• Journal of KIISE:Computing Practices and Letters
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• v.14 no.4
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• pp.341-351
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• 2008

Communication in wireless sensor networks comprising a plurality of sensor nodes located in an ad hoc environment is unidirectional in that data gathered by sensor nodes are transmitted to a sink node and not vice versa. In those networks, it is not possible for a server or a gateway to send commands to the sensor nodes to determine whether some previously received data are valid when the data indicate unusual conditions, which makes it difficult to make appropriate reactions to the unusual situations. This paper proposes and implements a TDMA-based sensor network communication protocol named BiWSLP(Bidirectional Wireless Sensor Line Protocol) supporting bidirectional communication capability. The BiWSLP is an extension of the WSLP, a unidirectional sensor network communication protocol based on the TDMA protocol. To test the feasibility of the proposed BiWSLP, we construct a virtual bridge management system capable of sending commands to sensor nodes as well as collecting data from the sensor nodes. Based on the test results of the virtual bridge management system, we show the applicability and advantages of the BiWSLP in terms of energy efficiency and bidirectional communication capability.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.337-340
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• 2005

Context-awareness techniques in ubiquitous computing environment provide various services to users who need to get information via the analysis of collected information from sensors in a spatial area. Context-awareness has been increased in ubiquitous computing and is applied to many different applications such as disaster management system, intelligent robot system, transportation management system, shopping management system, and digital home service. Many researches have recently focused on services that provide the appropriate information, which are collected from Internet by different kinds of sensors, to users according to context of their surrounding environment. In this paper, we propose an abstraction model to manage the large-scale contextual information and their metadata which are collected from different kinds of in-situ sensors in a spatial area and are presented them on the web. This model is composed of the modules expressing functional elements of sensors using sensorML(Sensor Model Language) based on XML language and the modules managing contextual information, which is transmitted from the sensors.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.11
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• pp.820-829
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• 2006

Wireless sensor networks consisting of hundreds to thousands of nodes are expected to be increasingly deployed in coming years, as they enable reliable monitoring and analysis of physical worlds. These networks have unique features that are very different from traditional networks, e.g., the numerous numbers of nodes, limitation in power, processing, and memory. Due to these unique features of wireless sensor networks, sensor data management including querying becomes a challenging problem. Furthermore, due to wide popularization of the Internet and its facility in use, it is generally accepted that an unattended network can be efficiently managed and monitored over the Internet. In particular, in order to more efficiently query and manage data in a sensor network. in this paper, the architecture of a sensor gateway including web-based query server is presented and its implementation detail is illustrated. The presented web-based gateway is largely divided into two important parts: Internet part and sensor network part. The sensor network part plays an important role of handling a variety of sensor networks, including flat or hierarchical network architecture, by using internally layered architecture for efficiently querying and managing data in a sensor network. In addition, the Internet part provides a modular gateway function for favorable exchange between the sensor network and Internet.

• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.1-10
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• 2007

The ubiquitous computing system is expected to be widely utilized in digital home, logistics control, environment/disaster management, medical/health-care services and other applications. The ubiquitous sensor network (USN) is a key infra-structure of this system. Nodes in the USN are exposed to adverse environments and required to perform their missions with very limited power supply only. Also the sensor network is composed of much more nodes. In case some node consumes up its power capacity under a certain required level, the network topology should change and re-routing/ re-transmission of data is necessitated. Resultantly communication protocols studied for conventional wireless networks or ad-hoc networks are not suitable for the sensor network. Schemes should be devised to control the efficient usage of node power in the sensor network. This paper proposes a routing algorithm to enhance the efficiency of power consumption for USN node and analyzes its performance by simulation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

• Spatial Information Research
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• v.20 no.1
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• pp.59-69
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• 2012

For the USN(Ubiquitous Sensor Network) environment which generally uses spatial sensor data as well as aspatial sensor data, a sensor database system to manage these sensor data is essential. In this reason, some sensor database systems such as TinyDB, Cougar are being developed by many researchers. However, since most of them do not support spatial data types and spatial operators to manage spatial sensor data, they have difficulty in processing spatial sensor data. Therefore, this paper developed a spatial sensor database system by extending TinyDB. Especially, the system supports spatial data types and spatial operators to TinyDB in order to manage spatial sensor data efficiently and provides the memory management function and the filtering function to reduce the system overload caused by sensor data streams. Lastly, we compared the processing time, accuracy, and memory usage of the spatial sensor database system with those of TinyDB and proved its superiority through the performance evaluation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.539-540
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• 2021

In this paper, we consider the management scheme of the crowd group in the sensor networks. In the case of the networks searching the critical area, the operation of crowd group can affect the mission implement. The mobile sink system leading the group networks can change the network configuration as the dangerous data gathered from group sensor nodes. The dynamic network management provides the important role to the mission of mobile sink to react the dangerous environments.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.370-375
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• 2023

As we enter a super-aged society, studies are being conducted to reduce complications and deaths caused by falls in elderly adults. Research is being conducted on interventions for preventing falls in the elderly, wearable devices for detecting falls, and methods for improving the performance of fall detection algorithms. Wearable devices for detecting falls of the elderly generally use gyro sensors. In addition, to improve the performance of the fall detection algorithm, an artificial intelligence algorithm is applied to the x, y, z coordinate data collected from the gyro sensor. In this paper, we develop a wearable device that uses a gyro sensor, body temperature, and heart rate sensor for health management as well as fall detection for the elderly. In addition, we develop a fall detection and health management system that works with wearable devices and a guardian's mobile app to improve the performance of the fall detection algorithm and provide health information to guardians.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.746-753
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• 2004

In this paper, the Power Management System(PMS) which based on a decision making system according to power strategy is proposed and implemented. PMS is designed to have functions of power monitoring. controlling, synchronizing load sharing and monitoring of driving engine, etc. PMS consists of the internet communication system(ICS). Remote Management System(RMS) and Sensor Driver System (SDS) ICS transmits the monitoring and supervisory data via Internet to Remote Management System(RMS) in real-time SDS detects various power system data on local generator and utility via I/O interface system. I/O interface system receives various status data and outputs control signals. Implemented PMS is tested with dummy signal to verify proposed functions and shows good results. For future study implemented PMS will be tested under real load condition to merchandize.