을 보여주었다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.387-388
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• 2012

최근 무선 멀티미디어 센서 네트워크(WMSN : Wireless Multimedia Sensor Network)에서 비디오 영상을 전송하는데 있어 그 특성을 고려하지 않은 전송 방법으로 인하여 영상 정보에 대한 신뢰도가 낮다는 문제점이 있다. 본 논문은 WMSN에서 비디오 프레임의 구성요소인 I, P, B 픽처의 특성에 따라 타임아웃을 동적으로 설정하여 노드와 노드간의 패킷 수신율을 증가시켜 전체 서비스의 QoS를 제공하고, 이벤트를 감지하여 특정 구간의 영상 정보를 제공받을 수 있는 기법을 제안한다.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.19-24
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• 2024

Recent advances in large-scale data processing technologies such as big data, cloud computing, and artificial intelligence have increased the demand for high-performance storage devices in data centers and enterprise environments. In particular, the fast data response speed of storage devices is a key factor that determines the overall system performance. Solid state drives (SSDs) based on the Non-Volatile Memory Express (NVMe) interface are gaining traction, but new bottlenecks are emerging in the process of handling large data input and output requests from multiple hosts simultaneously. SSDs typically process host requests by sequentially stacking them in an internal queue. When long transfer length requests are processed first, shorter requests wait longer, increasing the average response time. To solve this problem, data transfer timeout and data partitioning methods have been proposed, but they do not provide a fundamental solution. In this paper, we propose a dual queue based scheduling scheme (DQBS), which manages the data transfer order based on the request order in one queue and the transfer length in the other queue. Then, the request time and transmission length are comprehensively considered to determine the efficient data transmission order. This enables the balanced processing of long and short requests, thus reducing the overall average response time. The simulation results show that the proposed method outperforms the existing sequential processing method. This study presents a scheduling technique that maximizes data transfer efficiency in a high-performance SSD environment, which is expected to contribute to the development of next-generation high-performance storage systems

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1295-1304
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• 2010

This paper proposes a fault-tolerant data aggregation which provides energy efficient and reliable data collection in wireless sensor networks. The traditional aggregation scheme does not provide the countermeasure to packet loss or the countermeasure scheme requires a large amount of energy. The proposed scheme applies caching and re-transmission based on the track topology to the adaptive timeout scheduling. The proposed scheme uses a single-path routing based on the traditional tree topology at normal, which reduces the dissipated energy in sensor nodes without any countermeasure against packet loss. The proposed scheme, however, retransmits the lost packet using track topology under event occurrences in order to fulfill more accurate data aggregation. Extensive simulation work under various workloads has revealed that the proposed scheme decrease by 8% in terms of the dissipated energy and enhances data accuracy 41% when the potential of event occurrence exists as compared with TAG data aggregation. And the proposed scheme decrease by 53% in terms of the dissipated energy and shows a similar performance in data accuracy when the potential of event occurrence exists as compared with PERLA data aggregation.