• Journal of Internet of Things and Convergence
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• v.2 no.2
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• pp.15-20
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• 2016

MicroSoft Windows 10 IoT version, released in August 2015, successfully drew consumer interest by introducing the familiar Windows into the IoT market, and enabled an easier system construction of IoT web servers. Meanwhile, overdiagnosis has recently emerged as a controversy in medical society. Establishment of communication between IoT servers and medical devices will send treatment results to users and activate communication between hospitals, greatly reducing this problem. The IoT server, with its limited resources, utilizes lightweight protocols that do not generate traffic and are easy to use. This paper proposes IoT networks which will enable medical devices to easily provide ubiquitous environments to their users, through utilization of the lightweight Simple Service Discovery Protocol (SSDP) and the secure Extensible Messaging and Presence Protocol (XMPP).

• Journal of Internet of Things and Convergence
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• v.1 no.1
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• pp.1-4
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• 2015

This paper aims to present the mathematical model to find the mean web object transfer latency in the slow-start phase of TCP congestion control mechanism, which is one of the main control techniques of Internet. Mean latency is an important service quality measure of end-user in the network. The application area of the proposed latency model is the narrowband environment including multi-hop wireless network and Internet of Things(IoT), where packet loss occurs in the slow-start phase only due to small window. The model finds the latency considering initial window size and the packet loss rate. Our model shows that for a given packet loss rate, round trip time and initial window size mainly affect the mean web object transfer latency. The proposed model can be applied to estimate the mean response time that end user requires in the IoT service applications.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.273-281
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• 2019

The energy consumed by buildings among the total national energy consumption is more than 10% of the total. For this reason, Korea has adopted the zero energy building policy since 2025, and research on the energy saving technology of buildings has been demanded. Analysis of buildings' energy consumption patterns shows that lighting, heating and cooling energy account for more than 60% of total energy consumption, which is directly related to solar power acquisition and window opening and closing operation. In this paper, we have developed a low - power IoT sensor module for window system to transfer acquired information to building energy management system. This module transmits the external environment and window opening / closing status information to the building energy management system in real time, and constructs the network to actively take energy saving measures. The power used in the module is designed as an independent power source using solar power among the harvest energy. The topology of the power supply is a Buck converter, which is charged at 4V to the lithium ion battery through MPPT control, and the efficiency is about 85.87%. Communication is configured to be able to transmit in real time by applying WiFi. In order to reduce the power consumption of the module, we analyzed the hardware and software aspects and implemented a low power IoT sensor module.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.1-6
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• 2020

This paper aims to integrate the previous models about mean object transfer latency in one framework and analyze the result through the computational experience. The analytical object transfer latency model assumes the multiple packet losses and the Internet of Things(IoT) environment including multi-hop wireless network, where fast re-transmission is not possible due to small window. The model also considers the initial congestion window size and the multiple packet loss in one congestion window. Performance evaluation shows that the lower and upper bounds of the mean object transfer latency are almost the same when both transfer object size and packet loss rate are small. However, as packet loss rate increases, the size of the initial congestion window and the round-trip time affect the upper and lower bounds of the mean object transfer latency.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.327-342
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• 2017

In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.29-35
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• 2024

TCP is considered as one of the major candidate transport protocols even for constrained IoT networks..In our previous work, we investigated the congestion control mechanism of the uIP TCP. Since the uIP TCP sets the window size to one segment by default, managing the retransmission timer is the primary approach to congestion control. However, the original uIP TCP sets the retransmission timer based on the fixed RTO, it performs poorly when a radio duty cycling mechanism is enabled and the hidden terminal problem is severe. In our previous work, we proposed a TCP retransmission timer adjustment scheme for uIP TCP which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Although our previous work showed that the proposed retransmission timer adjustment scheme can improve performance, we observe that the scheme often causes a node to set the retransmission timer for an excessively too long time period. In this work, we show that slightly modifying the dithering mechanism of the previous scheme is effective for improving TCP fairness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.311-322
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• 2014

IoT(Internet of Things) is a concept of connected internet pursuing direct access to devices or sensors in fused environment of personal, industrial and public area. In IoT environment, it is possible to access realtime data, and the data format and topology of devices are diverse. Also, there are bidirectional communications between users and devices to control actuators in IoT. In this point, IoT is different from the conventional internet in which data are produced by human desktops and gathered in server systems by way of one-sided simple internet communications. For the cloud or portal service of IoT, there should be a file management framework supporting systematic naming service and unified data access interface encompassing the variety of IoT things. This paper implements a DB-based virtual file system maintaining attributes of IoT things in a UNIX-styled file system view. Users who logged in the virtual shell are able to explore IoT things by navigating the virtual file system, and able to access IoT things directly via UNIX-styled file I O APIs. The implemented virtual file system is lightweight and flexible because it maintains only directory structure and descriptors for the distributed IoT things. The result of a test for the virtual shell primitives such as mkdir() or chdir() shows the smooth functionality of the virtual file system, Also, the exploring performance of the file system is better than that of Window file system in case of adopting a simple directory cache mechanism.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.551-558
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• 2021

In this paper, we made the integrated intelligent air ventilation of the actuator module that can be remotely controlled based on IoT and sensors. we implemented a ventilation window system by configuring an algorithm design and a driving circuit to control the operation of the actuator to open and close the ventilation port based on a predetermined number of data that detects indoor gas/CO2/humidity temperature and outdoor fine dust related indoor/outdoor environment. It is difficult to store, manage, and analyze data due to the large number of sensors and conditions for the transmission data of indoor air circulation module. The remote monitoring and remote wireless control screens were constructed to automate the separation and operation conditions by extracting and managing the state. We apply MQTT to enhance big data transmission and construct the system using Rocket MQ to ensure safe transmission of operational big data against system errors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.965-973
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• 2016

As increase of IoT services in wireless communications, the performance evaluation is required to connects massive number of devices in LTE-A system. Existing performance analysis of 3GPP TR 37.868 is shown the limited results up to the number of devices only 30,000 in a cell. So, it is required the performance analysis according to various parameter values in the greater number of devices environment. In this paper, to evaluate the performance, we developed the new modules to simulate LTE-A random access procedures because of a limit that accommodate massive number of devices in existing LTE module. The simulator was developed by OPNET and it can support up to 100,000 devices per cell. This paper also represents the performance analysis results according to the number of preambles, the maximum number of preamble transmissions, backoff indicator and msg2 window size in the same environment with 3GPP TR 37.868. This results are expected to be utilized such as the transition to 5G network and standardization activities.