• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1B
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• pp.10-15
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• 2009

Recently, there are tendency that wireless sensor network is one of the important techniques for the future IT industry and thereby application areas in it are getting growing. Researches based on the hierarchical network topology are evaluated in good at energy efficiency in related protocols for wireless sensor network. LEACH is the best well known routing protocol for the hierarchical topology. However, there are problems in the range of message broadcasting, which should be expand into the overall network coverage, in LEACH related protocols. Thereby, this paper proposes a new clustering scheme to solve the co-shared problems in them. The basic idea of our scheme is using the inherent memory restrictions in sensor nodes. The results show that the proposed scheme could support the load balancing by distributing the clusters with a reasonable number of member nodes and thereby the network life time would be extended in about 1.8 times longer than LEACH.

• Smart Media Journal
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• v.8 no.1
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• pp.43-50
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• 2019

As numerous devices are connected through a wireless network, there exist many studies conducted to efficiently connect the devices. While earlier studies often use clustering for efficient device management, there is a load-intensive cluster node which may lead the entire network to be unstable. In order to solve this problem, we propose a scheduling model for centralized unequal chain clustering for efficient management of sensor nodes. For the cluster configuration, this study is based on the cluster head range and the distance to the base station(BS). The main vector projection technique is used to construct clustering with concentricity where the positions of the base stations are not the same. We utilize a multiple radio access interface, multiple-input multiple-output (MIMO), for data transmission. Experiments show that cluster head energy consumption is reduced and network lifetime is improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.758-760
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• 2012

The wireless sensor network is used to get information that location tracing or data of surrounding areas. Unnecessary retransmission request or many energy consumption because the transmission over the wireless links. In order to select the link of reliable and energy efficient to estimate the quality of radio link technique is required using RSSI, LQI, and so on. In this paper, each path between the sensor nodes, a small in the path within standard deviation of shall be determined the priority. Each path a high priority of the node values, respectively LQI is accumulated. Node can be selected the high LQI value path. Among them the less hop count to select the path is proposed. The proposed algorithm is removed the paths of shorten life using high the LQI value of the entire and high hop count even less variation. So its advantage that the sensor nodes can be selected more reliable path.

• Journal of KIISE:Databases
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• v.36 no.2
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• pp.99-111
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• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed. data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.413-424
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• 2009

Wireless sensor network is based on an event driven system. Sensor nodes collect the events in surrounding environment and the sensing data are relayed into a sink node. In particular, when events are detected, the data sensing periods are likely to be shorter to get the more correct information. However, this operation causes the traffic congestion on the sensor nodes located in a routing path. Since the traffic congestion generates the data queue overflows in sensor nodes, the important information about events could be missed. In addition, since the battery energy of sensor nodes exhausts quickly for treating the traffic congestion, the entire lifetime of wireless sensor networks would be abbreviated. In this paper, a new congestion control method is proposed on the basis of genetic algorithm. To apply genetic algorithm, the data traffic rate of each sensor node is utilized as a chromosome structure. The fitness function of genetic algorithm is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets, the proposed method selects the optimal data forwarding sensor nodes for relieving the traffic congestion. In experiments, when compared with other methods to handle the traffic congestion, the proposed method shows the efficient data transmissions due to much less queue overflows and supports the fair data transmission between all sensor nodes as possible. This result not only enhances the reliability of data transmission but also distributes the energy consumptions across the network. It contributes directly to the extension of total lifetime of wireless sensor networks.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.561-565
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• 2010

Many methods have been researched to prolong sensor network lifetime using mobile technologies. In the mobile sink research, there are the track based methods and the anchor points based methods as representative operation methods for mobile sinks. However, the existing methods decrease Quality of Service (QoS) and lead the routing hotspot in the vicinity of the mobile sink. The reason is that they use static mobile paths that are not concerned about the network environments such as the query position and the data priority. In this paper, we propose the novel mobile sink operation method that solves the problems of the existing methods. In our method, the probing priority of the mobile sink is determined with the data priorities for increasing the QoS and the mobile features are used for reducing the routing hotspot. The experimental results show that the proposed method reduces query response time and improves network lifetime over the existing methods.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.168-177
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• 2008

The code propagation is one of the most important technic for software update in wireless sensor networks. This paper presents a new scheme for code propagation using network coding. The proposed code propagation method roughly shows 20$\sim$25% performance improvement according to network environments in terms of the number of data exchange compared with the previously proposed pipelining scheme. As a result, we can efficiently perform the software update from the viewpoint of speed, energy, and network congestion when the proposed code propagation system is applied. In addition, the proposed system solves the overhearing problems of network coding such as the loss of original messages and decoding error using the predefined message. Therefore, our system allows a software update system to exchange reliable data in wireless sensor networks.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.163-168
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• 2007

무선 모바일 환경에서 통신 장비의 에너지와 전송 대역폭의 제한을 해결하기 위하여 방송기법을 사용한다. 기존의 방송 스케줄링 방법들은 고정된 데이터 요청 빈도를 바탕으로 방송 스트림을 구성하기 때문에 사용자의 요구가 동적으로 변화하는 모바일 환경에서 활용하는 것은 비효율적이다. 또한 사용자의 우선순위를 고려하지 않음으로써 요청빈도가 낮은 데이터를 원하는 중요한 사용자가 오랜 시간 대기하는 상황이 발생할 수 있다. 본 논문에서 제안한 DNPS와 FPDNPS 방법은 실제 방송 청취를 시작한 사용자들의 프로파일 정보를 바탕으로 방송 스트림을 구성함으로써 동적인 변화를 반영할 분만 아니라, 사용자의 우선순위를 함께 고려함으로써 중요한 사용자의 대기시간을 줄일 수 있다. 또한 동시에 여러 개의 데이터를 요청하는 사용자들의 대기시간을 단축시키는 FPDNPS 알고리즘을 다중 채널환경으로 확장한 M-FPDNPS를 제안하고 실험을 통해 성능을 평가한다.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.67-83
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• 2008

In order to realize ubiquitous computing, it is essential to efficiently use the resources and the computing power of mobile devices. Among others, memory efficiency, energy efficiency, and processing efficiency are required in executing the softwares embedded in mobile devices. In this paper, query processing over XML data in a mobile device where resources are limited is addressed. In a device with limited amount of memory, the techniques of XML. stream query processing need to be employed to process queries over a large volume of XML data Recently, a technique Galled XFrag was proposed whereby XML data is fragmented with the hole-filler model and streamed in fragments for processing. With XFrag, query processing is possible in the mobile device with limited memory without reconstructing the XML data out of its fragment stream. With the hole-filler model, however, memory efficiency is not high because the additional information on holes and fillers needs to be stored. In this paper, we propose a new technique called XFLab whereby XML data is fragmented with the XML labeling scheme which is for representing the structural relationship in XML data, and streamed in fragments for processing. Through implementation and experiments, XML showed that our XFLab outperformed XFrag both in memory usage and processing time.