• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1423-1435
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• 2010

Building wireless sensor networks requires a constituting sensor node to consider the following limited hardware resources: a small battery lifetime limiting available power supply for the sensor node, a low-power microprocessor with a low-performance computing capability, and scarce memory resources. Despite such limited hardware resources of the sensor node, the sensor node platform needs to activate real-time sensing, guarantee the real-time processing of sensing data, and exchange data between individual sensor nodes concurrently. Therefore, in this paper, we propose an energy-efficient real-time task scheduling technique for battery-powered wireless sensor nodes. The proposed energy-efficient task scheduling technique controls the microprocessor's operating frequency and reduces the power consumption of a task by exploiting the slack time of the task when the actual execution time of the task can be less than its worst case execution time. The outcomes from experiments showed that the proposed scheduling technique yielded efficient performance in terms of guaranteeing the completion of real-time tasks within their deadlines and aiming to provide low power consumption.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.711-712
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• 2006

In this paper, we propose algorithm that improves energy efficiency of sensor node. That is, sensor node suggests algorithm that minimizes unnecessary surrounding feeler, and when passes information to neighborhood node, transmit forecasting position of node.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.2
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• pp.209-218
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• 2009

In a wireless sensor network(WSN) each sensor node deals with numerous sensing data elements. For the sake of energy efficiency and network lifetime, sensing data must be handled effectively. A technique used for this is data aggregation. Sending/receiving data involves numerous steps such as MAC layer control packet handshakes and route path setup, and these steps consume energy. Because these steps are involved in all data communication, the total cost increases are related to the counts of data sent/received. Therefore, many studies have proposed sending combined data, which is known as data aggregation. Very effective methods to aggregate sensing data have been suggested, but there is no means of deciding how long the sensor node should wait for aggregation. This is a very important issue, because the wait time affects the total communication cost and data reliability. There are two types of data aggregation; the data counting method and the time waiting method. However, each has weaknesses in terms of the delay. A hybrid method can be adopted to alleviate these problems. But, it cannot provide an optimal point of aggregation. In this paper, we suggest a stochastic-based data aggregation scheme, which provides the cost(in terms of communication and delay) optimal aggregation point. We present numerical analysis and results.

• Journal of KIISE:Information Networking
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• v.36 no.4
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• pp.339-350
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• 2009

In wireless sensor networks, the limited battery resources of sensor nodes have a direct impact on network lifetime. To reduce unnecessary power consumption, it is often the case that only a minimum number of sensor nodes operate in active mode while the others are kept in sleep mode. In such a case, however, the network service can be easily unreliable if any active node is unable to perform its sensing or communication function because of an unexpected failure. Thus, for achieving reliable sensing, it is important to maintain the sensing level even when some sensor nodes fail. In this paper, we propose a new fault-tolerance scheme, called FCP(Fault-tolerant Coverage Preserving), that gives an efficient way to handle the degradation of the sensing level caused by sensor node failures. In the proposed FCP scheme, a set of backup nodes are pre-designated for each active node to be used to replace the active node in case of its failure. Experimental results show that the FCP scheme provides enhanced performance with reduced overhead in terms of sensing coverage preserving, the number of backup nodes and the amount of control messages. On the average, the percentage of coverage preserving is improved by 87.2% while the additional number of backup nodes and the additional amount of control messages are reduced by 57.6% and 99.5%, respectively, compared with previous fault-tolerance schemes.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.233-238
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• 2007

In this paper, a wireless sensor network system was embodied inside the shipping for digital ship. First, the analysis of radio environment inside ship are investigated. As a result, it was confirmed that a wireless sensor network system can be applied inside the ship. Using Shipboard Wireless Sensor Network System based on IEEE 802.15.4 technique, we designed, and made the prototype of Zigbee Node and RFID Reader. We could be sensing on shipboard and testing entrance of crew by using Zigbee Node and RFID Reader. The sensing and exit or entry control data are transmitted a server system through internet that connected Wireless Gateway with AP, so we can monitoring the saved data on shipboard database.

• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.123-131
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• 2008

Many researches have been actively performed to increase energy efficiency in wireless sensor networks. In these researches communication has been the most important target for reducing energy consumption. In fact, in most cases, the frequencies of communication and sensing in a sensor node affect its energy consumption. Therefore, sensing period and transmission period need to be controlled to increase energy efficiency. This paper proposes on energy efficient and restricted query flooding mechanism using service discovery. Further, we implement node management system based on the mechanism and evaluate its performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2946-2962
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• 2017

An improved energy-efficient compressed network coding method is proposed for the data communication in the wireless sensor networks (WSNs). In the method, the compressed sensing and network coding are jointly used to improve the energy efficiency, and the two-hop neighbor information is employed to choose the next hop to further reduce the number of the transmissions. Moreover, a new packet format is designed to facilitate the intermediate node selection. To theoretically verify the efficiency of the proposed method, the expressions for the number of the transmissions and receptions are derived. Simulation results show that, the proposed method has higher energy efficiency compared with the available schemes, and it only requires a few packets to reconstruct measurements with reasonable quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1A
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• pp.41-49
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• 2009

A crucial issue in deploying wireless sensor networks is to perform a sensing task in an area of interest in an energy-efficient manner since sensor nodes have limited energy Power. The most practical solution to solve this problem is to use a node wake-up scheduling protocol that some sensor nodes stay active to provide sensing service, while the others are inactive for conserving their energy In this paper, we present a simple wake-up scheduling protocol, which can maintain sensing coverage required by applications and yet increase network lifetime by turning off some redundant nodes. In order to do this, we use the concept of a weighted average distance. A node decides whether it is active or inactive based on the weighted average distance. The proposed protocol allows sensor nodes to sleep dynamically while satisfying the required sensing coverage.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.47-59
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• 2014

In this paper, I propose a new energy efficient clustering scheme to prolong the network lifetime by reducing energy consumption at the sensor node. It is possible that a node determines whether to participate in clustering with certain probability based on local density. This scheme is useful under the environment that sensor nodes are deployed unevenly within the sensing area. By adjusting the probability of participating in clustering dynamically with local density of nodes, the energy consumption of the network is reduced. So, the lifetime of the network is extended. In the region where nodes are densely deployed, it is possible to reduce the energy consumption of the network by limiting the number of node which is participated in clustering with probability which can be adjusted dynamically based on local density of the node. Through computer simulation, it is verified that the proposed scheme is more energy efficient than LEACH protocol under the environment where node are densely located in a specific area.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.1-6
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• 2009

Localization of nodes is a key technology for application of wireless sensor network. Having a GPS receiver on every sensor node is costly. In the past, several approaches, including range-based and range-free, have been proposed to calculate positions for randomly deployed sensor nodes. Most of them use some special nodes, called anchor nodes, which are assumed to know their own locations. Other sensors compute their locations based on the information provided by these anchor nodes. This paper uses a single mobile anchor node to move in the sensing field and broadcast its current position periodically. We provide a weighted centroid localization algorithm that uses coefficients, which are decided by the influence of mobile anchor node to unknown nodes, to prompt localization accuracy. We also suggest a criterion which is used to select mobile anchor node which involve in computing the position of nodes for improving localization accuracy. Weighted centroid localization algorithm is simple, and no communication is needed while locating. The localization accuracy of weighted centroid localization algorithm is better than maximum likelihood estimation which is used very often. It can be applied to many applications.