• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.359-366
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• 2018

Social infrastructure facilities that have been under construction since the country's high-growth period are undergoing rapid aging, and safety assessments of large structures such as bridge tunnels, which can be directly linked to large-scale casualties in the event of an accident, are necessary. Wireless smart sensor networks that improve SHM(Structural Health Monitoring) based on existing wire sensors are difficult to construct economical and efficient system due to short signal reach. The LPWAN, Low Power Wide Area Network, is becoming popular with the Internet of Things and it is possible to construct economical and efficient SHM by applying it to structural health monitoring. This study examines the applicability of LoRa LPWAN to structural health monitoring and proposes a channel usage pre-planning based LoRa network operation method that can efficiently utilize bandwidth while resolving conflicts between channels caused by using license - exempt communication band.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.710-715
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• 2006

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, a design of pervasive monitoring and control system has been made possible. In this paper, we present a new algorithm for one of the most likely applications for sensor networks; tracking moving targets. The proposed algorithm uses a cooperations between the sensor nodes which detect moving objects. Therefore, the proposed scheme is robust against prediction failures which may result in temporary loss of the target. Using simulations we show that tile proposed moving object tracking algorithm is capable of accurately tracking targets with random movement patterns.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.63-71
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• 2012

Most of sensor networks are difficult to be debugged, verified, and upgraded once they are deployed in the fields, for they are usually deployed in real world and large scale. Therefore, before deploying the sensor networks, we should test and verify them sufficiently in realistic testbeds. However, since we need to control physical environments which interact with sensor networks, it takes much of time and cost to test and verify sensor networks at the level of resource-constrained sensor nodes in such environments. This paper proposes an efficient software framework for evaluating and verifying sensor networks in the view points of network and application operations (i.e., accuracy of sensing algorithms). Applying the proposed software framework to the development of a simulator for a smart parking application based on wireless sensor network, this paper verifies the feasibility of the proposed framework.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• ETRI Journal
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• v.41 no.6
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• pp.782-796
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• 2019

This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large-scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi-Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.

• Journal of the Korean Society for Railway
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• v.11 no.4
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• pp.364-370
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• 2008

In this paper, we proposed new sensor network architecture with autonomous robots based on beacon mode and implemented real time monitoring system in real test-bed environment. The proposed scheme offers beacon based real-time scheduling for reliable association process with parent nodes and dynamically assigns network address by using NAA (Next Address Assignment) mechanism. For the large scale multi-sensor processing, our real-time monitoring system accomplished the intelligent database processing, which can generate not only the alert messages to the civilians but also process various sensing data such as fire, air, temperature and etc. Moreover, we also developed mobile robot which can support network mobility. Though the performance evaluation by using real test-bed system, we illustrate that our proposed system demonstrates promising performance for emergence monitoring systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.113-119
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• 2020

Large sensor networks require the collection and analysis of data from a large number of sensors. The number of sensors that can be controlled per micro controller is limited. In this paper, we propose how to aggregate sensor data from a large number of sensors using a large number of microcontrollers and multiple bridge nodes, and design and implement an efficient communication system for sensor data collection. Bridge nodes aggregate data from multiple microcontrollers using SPI communication, and transfer the aggregated data to PC servers using wireless TCP/IP communication. In this paper, the communication system was constructed using the Open H/W Aduo Mini and ESP8266 and performance of the system was analyzed. The performance analysis results showed that more than 30 sensing data can be collected per second from more than 700 sensors.

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.