• Journal of Internet of Things and Convergence
• /
• v.10 no.1
• /
• pp.69-75
• /
• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• The Journal of the Korea Contents Association
• /
• v.14 no.7
• /
• pp.29-44
• /
• 2014

To provide context aware service, certain phenomena and situation information of the user should be detecting and monitoring using sensors. But, the sensor data has heterogeneity about system, syntax, structure, and semantics. So, it is difficult to apply to various services. To solve this issue, we should remove these heterogeneities and convert to context information that is easy to provide context aware services. Also, the existing context aware service mash-up environment require relevant sensor type, criteria of context, programing ability to define the service situation. So, it is difficult to create service for a non-technical user. Therefore, we propose ontology based user-centric service environment to provide context aware IoT services. It provide context awareness and user-desired services using data that acquired via IoT paradigm. The proposed environment consist of the ontology based semantic sensor data processing and the user-centric service environment. It provide convert the object data to the context information and service mash-up process to create user service in easy way.

• International journal of advanced smart convergence
• /
• v.6 no.1
• /
• pp.76-81
• /
• 2017

This paper presents a low cost and flexible IoT enabled smart LED controller using Arduino that is used for emergency exit signs. The Internet of Things (IoT) is become a global network that put together physical objects using network communications for the purpose of inter-communication of devices, access information on internet, interaction with users as well as permanent connected environment. A crucial point in this paper, is underlined on the potential key points of applying the Arduino platform as low cost, easy to use microcontroller with combination of various sensors applied in IoT technology to facilitate and establishment of intelligent products. To demonstrate the feasibility and effectiveness of the system, devices such as LED strip, combination of various sensors, Arduino, power plug and ZigBee module have been integrated to setup smart emergency exit sign system. The general concept of the proposed system design discussed in this paper is all about the combination of various sensor such as smoke detector sensor, humidity, temperature sensor, glass break sensors as well as camera sensor that are connected to the main controller (Arduino) for the purpose of communicating with LED exit signs displayer and dedicated PC monitors from integrated system monitoring (controller room) through gateway devices using Zig bee module. A critical appraisal of the approach in the area concludes the paper.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.1
• /
• pp.90-96
• /
• 2019

Storage cabinet in a lab in these days measures various environmental factors in real-time with IoT sensors. Preexisting system collects sensor data, analyze a risk and then command other equipment. Such centralized control system tends to have an issue with of speed slowing down. It's because when there are more storage cabinets, there are more data to process. In order to solve this issue, this report addresses decentralized IoT sensor based lab safety control system. It can analyze internal state of storage cabinet to identify any hazardous situations and effectively control them. Such decentralized control system using sensor modules for internal environment of the cabinet storage and automated control algorithm based on administrator's log history can manage any hazardous situations by automated control of environment factors of inside a lab. It would allow users to deal with a hazard if it happens. Even better, it can prevent it to happen from the beginning.

• Journal of Advanced Navigation Technology
• /
• v.24 no.6
• /
• pp.593-600
• /
• 2020

The equipment for marine IoT service have to overcome the effect of seawater. Furthermore, the free floating transmitter in seawater will be less affected by the seawater environment. The results of the design and fabrication of antenna, which is embedded in buoy, are shown in this research. The proposed antenna is used to supervise the states of fishing gears in monitoring system for real-name system of electric fishing gear. The selected frequency band of the proposed antenna is 920 MHz, and PCB pattern type is selected for subminiature and light weight. It is confirmed that RF characteristics is more degraded, however, the radiation is gradually upward as the contact surface of buoy with seawater is more broaden through the simulation results. That is, the RF performance of the proposed antenna is more deteriorated but beam radiation characteristics is more suited the marine IoT, the seawater effect is more increased. It is expected that the proposed antenna will contribute the implementation of IoT network based on low power wide area (LPWA) when the degradation of RF performance will be settled.

• Advanced Industrial SCIence
• /
• v.1 no.2
• /
• pp.24-29
• /
• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.4
• /
• pp.216-221
• /
• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• Journal of Korea Multimedia Society
• /
• v.19 no.11
• /
• pp.1891-1899
• /
• 2016

Recently food problem and crop disaster have been increased continuously because of the meteorological changes. These cause rising cost for crops continuously and irregularly. Some researchers have studied straight structure of device for hydroponics and plant factory previously to solve a fundamental part of these problems. However, there are several problems such as limited crop cultivation space, providing irregular nutrients for crops, and lack of monitoring interfaces. For them, we propose an optimal growth and development crops management system using light source tracking and recirculation of nutrient solution method to supply nutrient continuously based on IoT. In order to evaluate the performance of our system, we compared and analyzed in terms of two viewpoints, the tracking analysis for natural light source measurement and the growth of crops through artificial light, LED, respectively. We confirmed that the higher the duty ratio of LED, the larger the crop's size, particularly. As well as, for about 1 month, we compared with the existing natural light growing environment and that of our system. It was confirmed that the size of the crops grown through our system is about three times larger than that of natural light natural crops.

• Journal of the Korean Solar Energy Society
• /
• v.37 no.2
• /
• pp.23-33
• /
• 2017

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as $CO_2$ sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured $CO_2$ value. This result indicates potential of IoT data for awareness of indoor environmental status.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.288-289
• /
• 2018

The use of IoT (Internet of Things) is rapidly expanding to enhance the quality of life as seen in the interconnection between mobile devices and Web. This paper proposes an embedded system that connects sensors and mobile devices via IoT technology, improving the user's ability and service experience to remotely control the home appliances with mobile-detection features. Home appliances are expected to increase labor efficiency by utilizing the embedded system connected to the sensors. Using mobile applications to control home appliances remotely and to monitor operation status advances the user's knowledge, experience, and perspectives to experience refined life qualities.