• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.40-44
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• 2009

Data-centric storage schemes(DCS) are one of representative researches that efficiently store and manage sensor readings in sensor nodes in the sensor networks. In DCS, a sensor sends the sensed data to a specific node in order to store them. However, it has a problem that sensor nodes consume a lot of energy for transmitting their readings to remote sensor node. In this paper, we propose a novel sensor data compression algorithm to reduce communication costs. The proposed algorithm does not transmit all the sensed data and establishes a safe region. It transmits the sensed data only when current measurement is out of the safe region, As a result, the propposed algorithm extends network life time and reduces data transmission. It is shown through performance evaluation that our proposed algorithm reduces energy consumption by about 60% over the conventional algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.260-264
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• 2009

There have been many studies on u-Healthcare system for human using sensor network systems. In this paper, we design and implement a healthcare system for pets called uPetCare(Ubiquitous Pet-Care System) that can manage the status of pet on the web. The main functions of this system are 1) gathering data using sensor network, 2) multi-hop communication in sensor network, 3) data compression and aggregation at sink node, 4) storing data in web server, 5) real-time data monitoring using AJAX, 6) activity recognition of pet.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.101-108
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• 2017

A wireless image sensor node collecting image data for environmental monitoring or surveillance requires a large amount of energy to transmit the huge amount of video data. Even though solar energy can be used to overcome the energy constraint, since the collected energy is also limited, an efficient energy management scheme for transmitting a large amount of video data is needed. In this paper, we propose a method to reduce the number of blackout nodes and increase the amount of gathered data by selecting an appropriate video coding method according to the energy condition of the node in a solar-powered wireless video sensor network. This scheme allocates the amount of energy that can be used over time in order to seamlessly collect data regardless of night or day, and selects a high compression coding method when the allocated energy is large and a low compression coding when the quota is low. Thereby, it reduces the blackout of the relay node and increases the amount of data obtained at the sink node by allowing the data to be transmitted continuously. Also, if the energy is lower than operating normaly, the frame rate is adjusted to prevent the energy exhaustion of nodes. Simulation results show that the proposed scheme suppresses the energy exhaustion of the relay node and collects more data than other schemes.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.705-708
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• 2005

Mobile Mapping Systems using the vehicle equipped the GPS, IMU, CCD Cameras is the effective system for the management of the road facilities, update of the digital map, and etc. They must provide users with the sensor data which is acquired by Mobile Mapping Systems in real-time so that users can process what they want by using the latest data. But it' s not an easy process because the amount of sensor data is very large, particularly image data to be transmitted. So it is necessary to reduce the amount of image data so that it is transmitted effectively. In this study, the effective method was suggested for the compression/decompression image data using the Wavelet Transformation and Huffman Coding. This technique will be possible to transmit of the geographic information effectively such as position data, attitude data, and image data acquired by Mobile Mapping Systems in the wireless internet environment when data is transmitted in real-time.

• ETRI Journal
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• v.42 no.3
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• pp.341-350
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• 2020

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple-input multiple-output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space-time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple-input multiple-output (MIMO) data transmission technique in the WSNs. The DSC-MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC-MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single-input single-output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.331-343
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• 2009

Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppressions provide opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not on the correlation of sensor data. In this paper, we propose a novel clustering algorithm based on the correlation of sensor data. We modify the advertisement sub-phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data is reduced and the whole network lifetime is prolonged.

• Annual Conference of KIPS
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• 2007.05a
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• pp.870-872
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• 2007

Sensor networks have been widely applied for data collection. Due to the energy limitation of the sensor nodes and the most energy consuming data transmission, we should allocate as much work as possible to the sensors, such as data compression and aggregation, to reduce data transmission and save energy. Querying extreme values is a general query type in wireless sensor networks. In this paper, we propose a novel querying method called Two-Level Filter (TLF) for querying extreme values in wireless sensor networks. We first divide the whole sensor network into domains using the Distributed Data Aggregation Model (DDAM). The sensor nodes report their data to the cluster heads using push method. The advantages of two-level filter lie in two aspects. When querying extreme values, the number of pull operations has the lower boundary. And the query results are less affected by the topology changes of the wireless sensor network. Through this method, the sensors preprocess the data to share the burden of the base station and it combines push and pull to be more energy efficient.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.723-740
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• 2022

This study aimed to develop an Arduino-based garment pressure device (APD) on the basis of using Single-Tact sensor by suggesting the reliable clothing pressure range and coefficient of selected sensors through the APD calibration process. Once the APD was validated, the pressure of the experimental men's lower body compression wears was measured using the APD and was compared to the pressure measured using the existing air-pack type pressure meter. The subjects were one mannequin and eight men in their 20's, and the trial compression wears were calf sleeves and pants. Clothing pressures were measured in hip, mid-thigh, calf, and ankle. In terms of the 99% confidence level, the experimental clothing pressure measured at the designated measuring points using the APD was considered identical to the one measured using an existing clothing pressure meter. Therefore, on the basis of the experiment results, this study demonstrated that the APD is as reliable as the existing clothing pressure meter within the pressure ranges of 0.54-16.79 kPa and 0.18-25.47 kPa as provided by the SingleTact sensor supplier's data on the basis of using an external ADC (Analog to Digital Converter) module.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.126-134
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• 2011

Recently, many researchers are interested in using join queries to correlate sensor readings stored in different regions. In the conventional algorithm, the preliminary join coordinator collects the synopsis from sensor nodes and determines a set of sensor readings that are required for processing the join query. Then, the base station collects only a part of sensor readings instead of whole readings and performs the final join process. However, it has a problem that incurs communication overhead for processing the preliminary join. In this paper, we propose a novel energy-efficient in-network join scheme that solves such a problem. The proposed scheme determines a preliminary join coordinator located to minimize the communication cost for the preliminary join. The coordinator prunes data that do not contribute to the join result and performs the compression of sensor readings in the early stage of the join processing. Therefore, the base station just collects a part of compressed sensor readings with the decompression table and determines the join result from them. In the result, the proposed scheme reduces communication costs for the preliminary join processing and prolongs the network lifetime.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.51-58
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• 2010

In this paper, we introduce various attempts to transmit sensor data efficiently for design of a water quality monitoring system under the USN environment. The representative methods are the sensor management on a sensor node and the clustering on a sink node. The sensor management includes controls of sensing intervals, data accumulations, and data transmissions. And the clustering is one of efficient data compression methods using data mining technology. From the experimental results we confirmed that the proposed transmission method using the sensor management and the clustering outperformed common transmission method.