The indoor location-based control system estimates the indoor position of a user to provide the service he/she requires. The major elements involved in the system are the localization server, service-provision client, user application positioning technology. The localization server controls access of terminal devices (e.g., Smart Phones and other wireless devices) to determine their locations within a specified space first and then the service-provision client initiates required services such as indoor navigation and monitoring/surveillance. The user application provides necessary data to let the server to localize the devices or allow the user to receive various services from the client. The major technological elements involved in this system are indoor space partition method, Bluetooth 4.0, RSSI (Received Signal Strength Indication) and trilateration. The system also employs the BLE communication technology when determining the position of the user in an indoor space. The position information obtained is then used to control a specific device(s). These technologies are fundamental in achieving a “Smart Living”. An indoor location-based control system that provides services by estimating user’s indoor locations has been implemented in this study (First scenario). The algorithm introduced in this study (Second scenario) is effective in extracting valid samples from the RSSI dataset but has it has some drawbacks as well. Although we used a range-average algorithm that measures the shortest distance, there are some limitations because the measurement results depend on the sample size and the sample efficiency depends on sampling speeds and environmental changes. However, the Bluetooth system can be implemented at a relatively low cost so that once the problem of precision is solved, it can be applied to various fields.

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With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

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Interest in green energy has increased worldwide. Therefore, smart grid projects to form a more efficient and eco-friendly intelligent grid by combining information technology (IT) technology with the existing grid are actively being conducted. In Korea, a national-level smart grid project road map has been confirmed, and an action plan has been prepared. Despite such actions, there may appear various threat scenarios in the application of the IT to the grid as a reverse function. Security technology is a measure to respond to such threats effectively. The security technology of a smart grid is an important factor that is directly related to the success or failure of the smart grid project. A smart grid is a new type of next-generation grid born of the fusion with IT. If the smart grid, the backbone of the power supply, is damaged by a cyberattack, it may cause huge damage, such as a nationwide power outage. In fact, there is an increasing cyberattack threat, and the cyber security threat to the smart grid is not insignificant. Furthermore, the legal system related to information protection is also important in order to support it systematically. In this paper, the necessity of the smart grid is examined, and the industry's initiative toward the smart grid security threat and threat response is examined. In this paper, we also suggest a security plan of applying Rainbowchain, the Blockchain technology, to the smart grid and energy exchange. We propose achieving superior performance and security functions by using Rainbowchain, which contains seven authentication techniques among existing Blockchain technologies, and propose the ecosystem and architecture necessary for its application. Figure 1. Diagram of smart grid.It applies a differentiated power rate system by power supply and demand condition in order to distribute power consumption. It also shows the usage and power rate in real time in order to induce consumers to save energy voluntarily. The power producer can also enhance the stability of the grid and reduce costs through remote automatic metering. As such, it increases operational efficiency and reduces the investment of the power plant due to the reduction of the maximum power demand. Consumers may charge power to the energy storage device during the time when the power rate is low. Conversely, they sell electricity at times when demand is high. As such, they become prosumers.In the first power layer, the distributed power source system connection and intelligence transmission system development take attention. The intelligent transmission network contains an intelligent power device, such as a smart meter to control the transmission network efficiently through the automatic restoration function by the grid operator, and it is expected that a distributed transmission network and distributed power to the customer system, a high-pressure direct current transmission system (HVDC3), a flexible transmission system (FACTS), and an auto recovery function will be introduced. In the second communication layer, ...

IoT devices are widely used in the smart home, automobile, and aerospace areas. Note, however, that recent information on thefts and hacking have given rise to many problems. The aim of this study is to overcome the security weaknesses of existing Internet of Things (IoT) devices using Blockchain technology, which is a recent issue. This technology is used in Machine-to-Machine (M2M) access payment—KYD (Know Your Device)—based on the reliability of existing IoT devices. Thus, this paper proposes a BoT (Blockchain of Things) ecosystem to overcome problems related to the hacking risk of IoT devices to be introduced, such as logistics management and history management. There are also many security vulnerabilities in the sensor multi-platform from the IoT point of view. In this paper, we propose a model that solves the security vulnerability in the sensor multi-platform by using blockchain technology on an empirical model. The color spectrum chain mentioned in this paper suggests a blockchain technique completed by using the multiple-agreement algorithm to enhance Thin-Plate Spline (TPS) performance and measure various security strengths. In conclusion, we propose a radix of the blockchain’s core algorithm to overcome the weaknesses of sensor devices such as automobile, airplane, and close-circuit television (CCTV) using blockchain technology. Because all IoT devices use wireless technology, they have a fundamental weakness over wired networks. Sensors are exposed to hacking and sensor multi-platforms are vulnerable to security by multiple channels. In addition, since IoT devices have a lot of security weaknesses we intend to show the authentication strength of security through the color spectrum chain and apply it to sensor and multi-platform using Blockchain in the future.

The obese population is increasing rapidly due to the change of lifestyle and diet habits. Obesity can cause various complications and is becoming a social disease. Nonetheless, many obese patients are unaware of the medical treatments that are right for them. Although a variety of online and offline obesity management services have been introduced, they are still not enough to attract the attention of users and are not much of help to solve the problem. Obesity healthcare and personalized health activities are the important factors. Since obesity is related to lifestyle habits, eating habits, and interests, I concluded that the big data analysis of these factors could deduce the problem. Therefore, I collected big data by applying the machine learning and crawling method to the unstructured citizen health data in Korea and the search data of Naver, which is a Korean portal company, and Google for keyword analysis for personalized health activities. It visualized the big data using text mining and word cloud. This study collected and analyzed the data concerning the interests related to obesity, change of interest on obesity, and treatment articles. The analysis showed a wide range of seasonal factors according to spring, summer, fall, and winter. It also visualized and completed the process of extracting the keywords appropriate for treatment of abdominal obesity and lower body obesity. The keyword big data analysis technique for personalized health activities proposed in this paper is based on individual's interests, level of interest, and body type. Also, the user interface (UI) that visualizes the big data compatible with Android and Apple iOS. The users can see the data on the app screen. Many graphs and pictures can be seen via menu, and the significant data values are visualized through machine learning. Therefore, I expect that the big data analysis using various keywords specific to a person will result in measures for personalized treatment and health activities.

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