• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.331-341
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• 2014

Internet of Thing(IoT), which is recently talked about with the development of information and communication technology, provides big data to all nodes such as companies and homes, means of transportation etc. by connecting all things with all people through the integrated global network and connecting all actual aspects of economic and social life with Internet of Thing through sensor and software. Defining Internet of Thing, it plays the role of a connector of providing various information required for the decision-making of companies in the cloud computing environment for the Insight usage by collecting and storing Raw Data of the production site through the sensor network and extracting big data in which data is accumulated and Insight through this. In addition, as the industry showing the largest linkage with other root industries among root industries, the mold industry is the core technology for controlling the quality and performance of the final product and realizing the commercialization of new industry such as new growth power industry etc. Recently, awareness on the mold industry is changing from the structure of being labor-intensive, relying on the experience of production workers and repeating modification without the concept of cost to technology-intensive, digitization, high intellectualization due to technology combination according to IT convergence. This study, therefore, is to provide a golden opportunity to increase the direct and indirect expected effects in poor management activities of small businesses by actually implementing and managing the entire process of mold life cycle to information system from mold planning to mass production and preservation by building SME(small and medium-sized enterprises)-type mold life cycle management information system in the cloud computing environment and applying it to the production site.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.364-371
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• 2017

TPMS is a vehicle electric system that measures the air pressure of a tire, and informs the driver of current tire states. The TPMS sensor typically uses unidirectional communication for small size, light weight, and low power. The transmission period of the sensor indicates the service quality of monitoring the tire. In order to determine the optimal transmission period, frame collision probability and the life time of the sensor should be analyzed. In this paper, collision probability model using Venn diagram is designed in low power TPMS with the normal and warning mode. And the life time and a collision probability were analyzed with the ratio(n) of the normal mode to warning mode transmission period. As a result, $T_{nP}=31sec$ and $T_{wP}=2.4sec$ at 5 years, and $T_{nP}=71sec$ and $T_{wP}=2.5sec$ at 7 years.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.187-196
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• 2022

In this paper, we propose an example of designing and constructing a roof-type solar power plant structure equipped with a Pseudo-BIPV (Building-Integrated Photovoltaic) shape suitable for use as a roof of a small warehouse with a sandwich-type panel structure. As the characteristics of the roof-type solar power generation facility to be installed in the small warehouse proposed in this study, the shape of the roof is not a general A type, but a right-angled triangle shape with the slope is designed to face south. We chose a structure in which an inverter for one power plant and a control facility are linked by grouping several roofs of buildings. In addition, the height of the roof structure is less than 20 cm from the floor, and it has a shape similar to that of the BIPV, so it is building-friendly because it is almost in close contact with the roof. At the same time, the roof creates a reflective light source due to the white color. By linking this roof with a double-sided solar panel, we designed it to obtain both the advantage of the roof-friendliness and the advantage of efficiency improvement for the electric power generation based on the double-sided panel. Compared to the existing solar power generation facilities using A-shaped cross-sectional modules, the power generation efficiency of roofs in this case is increased by more than 11%, which we can confirm, through the comparison analysis of monitoring data between power plants in the same area. Therefore, if the roof-type solar structure suitable for the small warehouse we have presented in this paper is used, the facilities of electric power generation is eco-friendly. Further it is easier to obtain facility certification compared to the BIPV, and improved capacity of the power generation can be secured at low material cost. It is believed that the roof-type solar power generation facility we proposed can be usefully used for warehouse or factory-based smart housing. Sensor devices for monitoring, CCTV monitoring, or safety and environment management, operating in connection with the solar power generation facilities, are linked with the Internet of Things (IoT) solution, so they can be monitored and controlled remotely.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.307-315
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• 2017

Random number generator(RNG) is essential in cryptographic applications. As recently a system using small devices such as IoT, Sensor Network, SmartHome appears, the lightweight cryptography suitable for this system is being developed. However due to resource limitations and difficulties in collecting the entropy, RNG designed for the desktop computer are hardly applicable to lightweight environment. In this paper, we propose a lightweight RNG to produce cryptographically strong random number using sensors. Our design uses a Hankel matrix, block cipher as the structure and sensors values as noise source. Futhermore, we implement the lightweight RNG in Arduino that is one of the most popular lightweight devices and estimate the entropy values of sensors and random number to demonstrate the effectiveness and the security of our design.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.49-59
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• 2017

Smart sensing plays a key role in a variety of IoT applications, and its importance is growing more and more together with the development of artificial intelligence. Therefore the importance of smart sensors cannot be overemphasized. However, most studies related to smart sensors have been focusing on specific application purposes, for example, security, energy saving, monitoring, and there are not much effort on researches on how to efficiently configure various types of smart sensors to be needed in the future. In this paper, a component-based framework with hierarchical structure for efficient construction of smart sensor is proposed and its application to smart home is designed and implemented. The proposed method shows that various types of smart sensors to be appeared in the near future can be configured through the design and development of necessary components within the proposed software framework. In addition, since it has a layered architecture, the configuration of the smart sensor can be expanded by inserting the internal or external layers. In particular, it is possible to independently design the internal and external modules when designing an IoT application service through connection with the external device layer. A small-scale smart home system is designed and implemented using the proposed method, and a home cloud operating as an external layer, is further designed to accommodate and manage multiple smart homes. By developing and thus adding the components of each layer, it will be possible to efficiently extend the range of applications such as smart cars, smart buildings, smart factories an so on.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.226-231
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• 2020

