• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.360-374
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• 2016

Since WSNs (Wireless Sensor Networks) applied to their application areas such as smart home, smart factory, environment monitoring, etc., depend on sensor data, the sensor data is the most important among WSN components. The resources of each node consisting of WSN are extremely limited in energy, hardware and so on. Due to these limitation, communication failure probabilities become much higher and the communication failure causes data loss to occur. For this reason, this paper proposes 2MC (Maximum/Minimum Compression) that is a method to compress sensor data by selecting circular queue-based maximum/minimum sensor data values. Our proposed method reduces sensor data losses and value errors when they are recovered. Experimental results of 2MC method show the maximum/minimum 35% reduction efficiency in average sensor data accumulation error rate after the 3 times compression, comparing with CQP (Circular Queue Compression based on Period) after the compressed data recovering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3641-3655
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• 2016

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.82-91
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• 2010

Data compression technique is traditional and effective to reduce network traffic. Generally, sensor data exhibit strong correlation in both space and time. Many algorithms have been proposed to utilize these characteristics. However, each sensor just utilizes neighboring information, because its communication range is restrained. Information that includes the distribution and characteristics of whole sensor data provide other opportunities to enhance the compression technique. In this paper, we propose an orthogonal approach for compression algorithm based on a novel feedback diffusion algorithm in sensor networks. The base station or a super node generates the Huffman code for compression of sensor data and broadcasts it into sensor networks. Every sensor that receives the information compresses their sensor data and transmits them to the base station. We define this approach as feedback-diffusion. In order to show the superiority of our approach, we compare it with the existing aggregation algorithms in terms of the lifetime of the sensor network. As a result, our experimental results show that the whole network lifetime was prolonged by about 30%.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.2
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• pp.79-86
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• 2016

In wireless sensor networks, increasing the sensing rate of each node to improve the data accuracy usually incurs a decrease of network lifetime. In this study, an energy-adaptive data compression scheme is proposed to efficiently control the sensing rate in an energy-harvesting wireless sensor network (WSN). In the proposed scheme, by utilizing the surplus energy effectively for the data compression, each node can increase the sensing rate without any rise of blackout time. Simulation result verifies that the proposed scheme gathers more amount of sensory data per unit time with lower number of blackout nodes than the other compression schemes for WSN.

• International Journal of Contents
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• v.11 no.2
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• pp.31-36
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• 2015

Recent years have seen a significant increase in demand for multimedia data over wireless sensor networks for monitoring applications that utilize sensor nodes to collect multimedia data, including sound and video. However, the multimedia streams generate a very large amount of data. When data transmission schemes for traditional wireless sensor networks are applied in wireless multimedia sensor networks, the network lifetime significantly decreases due to the excessive energy consumption of specific nodes. In this paper, we propose a data compression scheme that implements the Chinese remainder theorem to a wireless multimedia sensor network. The proposed scheme uses the Chinese Remainder Theorem (CRT) to compress and split multimedia data, and it then transmits the bit-pattern packets of the remainder to the base station. As a result, the amount of multimedia data that is transmitted is reduced. The superiority of our proposed scheme is demonstrated by comparing its performance to that of an existing scheme. The results of our experiment indicate that our proposed scheme significantly increased the compression ratio and reduced the compression operation in comparison to those of existing compression schemes.

• Journal of KIISE:Information Networking
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• v.35 no.4
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• pp.318-325
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• 2008

Most of the sensor applications collect and analyze sensor data within a given period of time. When sensor send a data to sink, it spend many communication cost. Accordingly, a compression algorithm is one of the most critical issues for the communication cost decrease in sensor fields. In this paper, we propose an algorithm for compressing sensor data using the dynamic bit assignment technique. In our algorithm, sink collect sensor data within a short period of time and make bit assign information. Then sink send the information to sensor. Finally, sensors compresssensing data and send to sink.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.87-91
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• 2008

Data compression techniques are traditional and effective to reduce the network traffic. Generally, sensor data exhibit strong correlation in both space and time. Many algorithms have been proposed to utilize these characteristics. However, each sensor just utilizes neighboring information, since its communication range is restrained. The distribution and characteristics of whole sensor data provide other opportunities to enhance the compression technique. In this paper, we propose an orthogonal approach for compression algorithm. The base station or a super node generates useful information for compression of sensor data and broadcasts it into sensor networks. Every sensor that received the information compresses their sensor data and transmits them to the base station. We define this approach as feedback-diffusion. In order to show the superiority of our approach, we compare it with the existing aggregation algorithms in terms of the lifetime of the sensor network. As a result, our experimental results show that the whole network lifetime was prolonged by about 30%.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.37-45
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• 2013

In recent years, the demands of multimedia data in wireless sensor networks have been significantly increased for the high-quality environment monitoring applications that utilize sensor nodes. However, since the amount of multimedia data is very large, the network lifetime is significantly reduced due to excessive energy consumption on particular nodes. To overcome this problem, in this paper, we propose a high efficiency data compression scheme in wireless multimedia sensor networks. The proposed scheme reduces the packet size by a multiple compression technique that consists of primary compression that deletes the lower priority bits considering characteristics of multimedia data and secondary compression based on Chinese Remainder Theorem. To show the superiority of our scheme, we compare it with the existing compression scheme. Our experimental results show that our proposed scheme reduces the amount of transmitted data by about 55% and increases network lifetime by about 16% over the existing scheme on average.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.81-88
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• 2017

Solar-powered wireless sensor nodes can use extra energy to obtain additional data to increase the precision. However, if the amount of data sensed is increased indiscriminately, the overhead of relay nodes may increase, and their energy may be exhausted. In this paper, we introduce a sensing and compression rate selection scheme to increase the amount of data obtained while preventing energy exhaustion. In this scheme, the neighbor nodes of the sink node determine the limit of data to be transmitted according to the allocated energy and their descendant nodes, and the other nodes select a compression algorithm appropriate to the allocated energy and the limitation of data to be transmitted. A simulation result verifies that the proposed scheme gathers more data with a lower number of blackout nodes than other schemes. We also found that it adapts better to changes in node density and the amount of energy harvested.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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• pp.28-36
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• 2007

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.