• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.67-74
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• 2019

As the market for Internet of Things (IoT) service grows, the security issue of the data from IoT devices becomes more important. In this paper, we implemented a cryptographic library for confidentiality of sensor data from Android Things based IoT services. The library made use of the SEED algorithm for encryption/decryption of data and we verified the library by implementing a service environment. With the library, the data is securely encrypted and stored in the database and the service environment is able to represent the current sensing status with the decrypted sensor data. The contribution of this work is in verifying the usability of SEED based encryption library by implementation in IoT sensor based service environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.408-409
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• 2018

In this paper, we propose an effective web service software platform for remote monitoring and control. In order to propose a system configuration that can check the object information on the web in real time to improve the performance of the web service, the object information reception module and the web viewer system are configured. As IoT devices grow, the environment becomes too complicated to identify and control the status of many devices. We have proposed that these problems be easily controlled in a web-based environment and current status information can be viewed in real time.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.486-488
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• 2013

In the earth more than half of the space filled with water. In that water most of the part is in the form of oceans. The ocean atmosphere determines climate on the land. Combining the Underwater Acoustic Sensor Network (UWASN) system with Internet Of Things (IoT) is called Internet of Underwater Things (IoUT). Using IoUT we can find the changes in the ocean environment. Underwater sensor nodes are used in UWASN. Underwater sensor nodes are constructive in offshore investigation, disaster anticipation, data gathering, assisted navigation, pollution checking and strategic inspection. By using IoT components such as Database, Server and Internet, ocean data can be broadcasted. This paper introduces IoUT architecture and and explains fish forming application scenario with this IoUT architecture.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.417-422
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• 2015

A service size of the IoT environment is determined by the number of sensors. The number of sensors increase means increases the amount of data generated by the IoT environment. There are studies to reliably operate a network for research and operational dynamic buffer for data when network congestion control congestion in the network environment. There are also studies of the stream data that has been processed in the connectionless network environment. In this study, we propose a sensor gateway for processing big data of the IoT environment. For this, review the RESTful for designing a sensor middleware, and apply the double-buffer algorithm to process the stream data efficiently. Finally, it generates a big data traffic using the MJpeg stream that is based on the HTTP protocol over TCP to evaluate the proposed system, with open source media player VLC using the image received and compare the throughput performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.173-178
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• 2019

Recently, the types and amount of data generated, collected, and measured in IoT such as smart home, security, and factory are increasing. The technologies for IoT service include sensor devices to measure desired data, embedded software to control the devices such as signal processing, wireless network protocol to transmit and receive the measured data, and big data and AI-based analysis. In this paper, we focused on developing a gateway for interfacing heterogeneous sensor network protocols that are used in various IoT devices and propose a heterogeneous network interface IoT gateway. We utilized a OpenWrt-based wireless routers and used 6LoWAN stack for IP-based communication via BLE and IEEE 802.15.4 adapters. We developed a software to convert Z-Wave and LoRa packets into IP packet using our Python-based middleware. We expect the IoT gateway to be used as an effective device for collecting IoT big data.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.2014-2023
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• 2016

Web presence is one of the key issues for extensive deployment of Internet-of-Things (IoT). An obstacle to overcome for Web presence is relatively low computing power of IoT devices. In this paper, we present implementation of an IoT platform based on Constrained Application Protocol (CoAP) which is a web transfer protocol proposed by Internet Engineering Task Force (IETF) for the low performance IoT devices such as Wireless Sensor Network (WSN) nodes and micro-controllers. To qualify the performance of CoAP-based IoT system for such an application as smart grid, we designed a test platform consisting of Raspberry Pi2, Kmote WSN node and a desktop PC. Using open source softwares, CoAP was implemented on top of the platform. Leveraging the GET command defined at CoAP specification, performance of the system was measured in terms of round-trip time (RTT) from web application to the Kmote sensor node. To investigate abnormal cases among the test results, hop-by-hop delays were measured to analyze resulting data. The average response time of CoAP-based communication except the abnormal data was reduced by 23% smaller than the previous research result.

• Current Photovoltaic Research
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• v.9 no.3
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• pp.106-109
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• 2021

In order to perform photovoltaic (PV) operation and management (O&M) efficiently, individual PV module monitoring is becoming more important. In this research, we developed wireless IoT sensor which can monitor individual photovoltaic modules. This IoT sensor can detect the output voltage, current and module temperature of individual modules and provide monitored data by wireless communication. Measured voltage error was 1.23%, and it shows 16.6 dBM, 0.42sec and 7.1 mA for voltage, transmittance output, response time and mean power consumption, respectively. IoT sensors were demonstrated in the test field with real climate environment condition and each of 5 sensors showed precise results of voltage, current and temperature. Also, sensors were compared with commercial power-optimizers and showed result difference within 5%.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.10
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• pp.1139-1144
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• 2015

The internet of things(: IoT) which could produce new information as well as service through connecting small devices with internet interface becomes reality and could be utilized in home, office and plant. Especially the IoT can provide our home for safety, security, convenience, and power saving since all devices form a small internet. This paper proposes an IoT prototype for home with Raspberry Pi which has various sensors and monitors environment. This Raspberry Pi sensor node is small and inexpensive but can provide powerful service. The implemented node goes beyond simple role of sensor node and features a kind of sensor web node which performs various functions to outer network. So the proposed IoT prototype can provide flexibility as well as extensibility since it does not need expensive server.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.309-310
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• 2018

Recently, the systems for managing the labs provide services that can be managed in real time by using various sensors based on IoT. The system collects sensor data and transmits it to the server, identifies the dangerous situation, and sends operation commands to the devices. These systems have a centralized structure that slows data processing when managing multiple laboratories. To solve this problem, this paper proposes a system that manages laboratories in distributed processing environment to identify and manage risk situations. The sensor module is used to control the laboratory and to automatically identify and respond to the dangerous situation.