In order to maximize the production of landscape plants in optimal condition while coexisting with the environment in terms of precision agriculture, quick and accurate information gathering of the internal environmental elements of the growing container is necessary. This may depend on the accuracy of the positioning of numerous sensors connected to landscape plants cultivating system (LPCS) in containers. Thus, this paper presents a method for estimating the location of the sensors related to cultivation environment connected to LPCS by measuring the received signal strength (RSS) or time of arrival TOA received between oneself and adjacent sensors. The Small sensors connected to the LPCS of container are known for their locations, but the remaining locations must be estimated. For this in the paper, Rao-Cramer limits and maximum likelihood estimators are derived from Gaussian models and lognormal models for TOA and RSS measurements, respectively. As a result, this study suggests that both RSS and TOA range measurements can produce estimates of the exact locations of the cultivation environment sensors within the wireless sensor network related to the LPCS.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.21-27
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• 2017

Internet of Things(IoT), which is developing for the hyper connection society, is based on OSHW (Open Source Hardware) such as Arduino and various small products are emerging. Because of the limitation of low performance and low memory, the IoT is causing serious information security problem that it is difficult to apply strong security technology. In this paper, we analyze the vulnerability that can occur as a result of compiling and loading the application program of Arduino on the host computer. And we propose a new attack method that allows an attacker to arbitrarily change the value input from the sensor of the arduino board. Such as a proposed attack method may cause the arduino board to misinterpret environmental information and render it inoperable. By understanding these attack techniques, it is possible to consider how to build a secure development environment and cope with these attacks.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.2
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• pp.97-105
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• 2016

Microcontrollers (MCUs) for endpoint smart sensor devices of internet-of-thing (IoT) are being implemented as system-on-chip (SoC) with on-chip instruction flash memory, in which user firmware is embedded. MCUs directly fetch binary code-based instructions through bit-line sense amplifier (S/A) integrated with on-chip flash memory. The S/A compares bit cell current with reference current to identify which data are programmed. The S/A in reading '0' (erased) cell data consumes a large sink current, which is greater than off-current for '1' (programmed) cell data. The main motivation of our approach is to reduce the number of accesses of erased cells by binary code level transformation. This paper proposes a built-in write/read path architecture using binary code inversion method based on hot-spot region detection of instruction code access to reduce sensing current in S/A. From the profiling result of instruction access patterns, hot-spot region of an original compiled binary code is conditionally inverted with the proposed bit-inversion techniques. The de-inversion hardware only consumes small logic current instead of analog sink current in S/A and it is integrated with the conventional S/A to restore original binary instructions. The proposed techniques are applied to the fully-custom designed MCU with ARM Cortex-M0$^{TM}$ using 0.18um Magnachip Flash-embedded CMOS process and the benefits in terms of power consumption reduction are evaluated for Dhrystone$^{TM}$ benchmark. The profiling environment of instruction code executions is implemented by extending commercial ARM KEIL$^{TM}$ MDK (MCU Development Kit) with our custom-designed access analyzer.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.935-941
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• 2020

People estimation is important to provide IoT services. Most people counting technologies use camera or sensor data. However, the conventional technologies have the disadvantages of invasion of privacy and the need to install extra infrastructure. This paper proposes a method for estimating the number of people using a Wi-Fi AP. We use channel state information of Wi-Fi and analyze that using deep learning technology. It can be achieved by pre-installed Wi-Fi infrastructure that reduce cost for people estimation and privacy infringement. The proposed algorithm uses a k-binding data for pre-processing process and a 1D-CNN learning model. Two APs were installed to analyze the estimation results of six people. The result of the accurate number estimation was 64.8%, but the result of classifying the number of people into classes showed a high result of 84.5%. This algorithm is expected to be applicable to estimate the density of people in a small space.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.4
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• pp.19-31
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• 2022

Purpose: This study was conducted to identify indoor air quality in various living spaces using sensors that can measure noise, vibration, fine dust, and odor in real time and to propose optimal indoor air quality maintenance management using Internet of Things(IoT). Research design, data and methodology: Using real-time sensors to monitor physical factors and environmental air pollutants that affect the comfort of the residential environment, Noise, Vibration, Atmospheric Pressure, Blue Light, Formaldehyde, Hydrogen Sulfide, Illumination, Temperature, Ozone, PM10, Aldehyde, Amine, LVOCs and TVOCs were measured. It were measured every 1 seconds from 4 offices and 4 stores on a small scale from November 2018 to January 2019. Results: The difference between illuminance and blue light for each measuring point was found to depend on lighting time, and the ratio of blue light in total illumination was 0.358 ~ 0.393. Formaldehyde and hydrogen sulphide were found to be higher than those that temporarily attract people in an indoor office space that is constantly active, requiring office air ventilation. The noise was found to be 50dB higher than the office WHO recommendation noise level of 35 ~ 40dB. The most important factors for indoor environmental quality were temperature> humidity> illumination> blue light in turn. Conclusions: Various factors that determine the comfort of indoor living space can be measured with real-time sensors. Further, it is judged that the use of IoT can help maintain indoor air quality comfortably.